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Application of artificial neural networks for testing long-term energy policy targets
Energy 174 (2019) 488e496
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Energy journal homepage: www.elsevier.com/locate/energy
Application of artificial neural networks for testing long-term energy policy targets Damir J. Ðozi c a, *, Branka D. Gvozdenac Urosevi cb a b
Department of Industrial Engineering and Engineering Management, Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia Department of Energy and Process Engineering, Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia
a r t i c l e i n f o
a b s t r a c t
Article history: Received 5 October 2018 Received in revised form 30 January 2019 Accepted 27 February 2019 Available online 28 February 2019
The paper analyses a model of the EU energy system by means of artificial neural networks. This model is based on the prediction of CO2 emissions until 2050 taking into account the current Energy Policy of the EU. The results show that artificial neural networks model this system very well and that this model has the ability to predict the behaviour of CO2 emissions. This will also enable timely response and correction of energy and economic strategy by changing the value of the relevant indicators in order to achieve the ambitious planned reductions of CO2 emissions by 2050. These plans are specified in the Energy Roadmap 2050 document of the European Commission from 2012 and promote economically costeffective scenarios that will adapt the European Union's economy to the needs of environmental protection and the reduction of energy consumption. Several structures of Artificial Neural Networks were analysed in order to select the best one for modelling large energy systems. It was determined that the model with the Cascade Forward Back Propagation structure with numerous specific indicators can model such energy systems and predict of CO2 emissions with acceptable accuracy. © 2019 Elsevier Ltd. All rights reserved.
Keywords: Energy policy Artificial neural networks European union GHG emissions
1. Introduction Energy efficiency and the use of renewable energy sources are key elements in restraining the growth of GHG emissions. However, in order to achieve optimum effects from their implementation, structural changes to national economies and society as a whole are necessary. The EU has identified these problems in time and has reacted by introducing numerous directives. It has also devised an energy policy that fully recognizes these structural changes and prescribes mechanisms for their implementation. 1.1. Energy Policies An energy policy is a way for an entity within a political unit (often a government) or within its part to present a plan and an intention to develop the energy sector, taking into account the production, transmission, distribution and consumption of energy. A national energy policy is needed to establish a framework within which entities (municipalities and industry) can and must establish
and implement their own energy policies in order to reach EU targets. The energy policy should provide conditions for economical energy production and for the reduction of energy losses, for efficient allocation of producers and for the choice of the optimum manner of transportation, as well as conditions for the reduction of the negative effects of energy transformations on the environment. Here, we primarily have in mind the reduction of gas emissions (carbon dioxide and other gases), which cause the greenhouse effect, and other harmful by-products of the energy transformation process (sulphur and nitrogen oxides, ash, soot, etc.). The European Union (EU281) strategic energy objectives are given in the Energy Roadmap 2050 document [1]. Clean technologies and energy efficiency are key elements of the future European economy. It is expected that by means of the implementation of these policies in the EU, CO22 emissions will be reduced by 80% and even up to 95% in relation to the reference level of 1990 and that
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European Union which include 28 member states. In the paper, the mention of the change of CO2 emissions will always imply the Carbon Dioxide Equivalency (CO2eq) as the quantity that describes for a given mixture and amount of greenhouse gas (GHG), the amount of CO2 that will have the same global warming potential (GWP), when measured over a specified timescale (generally, 100 years). 2
* Corresponding author. E-mail addresses: [email protected] (B.D. Gvozdenac Urosevi c).
(D.J.
https://doi.org/10.1016/j.energy.2019.02.191 0360-5442/© 2019 Elsevier Ltd. All rights reserved.
Ðozi c),
[email protected]
D.J. Ðozic, B.D. Gvozdenac Urosevic / Energy 174 (2019) 488e496
this will be achieved in an economical and cost-effective way. Starting targets are a 40% reduction by 2030 and a 60% reduction by 2040. The adoption of this comprehensive strategic document was preceded by the adoption of other documents and by the systematic analysis of the conditions necessary and sufficient for the effective and efficient implementation of strategies. First of all, this refers to institutional organization, building of capacities and creating economic preconditions. In this paper, current EU policies are described through 10 key indicators, which are given as inputs to an artificial neural network (ANN) model in order to predict CO2 emissions by 2050 [1]. Furthermore, it is possible to manage CO2 emissions by changing the values of indicators (i.e. changing the policies). This enables assessing and planning the reduction of CO2 emissions in the most efficient way through the correction of energy and economy policies and energy and economy strategies. The paper is primarily focused on testing the scope of current EU policies aimed at the reduction of CO2 emissions until 2050. 1.2. Artificial Neural Networks ANNs are modern artificial intelligence models that solve nonlinear functions, data sorting, pattern recognition, optimization, prediction, modelling, system identification, forecasting, management and simulation very well. They are being used with ever increasing frequency due to their ease of implementation, clear model and good performance. They give the best results for problems in which there is non-linearity and unpredictability and where there is no clear definition and direct connection between inputs and outputs [2]. They are based on learning from examples rather than on clearly defined rules as in other methods (fuzzy logic, linear regression, etc.). They use real data from the past and they modify and adapt the model itself (during the training period) so that they give exact outputs that correspond to the actual data. This way of defining a system provides for generalization and enables the drawing of many conclusions about the system that is being studied, although all information about it is unavailable. It increases robustness and lowers sensitivity to changes of input data [3,4], which is ANN's biggest advantage over mathematical models. In this paper, the ANN model presented recognizes the significance of certain indicators and their influence on CO2 emissions prediction and the simulation results can be used as a suggestion for the modification of existing policies and for the creation of new ones in order to achieve the desired objectives. 1.3. Energy structure The Total Primary Energy Supply (TPES) affects the change in CO2 emissions. However, with a change of the fuel structure that is used and an increase in renewable energy sources (RES), emissions can be significantly reduced despite the growth of TPES. Fig. 1 shows TPES in the EU28 for the period 1990e2015 through the share of individual fuels. The main pillar of the EU Energy Policy is primarily a significant increase of the share of RES, a reduction of the share of solid and liquid fossil fuels, a small increase of the share of natural gas as the Earth's cleanest fossil fuel with far less emissions than other fossil fuels. This strategic document does not anticipate the cessation of the use of nuclear energy. In addition to RES, the key element of any energy policy is energy efficiency. Political commitment to high energy savings include, for example, stricter minimum requirements for appliances and new buildings, a high degree of adaption of existing buildings, the establishment of an obligation to save plant energy, etc. This will lead to a significant reduction of the TPES by 2050.
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Fig. 1. The share of certain fuels in the TPES for the EU28 from 1990 to 2015.
1.4. Previous research Artificial intelligence, with the emphasis on ANN, due to its structure and universality, has become very popular in various realworld applications. Numerous studies have shown good results and, therefore, it has become the method that is often used in predictions. It is of crucial importance to have information and to evaluate current policy in order to improve it. Studies so far have specified certain indicators that significantly affect the energy sector (such as population, gross domestic product (GDP), structure of the economy, global average air temperature, etc.) In study [5], the ANN is used for energy and environmental planning in the transportation sector. On the basis of two indicators, population and GDP, fuel consumption in the future is predicted and thus, the direction in which it is necessary to adjust the energy policy in order to decrease fuel consumption. In study [6], forecasting of energy demand, wind speed generation and CO2 emissions, which are all vital for the energy sector for the purpose of planning, is carried out. In paper [7], various scenarios in the transportation sector are analysed in order to reduce CO2 emissions and primary energy consumption. In paper [8], the algorithm used in the ANN for the prediction of electricity consumption in Iran is presented. Paper [9] presents the use of the ANN in predicting of energy dependency in Turkey. This is important because information is provided that is related to the dependence of the economy on the import of energy in order to meet demand in the country. The ANN in paper [10] is used for modelling and optimizing energy efficiency in distillation columns. The ANN in paper [11] is used for the prediction of electricity consumption in residential buildings. In Japan [12], peak electric loads are predicted by means of the ANN. In paper [13], electricity tariffs are predicted in order to improve energy management. Depending on the tariff and demand in the forthcoming 3-h period, heating system management has been analysed. Financial security evaluation of the electric power industry in China is evaluated in paper [14] by means of an upgraded ANN and by comparison with the Support Vector Machine (SVM) model and the classical gray forecasting model. This upgraded ANN model has given more precise results, rapid convergence and the probability of entering into local optimum has been reduced. Ermis et al. [15] have analysed the use of green energy in the world by means of ANNs. Also, he emphasizes the important role of renewable energy sources in the country's future policy in order to stabilize the climate. On the other hand, data about solar radiation and the amount of wind also play an important role in the economic and energy strategy of countries because they show the cost effectiveness of the use of solar panels and wind turbines in certain areas. In paper [16], the ANN is used to predict solar irradiation in relation to astronomic and meteorological and climate conditions.
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In ref. [17], the ANN is used to estimate solar irradiation. The ANN in paper [18] is used to determine the wind speed in a certain region more precisely. This enables better definition of wind potentials and operations of wind turbines. Jayaraj et al. [19] have used MultiLayer Perceptron (MLP) and the Elman's ANN for the prediction of wind up to 48 h. Barbounis et al. [20] have used recurrent neural network for long term wind speed prediction (up to 72 h). In paper [21], wind speed has been predicted for different target sites in South Korea using an ANN. The above-mentioned examples show that there are many applications of ANNs for the prediction of indicators in the energy sector. However, none of these studies analyse such a complex energy system as the system of the European Union. The majority of papers use Feed Forward Back Propagation (FFBP) artificial neural networks because they have extensive applications and can resolve the majority of problems. In paper [22], in which the prediction of solar radiation is made for each hour, the four most frequently used networks are compared, FFBP (which is essentially a MLP), Cascade Forward Back Propagation (CFBP), Generalized Regression Neural Network (GRNN) and Elman Back Propagation (EBP). Three error parameters are used for the evaluation. It is concluded that the GRNN shows the best results. Also, the paper has shown that on a concrete problem, the same parameters and the same network in one region can give very good results while in another region, bad results are obtained. Therefore, it is very important to adapt the network to a specific problem. In paper [23], a CFBP network is used to estimate the concentration of harmful gases in urban areas by means of data from surrounding meteorological stations. The CFBP has proven to be a very powerful tool for solving this problem. In ref. [24], the FFBP ANN is used to predict energy consumption in cross laminated timber (CLT) buildings on the basis of eleven characteristic parameters. Majeed Safa et al. [25]. attempt to predict CO2 emissions during wheat production by using two models: 1) MLP and 2) Multiple Linear Regression model (MLR). The results show that the model with the ANN has provided much better results than the MLR model, which confirms that in this case, ANNs can make a valuable contribution. Paper [26] compares the CFBP and the FFBP ANN for the prediction of the behaviour of concrete in the first 28 days after casting. The results show that the CFBP network performs better, especially from the point of less dissipation of simulated values. Although the scope of application of the ANN in paper [26] is not similar to our research, this paper provides yet another in a series of confirmations that ANNs, and especially the CFBP, can be applied very well in predictions and that it is worthwhile examining them in other problems, as well. Although the linear regression model has also often been used for predictions, that model has not been analysed in this paper since it does not provide satisfactory results in resolving nonlinear problems (the complete energy system of the European Union is by its nature nonlinear). Based on the literature it has been assumed that the CFBP model can give better results (less errors and better prediction) in this research. The possession of information about the current parameters of the system, as well as those in the near future enables better management of the system, reaching the targets easier and faster and the reduction of possible occurrence of unwanted and irreversible consequences. Therefore, the analyses of energy parameters represent very important information for state governments for the analysis of their current policies and opens opportunities for the adjustment of the energy policy and strategies in order to achieve the desired objectives. In paper [5], population and GDP have been specified as essential parameters for the assessment of fuel consumption. Paper [27] has analysed the influence of population on CO2 emissions in
the European Union. Ekonomou et al. [28] have used air temperature, installed power capacity, electricity consumption and GDP as parameters for the prediction of long-term final energy consumption in Greece. In ref. [9], energy dependency in Turkey has been analysed using parameters such as total production of primary energy per capita, net imports of gas and primary energy, total gross electricity generation and final energy consumption per capita. In ref. [8], sources of primary energy and their share in the production and consumption of total primary energy are discussed. This paper gives predictions of monthly electricity consumption on the basis of electricity consumption in the previous two months as well as in the same month of the previous year. In paper [29], energy consumption in Turkey is estimated based on data about population, GDP, export and import by means of an ANN. In paper [30], electricity consumption in New Zealand is estimated using parameters such as population, GDP and electricity price. In paper [31], CO2 emissions, energy consumption and economic growth in China are predicted in order to achieve a clean energy economy and halt climate change. Based on the literature it is hypothesized that the following ten parameters are most important for forecasting CO2 emissions in the European Union: GDPppp,3 average annual air temperature, TPES, electricity consumption, population and share of renewable, nuclear, natural gas, total petroleum products and solid fuels energy in the TPES. These parameters can depict current EU policy, as well as the energy strategy in the future. Therefore, these ten parameters represent the quantified EU policy through which the energy system of the EU will be managed in order to analyse CO2 emissions. CO2 emissions greatly affect climate changes and it is necessary firstly to restrict and then to reduce them. Since the energy sector is the largest producer of CO2 emissions, it is necessary to change policies to achieve the desired results. So far, studies have been dealing with predictions of parts of the energy systems and with certain parameters. However, there is not even one paper, to our knowledge, that deals with such a complete and complex energy system as the EU energy system. Various methods have been used for the estimation and forecasting of isolated parts of certain systems of which some are linear and some are nonlinear. Since the EU energy system is very complex and nonlinear by its nature, the ANN model is used in this research. Although the use of the basic model of artificial neural networks (FFBP) model is most often found in the literature, it can also be seen that the CFBP model is shown to give good results. Taking into account the complexity of the current problem, as well as the fact that there is a relatively small set of data, we hypothesized that the CFBP will give better results for this problem in comparison to the MLP (FFBP) network, which pilot tests have proven. So far, research has been dealing with the prediction of the above-mentioned parameters on the basis of the determined relevant energy indicators. This paper includes a combination of ten indicators that have been recognized in the literature as the most important and the most relevant for the concrete problem. As one more novelty, in addition to the estimation of CO2 emissions taking into account the current policy, this model will enable the simulation of various future energy policy scenarios by changing the behaviour of the indicators of CO2 emissions. Therefore, the hypothesis of this research is that an ANN model with CFBP structure and with the use of ten specific indicators can model the complete EU energy system and predict CO2 emissions until 2050 with acceptable accuracy.
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GDP at Purchasing Power Parity, measured for US Dollar in 2010.
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2. Material and methods 2.1. Neural network structure In this research, we use a modified MLP model of ANN, CFBP which, according to its structure, corresponds to the nature of the problem of predicting the behaviour of a very complex energy system. In the CFBP architecture, each input is connected to each layer of the network, which means that the network has much more opportunities for more accurate correction of errors during the back-propagation process, as well as that inputs themselves correct the weight of each layer. This enables the network to learn complex problems and dependencies among inputs more easily and therefore, the probability of the accuracy of the prediction is increased. We created pilot tests which include modelling and simulation of two ANN structures, CFBP and MLP, with two hidden layers and a different number of neurons in each layer (range from 2 to 20). We have hypothesized that the results of the prediction during the research on this particular problem would be better by using the CFBP due to the above-mentioned reasons. Tests have shown that CFBP networks (Fig. 2) perform better than MLP networks and so the CFBP structure was used in further research. Energy policies are especially important and very complex. It is of the utmost importance to place all variables and relevant sizes in the functional dependency in order to obtain good predictions of CO2 emissions at the output and to establish control over these emissions by the mechanisms envisaged in the energy policy. The CFBP has proven a very powerful network for solving this complex problem because of its great possibilities for generalization and excellent matching of systems. 2.2. Neural network learning mechanism Cascade Forward networks learn to associate specific output to specific input by adjusting its weights based on the output error. The most popular algorithm for weight correction is back propagation, which propagates the error backwards, from the output through all hidden layers [32]. For the output network layer, the error between the computed output and the actual output is calculated. Then, weights are recalculated in order to minimize the error using the steepest descent algorithm. This becomes the desired output value for preceding hidden layer. The process is repeated (propagating error back) for each hidden layer.
Fig. 3. Sigmoid activation function.
allow the network to find and to calculate new coefficients that can describe the state of the system, and then to use their combination in order to calculate the output. Due to the complexity of the energy system and since there is a relatively small set of data (data for only 26 years), it was decided that the number of hidden layers should be kept to a minimum. However, in order for the network to use the potential of multi-layer network (above-mentioned), it is necessary to have more than one hidden layer. As a compromise, two hidden layers were formed. There is a linear and a sigmoidal activation function. The linear activation function transforms inputs into the neuron in a linear way. Thus, at the end, the output from the network is linearly dependent on the input by which the significance of multiple layers is lost, as well as the ability to solve non-linear problems. On the other hand, the sigmoidal function (Fig. 3) enables the creation of non-linear connections between neurons and layers, leading to nonlinear dependency between input and output. There are two types of sigmoidal functions: a logsig and a tansig. Although the nature of the function is the same, we opted for the tansig function because it has a larger output range (1 to 1), which means that the output from the neuron is more precise and that the output is more sensitive to changes at the input. Levenberg-Marquardt is the most often used function for training purposes and it has shown good results. Therefore, we have not changed it. 2.4. Neural network input and output parameters
2.3. Neural network architecture There are several parameters that can be changed in order to adjust the network architecture to the problem. The most important parameters are the number of hidden layers, the number of neurons in each hidden layer, the activation function and the training function. Multiple network layers represent input re-dimensioning and
In order to describe and quantify EU energy policies in order to predict CO2 emissions, the following ten indicators were chosen: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
GDPppp Average annual temperature TPES Electricity consumption Population Share of renewable energy sources in TPES Share of nuclear energy in TPES Share of natural gas energy in TPES Share of total petroleum product energy in TPES Share of solid fuel energy in TPES
The relevant data was taken from the IEA4 database for the period 1990e2015. Fig. 4 shows the first five parameters and their Fig. 2. Cascade forward back propagation (CFBP) Networks architecture used in this research.
4
International Energy Agency (IEA), http://www.iea.org.
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Fig. 4. Normalized values of five input parameters.
trend until 2050. The trend of each indicator is calculated by data fitting [33,34] in the period 1990e2015 by a first-degree polynomial and based on that, linear dependency between indicators and time is obtained in the form of (1) where x is time, y is indicator value and a and b are calculated parameters. After that, a new time axis, 1990e2050, is applied to the function, which results in the assessment of the value of each indicator for the whole from 1990 to 2050.
y ¼ ax þ b
(1)
Why is the period from 1990 to 2015 used? The latest data published by the IEA at the time this paper was drafted was until 2015. The year 1990 is chosen because most analyses take data from this year for the purpose of comparing effects of actions or programs undertaken in order to increase energy efficiency and to reduce CO2 emissions. For example, one of the main objectives of the Europe 2020 Strategy is to achieve 20% reduction of gas emissions causing the greenhouse effect in relation to 1990. In the 1990s, the EU was already a well-established political platform for actions aimed at the reduction of CO2 emissions and the increase of energy efficiency. At the global level, this is marked by Agenda 21 (21 designates the 21st century), that is, the plan of actions in relation to sustainable development which was adopted at the Conference on Environment and Development in Rio de Janeiro in 1992. It has been considered as the upper most achievement of this Conference, which also included the protection of the atmosphere. It can be said that the commitment and organized actions, that will finally enable not only the reduction of CO2 emissions but also controlled and optimum primary energy consumption, started in the 1990s. Nevertheless, it should be noted that only in 2005 was there a noticeable fall in the TPES and other indicators, that is, changes in the desired direction. This shows the slowness of changes in large systems such as the energy system, as the biggest pollutant of the atmosphere. Because of this slowness, the database from a wider period of time was chosen. In the case of neural networks, best practice is to prepare input
data in such a way that the neural network learns more quickly, becomes more efficient in terms of memory usage, and finally, generates better results for simulations. The logic itself within the neural network operates with data in a range of 1 to 1 or 0 to 1 due to the activation functions that are used in hidden layers (transfer functions) [35]. The nature of the parameters that are used in this paper indicates the use of the interval [0, 1]. It is not realistic, for example, that the TPES or the population is negative. Normalization can be a simple scaling of the range or it can be the calculation of the difference between (the previous and the next) values of the specified parameter. In any case, the goal is the same no matter which method is applied. Bringing parameters that are within the range from 0 to 1 enables the ANN to recognize the dependence and mutual influence of parameters more easily and thus make a better assessment of the output, which is also within the same range. After the simulation, it is possible to do post processing in which the obtained data is scaled backward in the actual range of values, enabling better readability and interpretation. Following the previously mentioned trends and best practices, the normalization of all parameters in this research is done to the range 0e1. A great challenge was to find and to define the values of each parameter that would be taken as minimum and maximum values in order to perform normalization. If we regard the ANN as an object to which we have to transfer knowledge, its result in learning and further reproduction of knowledge will directly depend on the way in which we have transferred knowledge, as well as on the availability of curriculum content. Guided by this logic, we recognized normalization as a very serious challenge that should be treated wisely and resolved with maximum care. For each parameter, minimum and maximum values were carefully chosen so that the influence of the parameters is logical. The temperature changes very slowly, and it would not be good to normalize it in such a way that it changes completely from 0 to 1 as this gives it too much importance and the results would be incorrect. Therefore, the minimum and maximum temperature values were the minimum and maximum temperatures recorded in the EU28 in the period 1990e2015. For other parameters, the minimum value of zero and the maximum one were taken so that they reflect possible and realistic maximums within the interval from 1990 to 2050. Table 1 shows the minimum and maximum values that have been used for the normalization of each parameter. The formula for normalization is:
Normalized Indicator ¼
x Xmin Xmax Xmin
(2)
2.5. Neural network e modelling and simulation In the research, the program package MATLAB 2014a with the Neural Network toolbox was used for modelling the system and simulation, as well as for data processing. The network consists of two hidden layers and one output layer (Fig. 2). In the first hidden layer, there are 4 neurons while in the other one, there are 10. The
Table 1 Minimum and maximum indicator values for normalization.
GDP Average Temperature Population TPES Electricity Consumption
Minimum Value
Maximum Value
Unit
Billion USD (2010) o C Million Mtoe TWh
0 16.41 0 0 0
30000 29.98 600 2000 10000
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Levenberg-Marquardt function is used for training. The activation function in the hidden layers is the tangent sigmoid ‘tansig’ function while the linear activation function ‘purelin’ is placed in the output layer. The pre-processing of data includes: 1) Finding the trend of each indicator using MATLAB functions ‘polyfit’ (for finding the first-degree polynomial) and ‘polyval’ for calculating new values in the given period, 2) Data normalizing by means of the above-mentioned algorithm.
2.5.1. Training process Original data was used for the network training (the period 1990 to 2015) while both original data and calculated indicator trends were used for simulations (the period 1990 to 2050). Data is randomly divided into two groups: 1) 80% of data for training and 2) 20% of data for network testing. The training of the neural network depends on several factors that directly affect the outcome of the prediction: a. Choice of a good data set for learning, b. Choice of a sequence for data presentation from the training set, c. Initial weights of each neuron. If the data was badly selected, the ANN would not recognize essential characteristics of the input parameters. Bearing in mind the fact that the ANN randomly chooses neuron weights at the start that are corrected during training, and that the sequence in which input data is entered in the network during training directly affects the correction of weights during the error backward propagation, it is clear that both the above-mentioned factors directly affect the final values of weights, that is, the ANN behaviour. These problems can lead the network to reach local optimum, which causes the network to lose its power of generalization. For this reason, it is very important to train a large number of networks (with different input sets) in order to avoid reaching a local optimum due to badly selected (non-characteristic) inputs. 2.5.2. Simulation and validation process and error analysis In this research, 100 artificial neural networks were modelled and simulated. Out of them, only those networks that fulfil the two criteria specified below were retained: a. The root mean square error (RMSE) for tested data is less than 0.02 (2%). b. The prediction of CO2 emissions in 2050 (simulated data) is between 50% and 70%. The first requirement refers to the accuracy of training (in the period 1990e2015) and it is calculated with the formula
RMSE ¼
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u 2 uPT u y t yt t t¼1 b T
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less than 2%. The European Union has anticipated that if it continues with the current policy until 2050, CO2 emissions will be reduced from 100% (1990) to some 62%. This information was obtained on the basis of data on the current share of different sectors in the consumption of energy, and the share of specified energy resources in the production of energy and the emission coefficients of certain fuels (RES ¼ 0; Nuclear ¼ 0, Natural Gas ¼ 0.1836, Total Petroleum Products ¼ 0.2517, Solid Fuels ¼ 0.3325 kg/CO2/kWh). Bearing in mind the complexity of the system and the fact that there is no method that is able to predict the future accurately, it is necessary to take this result with a certain reserve. On the basis of that, another criterion for selecting the network was formed and that is the result of network simulation using the current policy should be ±10% of the predicted value for the European Union in 2050. Taking a lower deviation (for example, ± 5%) would increase the importance of the EU results, i.e. it would mean that these results and the method used are more trusted. On the other hand, if the range is increased (to, for example, ± 20%), this would increase the significance of this model and its results, but the possibility of error would also be increased (due to the inaccuracy of ANNs). Therefore, the range of ±10% of the EU predicted value of CO2 emissions in 2050 if the current policy is continued (i.e. 50e70%), becomes logical and acceptable. 3. Results Out of 100 networks that were modelled and simulated, 78 satisfy the first criterion (the root mean square error less than 2%) and 30 of them fulfil both of the above mentioned criteria. Fig. 5 shows the measured (input) data and the result of the simulation of one of the 30 networks. The simulated network completely follows the measured values indicating that the network has managed to fully imitate the analysed energy system. For all 30 qualified results, a root mean square error after the simulation was calculated for the period from 1990 to 2015. In this case, the root mean square error was calculated for the measured normalized values of CO2 emissions and for the values obtained by the ANN simulation in which: 1) trends for the first five inputs and indicators, and 2) measured data for the five other inputs that refer to the share of the certain type of energy in the TPES were used. Fig. 5 shows the simulation curve of the network that had the least root mean square error. Out of all curves, this curve best describes
(3)
where b y t represents simulated value and yt original value, while the second condition is directed to network performances until 2050, i.e. simulated data. The first criterion is significant because it allows a small error (which is necessary to exist in order to be sure that there is no network overfitting, i.e., learning to work only with data from a training session). On the other hand, the error should not be too big either in order to avoid underfitting, i.e., the network cannot generalize at all (it fails to find significant dependencies between inputs and outputs). In this research, we have assumed that the network is sufficiently capable of modelling the system if RMSE is
Fig. 5. Measured and best simulated data of CO2 emissions for the training period (1990e2015).
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the analysed period (1990e2015), that is, the ANN has been trained the best. Therefore, it was assumed that it will also best reflect changes to CO2 emissions as a consequence of current energy policies in periods outside the specified one. In other words, if these energy policies are to be applied in the period after the analysed one (the period after training, 2015e2050), the model estimates that in 2050, CO2 emissions will be 67.72% in relation to those measured in 1990 (Fig. 6). 4. Discussion The research has confirmed the results from pilot tests that the CFBP ANN can be used for efficient modelling of very complex energy systems. Taking into account the fact that 78 out of 100 networks produced results with an error (RMSE) of less than 2%, it can be concluded that the CFBP ANN model is properly chosen and that it is capable of learning such a complex system efficiently.
The calculation and the use of parameter trends (the continuation of the former policy) in the system modelled in the above way have forecast CO2 emissions in the atmosphere in relation to the year 1990. It is necessary to point out that linear regression is used to estimate these indicators until 2050 and that these indicators in the period from 2016 to 2050 are a straight line. Therefore, the logical ANN result for that period, is that CO2 emissions are also a straight line (Fig. 6). The EU Energy Roadmap 2050 anticipates that in 2050, with the continuation of current energy policies in all EU countries, CO2 emission will fall to 62% of those measured in 1990. Taking into account the complexity of the problem and the long-term nature of the predictions, the result obtained in this paper (67.72%) is satisfactory. This assessment can also be made by means of linear regression, that is, by the extrapolation of the CO2 trend for the period 1990e2015, however, since the system is nonlinear by its nature, linear regression cannot provide realistic predictions for the future.
4.1. Inputs for neural network 4.3. Problems and limitations of presented approach In the literature [27,29e31], various indicators are used (global temperature assessment, air humidity, primary energy production and consumption, energy capacity, electricity consumption, population, etc.) to estimate energy parameters (fuel consumption, electricity consumption, total primary energy consumption, solar radiation, emission of CO2 and other gases, etc.) in order to amend and improve policies, achieve an increase in energy efficiency and to reduce the influence of mankind on the environment. However, the combination of parameters used in this paper for the prediction of CO2 emissions, was not appeared in the literature so far. Therefore, in addition to the model itself, it should also be emphasized that the 10 selected indicators represent current energy policies accurately and that it is possible to establish the dependence of the effects of these policies on CO2 emissions and to establish efficient control for the implementation of these policies. 4.2. The results To identify 30 out of 100 networks is a very good result. It is necessary to bear in mind the fact that there are several ways to bring the network into a local optimum during training. Therefore, it is good practice to take as many neural networks as possible in order to increase the chance of finding a model that will credibly correspond to the problem. The fact that as many as 30 networks have been singled out speaks in favor of the ability of this type of ANN to solve a concrete problem, as well as of the fact that parameters have been very well selected and pre-processed.
Fig. 6. Measured data (1990e2015) along with the ANN output result e estimated CO2 emissions by 2050.
As with the all prediction methods, the ANN also has its own problems and limitations. As opposed to axiomatic methods such as linear regression, the way in which conclusions are made relevant to input and output dependence in ANNs is not exact and it is not precisely defined. ANNs are based on setting weights (priorities) for each node, which represent the probability of its activation [36], giving it the power to generalize on one hand and on the other hand, reducing the accuracy of results. For ANNs it is necessary to have a sufficient quantity of representative data based on which it will be formed (trained). ANNs are of limited use in cases when there is not enough information. A small training data set prevents the network from learning all dependencies between inputs and outputs and therefore, to have the power of generalization. Although an increase in the quantity of data increases the probability of better training of the system, it also increases the probability of the problems that occur in large data systems [37]. Furthermore, a badly chosen network structure can lead to overfitting or underfitting. Overfitting (overtraining) occurs when the network has learned the set well by training but in this way, it has lost the power of generalization and prediction (the error when presenting new data is very large). In other words, the model describes current data so well (it provides very small error) that it cannot predict any new patterns. This can happen if the network structure is too complex for the given problem. On the other hand, underfitting (undertraining) occurs when there is insufficient data or when the network structure does not correspond to the problem. Finding a good network structure is a great challenge and it always concerns the way in which to make a good trade-off between overfitting and model complexity. The sequence of presenting input-output pairs during learning is very important to artificial neural networks so that weights can be better corrected. The back-propagation algorithm directly relies on the inputs that are presented to the network, as well as on the initial values of weights which are randomly selected at the beginning of the training process. Therefore, it is necessary to model as many networks as possible with different starting values in order to reduce the probability of network entrance into local optimum and for the purpose of creating a network that provides the best results. It is also very important to be aware that ANNs learn from existing data and that therefore, they use acquired knowledge for decision making and therefore, cannot be expected to predict scenarios that they have not seen before. Bearing in mind the existence of all the above-mentioned
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problems, the only thing that can be done is to minimize their probability and to take the results with a certain dose of reserve. In this research, a sufficiently large set of data was used so that the network can learn essential characteristics of the system, but not so large that it creates new problems. The carefully selected set of ten representative indicators (inputs) and their preparation (normalization) have, therefore, generated logical and very promising results. Also, it is important to emphasize that this model (which incorporates the impact of each indicator on the output) has the potential to predict new CO2 values by giving it modified values of indicators in the future period. In other words, it is possible to predict the impact of a specific (new) policy and strategy of the EU (by changing the values of indicators) and then, in line with the results, to adjust the policy in the real system in order to achieve the desired impact. 5. Conclusion A method for modelling the highly complex energy system of the European Union is presented. The main goal is to determine CO2 emissions until 2050 under the conditions of current EU energy policies using the CFBP ANN model. The IEA database for the period 1990e2015 was used. Ten key indicators were selected for training and simulation. One hundred CFBP networks were modelled, out of which 30 networks were singled out by means of two criteria. One network out of 30 with the smallest root mean square error in comparison to measured data from 1990 to 2015 was used for the final result. The results indicate that the CFBP network can learn the behaviour of such a system with acceptable accuracy (RMSE < 2%) and that CO2 emissions will drop to 67.72% in relation to 1990 emissions if the current EU28 policy is continued. This gives the model the potential for the assessment and analysis of CO2 emissions in the future through modification of indicators (simulating different energy policy and strategy scenarios), which can be a very important factor for reaching the EU goal for CO2 emissions in an easier, quicker and more economical way. Acronyms ANN BP CFBP CLT CO2 EBP EU28 FFBP GA GDP GDPppp GHG GRNN GWP IEA MLP MLR NG RES RMSE SF SVM
Artificial Neural Network Back Propagation Cascade Forward Back Propagation Cross Laminated Timber Carbon Dioxide Elman Back Propagation European Union (Including 28 Countries) Feed Forward Back Propagation Genetic Algorithm Gross Domestic Product Gross Domestic Product based on purchasing power parity Green House Gasses Generalized Regression Neural Network Global Warming Potential International Energy Agency Multi-Layer Perceptron Multiple Linear Regression Natural Gas Renewable Energy Source Root Mean Square Error Solid Fuels Support Vector Machine
TPES TPP
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Total Primary Energy Supply Total Petroleum Products
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Application of artificial neural networks for testing long-term energy policy targets Damir J. Ðozi c a, *, Branka D. Gvozdenac Urosevi cb a b
Department of Industrial Engineering and Engineering Management, Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia Department of Energy and Process Engineering, Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia
a r t i c l e i n f o
a b s t r a c t
Article history: Received 5 October 2018 Received in revised form 30 January 2019 Accepted 27 February 2019 Available online 28 February 2019
The paper analyses a model of the EU energy system by means of artificial neural networks. This model is based on the prediction of CO2 emissions until 2050 taking into account the current Energy Policy of the EU. The results show that artificial neural networks model this system very well and that this model has the ability to predict the behaviour of CO2 emissions. This will also enable timely response and correction of energy and economic strategy by changing the value of the relevant indicators in order to achieve the ambitious planned reductions of CO2 emissions by 2050. These plans are specified in the Energy Roadmap 2050 document of the European Commission from 2012 and promote economically costeffective scenarios that will adapt the European Union's economy to the needs of environmental protection and the reduction of energy consumption. Several structures of Artificial Neural Networks were analysed in order to select the best one for modelling large energy systems. It was determined that the model with the Cascade Forward Back Propagation structure with numerous specific indicators can model such energy systems and predict of CO2 emissions with acceptable accuracy. © 2019 Elsevier Ltd. All rights reserved.
Keywords: Energy policy Artificial neural networks European union GHG emissions
1. Introduction Energy efficiency and the use of renewable energy sources are key elements in restraining the growth of GHG emissions. However, in order to achieve optimum effects from their implementation, structural changes to national economies and society as a whole are necessary. The EU has identified these problems in time and has reacted by introducing numerous directives. It has also devised an energy policy that fully recognizes these structural changes and prescribes mechanisms for their implementation. 1.1. Energy Policies An energy policy is a way for an entity within a political unit (often a government) or within its part to present a plan and an intention to develop the energy sector, taking into account the production, transmission, distribution and consumption of energy. A national energy policy is needed to establish a framework within which entities (municipalities and industry) can and must establish
and implement their own energy policies in order to reach EU targets. The energy policy should provide conditions for economical energy production and for the reduction of energy losses, for efficient allocation of producers and for the choice of the optimum manner of transportation, as well as conditions for the reduction of the negative effects of energy transformations on the environment. Here, we primarily have in mind the reduction of gas emissions (carbon dioxide and other gases), which cause the greenhouse effect, and other harmful by-products of the energy transformation process (sulphur and nitrogen oxides, ash, soot, etc.). The European Union (EU281) strategic energy objectives are given in the Energy Roadmap 2050 document [1]. Clean technologies and energy efficiency are key elements of the future European economy. It is expected that by means of the implementation of these policies in the EU, CO22 emissions will be reduced by 80% and even up to 95% in relation to the reference level of 1990 and that
1
European Union which include 28 member states. In the paper, the mention of the change of CO2 emissions will always imply the Carbon Dioxide Equivalency (CO2eq) as the quantity that describes for a given mixture and amount of greenhouse gas (GHG), the amount of CO2 that will have the same global warming potential (GWP), when measured over a specified timescale (generally, 100 years). 2
* Corresponding author. E-mail addresses: [email protected] (B.D. Gvozdenac Urosevi c).
(D.J.
https://doi.org/10.1016/j.energy.2019.02.191 0360-5442/© 2019 Elsevier Ltd. All rights reserved.
Ðozi c),
[email protected]
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this will be achieved in an economical and cost-effective way. Starting targets are a 40% reduction by 2030 and a 60% reduction by 2040. The adoption of this comprehensive strategic document was preceded by the adoption of other documents and by the systematic analysis of the conditions necessary and sufficient for the effective and efficient implementation of strategies. First of all, this refers to institutional organization, building of capacities and creating economic preconditions. In this paper, current EU policies are described through 10 key indicators, which are given as inputs to an artificial neural network (ANN) model in order to predict CO2 emissions by 2050 [1]. Furthermore, it is possible to manage CO2 emissions by changing the values of indicators (i.e. changing the policies). This enables assessing and planning the reduction of CO2 emissions in the most efficient way through the correction of energy and economy policies and energy and economy strategies. The paper is primarily focused on testing the scope of current EU policies aimed at the reduction of CO2 emissions until 2050. 1.2. Artificial Neural Networks ANNs are modern artificial intelligence models that solve nonlinear functions, data sorting, pattern recognition, optimization, prediction, modelling, system identification, forecasting, management and simulation very well. They are being used with ever increasing frequency due to their ease of implementation, clear model and good performance. They give the best results for problems in which there is non-linearity and unpredictability and where there is no clear definition and direct connection between inputs and outputs [2]. They are based on learning from examples rather than on clearly defined rules as in other methods (fuzzy logic, linear regression, etc.). They use real data from the past and they modify and adapt the model itself (during the training period) so that they give exact outputs that correspond to the actual data. This way of defining a system provides for generalization and enables the drawing of many conclusions about the system that is being studied, although all information about it is unavailable. It increases robustness and lowers sensitivity to changes of input data [3,4], which is ANN's biggest advantage over mathematical models. In this paper, the ANN model presented recognizes the significance of certain indicators and their influence on CO2 emissions prediction and the simulation results can be used as a suggestion for the modification of existing policies and for the creation of new ones in order to achieve the desired objectives. 1.3. Energy structure The Total Primary Energy Supply (TPES) affects the change in CO2 emissions. However, with a change of the fuel structure that is used and an increase in renewable energy sources (RES), emissions can be significantly reduced despite the growth of TPES. Fig. 1 shows TPES in the EU28 for the period 1990e2015 through the share of individual fuels. The main pillar of the EU Energy Policy is primarily a significant increase of the share of RES, a reduction of the share of solid and liquid fossil fuels, a small increase of the share of natural gas as the Earth's cleanest fossil fuel with far less emissions than other fossil fuels. This strategic document does not anticipate the cessation of the use of nuclear energy. In addition to RES, the key element of any energy policy is energy efficiency. Political commitment to high energy savings include, for example, stricter minimum requirements for appliances and new buildings, a high degree of adaption of existing buildings, the establishment of an obligation to save plant energy, etc. This will lead to a significant reduction of the TPES by 2050.
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Fig. 1. The share of certain fuels in the TPES for the EU28 from 1990 to 2015.
1.4. Previous research Artificial intelligence, with the emphasis on ANN, due to its structure and universality, has become very popular in various realworld applications. Numerous studies have shown good results and, therefore, it has become the method that is often used in predictions. It is of crucial importance to have information and to evaluate current policy in order to improve it. Studies so far have specified certain indicators that significantly affect the energy sector (such as population, gross domestic product (GDP), structure of the economy, global average air temperature, etc.) In study [5], the ANN is used for energy and environmental planning in the transportation sector. On the basis of two indicators, population and GDP, fuel consumption in the future is predicted and thus, the direction in which it is necessary to adjust the energy policy in order to decrease fuel consumption. In study [6], forecasting of energy demand, wind speed generation and CO2 emissions, which are all vital for the energy sector for the purpose of planning, is carried out. In paper [7], various scenarios in the transportation sector are analysed in order to reduce CO2 emissions and primary energy consumption. In paper [8], the algorithm used in the ANN for the prediction of electricity consumption in Iran is presented. Paper [9] presents the use of the ANN in predicting of energy dependency in Turkey. This is important because information is provided that is related to the dependence of the economy on the import of energy in order to meet demand in the country. The ANN in paper [10] is used for modelling and optimizing energy efficiency in distillation columns. The ANN in paper [11] is used for the prediction of electricity consumption in residential buildings. In Japan [12], peak electric loads are predicted by means of the ANN. In paper [13], electricity tariffs are predicted in order to improve energy management. Depending on the tariff and demand in the forthcoming 3-h period, heating system management has been analysed. Financial security evaluation of the electric power industry in China is evaluated in paper [14] by means of an upgraded ANN and by comparison with the Support Vector Machine (SVM) model and the classical gray forecasting model. This upgraded ANN model has given more precise results, rapid convergence and the probability of entering into local optimum has been reduced. Ermis et al. [15] have analysed the use of green energy in the world by means of ANNs. Also, he emphasizes the important role of renewable energy sources in the country's future policy in order to stabilize the climate. On the other hand, data about solar radiation and the amount of wind also play an important role in the economic and energy strategy of countries because they show the cost effectiveness of the use of solar panels and wind turbines in certain areas. In paper [16], the ANN is used to predict solar irradiation in relation to astronomic and meteorological and climate conditions.
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In ref. [17], the ANN is used to estimate solar irradiation. The ANN in paper [18] is used to determine the wind speed in a certain region more precisely. This enables better definition of wind potentials and operations of wind turbines. Jayaraj et al. [19] have used MultiLayer Perceptron (MLP) and the Elman's ANN for the prediction of wind up to 48 h. Barbounis et al. [20] have used recurrent neural network for long term wind speed prediction (up to 72 h). In paper [21], wind speed has been predicted for different target sites in South Korea using an ANN. The above-mentioned examples show that there are many applications of ANNs for the prediction of indicators in the energy sector. However, none of these studies analyse such a complex energy system as the system of the European Union. The majority of papers use Feed Forward Back Propagation (FFBP) artificial neural networks because they have extensive applications and can resolve the majority of problems. In paper [22], in which the prediction of solar radiation is made for each hour, the four most frequently used networks are compared, FFBP (which is essentially a MLP), Cascade Forward Back Propagation (CFBP), Generalized Regression Neural Network (GRNN) and Elman Back Propagation (EBP). Three error parameters are used for the evaluation. It is concluded that the GRNN shows the best results. Also, the paper has shown that on a concrete problem, the same parameters and the same network in one region can give very good results while in another region, bad results are obtained. Therefore, it is very important to adapt the network to a specific problem. In paper [23], a CFBP network is used to estimate the concentration of harmful gases in urban areas by means of data from surrounding meteorological stations. The CFBP has proven to be a very powerful tool for solving this problem. In ref. [24], the FFBP ANN is used to predict energy consumption in cross laminated timber (CLT) buildings on the basis of eleven characteristic parameters. Majeed Safa et al. [25]. attempt to predict CO2 emissions during wheat production by using two models: 1) MLP and 2) Multiple Linear Regression model (MLR). The results show that the model with the ANN has provided much better results than the MLR model, which confirms that in this case, ANNs can make a valuable contribution. Paper [26] compares the CFBP and the FFBP ANN for the prediction of the behaviour of concrete in the first 28 days after casting. The results show that the CFBP network performs better, especially from the point of less dissipation of simulated values. Although the scope of application of the ANN in paper [26] is not similar to our research, this paper provides yet another in a series of confirmations that ANNs, and especially the CFBP, can be applied very well in predictions and that it is worthwhile examining them in other problems, as well. Although the linear regression model has also often been used for predictions, that model has not been analysed in this paper since it does not provide satisfactory results in resolving nonlinear problems (the complete energy system of the European Union is by its nature nonlinear). Based on the literature it has been assumed that the CFBP model can give better results (less errors and better prediction) in this research. The possession of information about the current parameters of the system, as well as those in the near future enables better management of the system, reaching the targets easier and faster and the reduction of possible occurrence of unwanted and irreversible consequences. Therefore, the analyses of energy parameters represent very important information for state governments for the analysis of their current policies and opens opportunities for the adjustment of the energy policy and strategies in order to achieve the desired objectives. In paper [5], population and GDP have been specified as essential parameters for the assessment of fuel consumption. Paper [27] has analysed the influence of population on CO2 emissions in
the European Union. Ekonomou et al. [28] have used air temperature, installed power capacity, electricity consumption and GDP as parameters for the prediction of long-term final energy consumption in Greece. In ref. [9], energy dependency in Turkey has been analysed using parameters such as total production of primary energy per capita, net imports of gas and primary energy, total gross electricity generation and final energy consumption per capita. In ref. [8], sources of primary energy and their share in the production and consumption of total primary energy are discussed. This paper gives predictions of monthly electricity consumption on the basis of electricity consumption in the previous two months as well as in the same month of the previous year. In paper [29], energy consumption in Turkey is estimated based on data about population, GDP, export and import by means of an ANN. In paper [30], electricity consumption in New Zealand is estimated using parameters such as population, GDP and electricity price. In paper [31], CO2 emissions, energy consumption and economic growth in China are predicted in order to achieve a clean energy economy and halt climate change. Based on the literature it is hypothesized that the following ten parameters are most important for forecasting CO2 emissions in the European Union: GDPppp,3 average annual air temperature, TPES, electricity consumption, population and share of renewable, nuclear, natural gas, total petroleum products and solid fuels energy in the TPES. These parameters can depict current EU policy, as well as the energy strategy in the future. Therefore, these ten parameters represent the quantified EU policy through which the energy system of the EU will be managed in order to analyse CO2 emissions. CO2 emissions greatly affect climate changes and it is necessary firstly to restrict and then to reduce them. Since the energy sector is the largest producer of CO2 emissions, it is necessary to change policies to achieve the desired results. So far, studies have been dealing with predictions of parts of the energy systems and with certain parameters. However, there is not even one paper, to our knowledge, that deals with such a complete and complex energy system as the EU energy system. Various methods have been used for the estimation and forecasting of isolated parts of certain systems of which some are linear and some are nonlinear. Since the EU energy system is very complex and nonlinear by its nature, the ANN model is used in this research. Although the use of the basic model of artificial neural networks (FFBP) model is most often found in the literature, it can also be seen that the CFBP model is shown to give good results. Taking into account the complexity of the current problem, as well as the fact that there is a relatively small set of data, we hypothesized that the CFBP will give better results for this problem in comparison to the MLP (FFBP) network, which pilot tests have proven. So far, research has been dealing with the prediction of the above-mentioned parameters on the basis of the determined relevant energy indicators. This paper includes a combination of ten indicators that have been recognized in the literature as the most important and the most relevant for the concrete problem. As one more novelty, in addition to the estimation of CO2 emissions taking into account the current policy, this model will enable the simulation of various future energy policy scenarios by changing the behaviour of the indicators of CO2 emissions. Therefore, the hypothesis of this research is that an ANN model with CFBP structure and with the use of ten specific indicators can model the complete EU energy system and predict CO2 emissions until 2050 with acceptable accuracy.
3
GDP at Purchasing Power Parity, measured for US Dollar in 2010.
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2. Material and methods 2.1. Neural network structure In this research, we use a modified MLP model of ANN, CFBP which, according to its structure, corresponds to the nature of the problem of predicting the behaviour of a very complex energy system. In the CFBP architecture, each input is connected to each layer of the network, which means that the network has much more opportunities for more accurate correction of errors during the back-propagation process, as well as that inputs themselves correct the weight of each layer. This enables the network to learn complex problems and dependencies among inputs more easily and therefore, the probability of the accuracy of the prediction is increased. We created pilot tests which include modelling and simulation of two ANN structures, CFBP and MLP, with two hidden layers and a different number of neurons in each layer (range from 2 to 20). We have hypothesized that the results of the prediction during the research on this particular problem would be better by using the CFBP due to the above-mentioned reasons. Tests have shown that CFBP networks (Fig. 2) perform better than MLP networks and so the CFBP structure was used in further research. Energy policies are especially important and very complex. It is of the utmost importance to place all variables and relevant sizes in the functional dependency in order to obtain good predictions of CO2 emissions at the output and to establish control over these emissions by the mechanisms envisaged in the energy policy. The CFBP has proven a very powerful network for solving this complex problem because of its great possibilities for generalization and excellent matching of systems. 2.2. Neural network learning mechanism Cascade Forward networks learn to associate specific output to specific input by adjusting its weights based on the output error. The most popular algorithm for weight correction is back propagation, which propagates the error backwards, from the output through all hidden layers [32]. For the output network layer, the error between the computed output and the actual output is calculated. Then, weights are recalculated in order to minimize the error using the steepest descent algorithm. This becomes the desired output value for preceding hidden layer. The process is repeated (propagating error back) for each hidden layer.
Fig. 3. Sigmoid activation function.
allow the network to find and to calculate new coefficients that can describe the state of the system, and then to use their combination in order to calculate the output. Due to the complexity of the energy system and since there is a relatively small set of data (data for only 26 years), it was decided that the number of hidden layers should be kept to a minimum. However, in order for the network to use the potential of multi-layer network (above-mentioned), it is necessary to have more than one hidden layer. As a compromise, two hidden layers were formed. There is a linear and a sigmoidal activation function. The linear activation function transforms inputs into the neuron in a linear way. Thus, at the end, the output from the network is linearly dependent on the input by which the significance of multiple layers is lost, as well as the ability to solve non-linear problems. On the other hand, the sigmoidal function (Fig. 3) enables the creation of non-linear connections between neurons and layers, leading to nonlinear dependency between input and output. There are two types of sigmoidal functions: a logsig and a tansig. Although the nature of the function is the same, we opted for the tansig function because it has a larger output range (1 to 1), which means that the output from the neuron is more precise and that the output is more sensitive to changes at the input. Levenberg-Marquardt is the most often used function for training purposes and it has shown good results. Therefore, we have not changed it. 2.4. Neural network input and output parameters
2.3. Neural network architecture There are several parameters that can be changed in order to adjust the network architecture to the problem. The most important parameters are the number of hidden layers, the number of neurons in each hidden layer, the activation function and the training function. Multiple network layers represent input re-dimensioning and
In order to describe and quantify EU energy policies in order to predict CO2 emissions, the following ten indicators were chosen: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
GDPppp Average annual temperature TPES Electricity consumption Population Share of renewable energy sources in TPES Share of nuclear energy in TPES Share of natural gas energy in TPES Share of total petroleum product energy in TPES Share of solid fuel energy in TPES
The relevant data was taken from the IEA4 database for the period 1990e2015. Fig. 4 shows the first five parameters and their Fig. 2. Cascade forward back propagation (CFBP) Networks architecture used in this research.
4
International Energy Agency (IEA), http://www.iea.org.
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Fig. 4. Normalized values of five input parameters.
trend until 2050. The trend of each indicator is calculated by data fitting [33,34] in the period 1990e2015 by a first-degree polynomial and based on that, linear dependency between indicators and time is obtained in the form of (1) where x is time, y is indicator value and a and b are calculated parameters. After that, a new time axis, 1990e2050, is applied to the function, which results in the assessment of the value of each indicator for the whole from 1990 to 2050.
y ¼ ax þ b
(1)
Why is the period from 1990 to 2015 used? The latest data published by the IEA at the time this paper was drafted was until 2015. The year 1990 is chosen because most analyses take data from this year for the purpose of comparing effects of actions or programs undertaken in order to increase energy efficiency and to reduce CO2 emissions. For example, one of the main objectives of the Europe 2020 Strategy is to achieve 20% reduction of gas emissions causing the greenhouse effect in relation to 1990. In the 1990s, the EU was already a well-established political platform for actions aimed at the reduction of CO2 emissions and the increase of energy efficiency. At the global level, this is marked by Agenda 21 (21 designates the 21st century), that is, the plan of actions in relation to sustainable development which was adopted at the Conference on Environment and Development in Rio de Janeiro in 1992. It has been considered as the upper most achievement of this Conference, which also included the protection of the atmosphere. It can be said that the commitment and organized actions, that will finally enable not only the reduction of CO2 emissions but also controlled and optimum primary energy consumption, started in the 1990s. Nevertheless, it should be noted that only in 2005 was there a noticeable fall in the TPES and other indicators, that is, changes in the desired direction. This shows the slowness of changes in large systems such as the energy system, as the biggest pollutant of the atmosphere. Because of this slowness, the database from a wider period of time was chosen. In the case of neural networks, best practice is to prepare input
data in such a way that the neural network learns more quickly, becomes more efficient in terms of memory usage, and finally, generates better results for simulations. The logic itself within the neural network operates with data in a range of 1 to 1 or 0 to 1 due to the activation functions that are used in hidden layers (transfer functions) [35]. The nature of the parameters that are used in this paper indicates the use of the interval [0, 1]. It is not realistic, for example, that the TPES or the population is negative. Normalization can be a simple scaling of the range or it can be the calculation of the difference between (the previous and the next) values of the specified parameter. In any case, the goal is the same no matter which method is applied. Bringing parameters that are within the range from 0 to 1 enables the ANN to recognize the dependence and mutual influence of parameters more easily and thus make a better assessment of the output, which is also within the same range. After the simulation, it is possible to do post processing in which the obtained data is scaled backward in the actual range of values, enabling better readability and interpretation. Following the previously mentioned trends and best practices, the normalization of all parameters in this research is done to the range 0e1. A great challenge was to find and to define the values of each parameter that would be taken as minimum and maximum values in order to perform normalization. If we regard the ANN as an object to which we have to transfer knowledge, its result in learning and further reproduction of knowledge will directly depend on the way in which we have transferred knowledge, as well as on the availability of curriculum content. Guided by this logic, we recognized normalization as a very serious challenge that should be treated wisely and resolved with maximum care. For each parameter, minimum and maximum values were carefully chosen so that the influence of the parameters is logical. The temperature changes very slowly, and it would not be good to normalize it in such a way that it changes completely from 0 to 1 as this gives it too much importance and the results would be incorrect. Therefore, the minimum and maximum temperature values were the minimum and maximum temperatures recorded in the EU28 in the period 1990e2015. For other parameters, the minimum value of zero and the maximum one were taken so that they reflect possible and realistic maximums within the interval from 1990 to 2050. Table 1 shows the minimum and maximum values that have been used for the normalization of each parameter. The formula for normalization is:
Normalized Indicator ¼
x Xmin Xmax Xmin
(2)
2.5. Neural network e modelling and simulation In the research, the program package MATLAB 2014a with the Neural Network toolbox was used for modelling the system and simulation, as well as for data processing. The network consists of two hidden layers and one output layer (Fig. 2). In the first hidden layer, there are 4 neurons while in the other one, there are 10. The
Table 1 Minimum and maximum indicator values for normalization.
GDP Average Temperature Population TPES Electricity Consumption
Minimum Value
Maximum Value
Unit
Billion USD (2010) o C Million Mtoe TWh
0 16.41 0 0 0
30000 29.98 600 2000 10000
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Levenberg-Marquardt function is used for training. The activation function in the hidden layers is the tangent sigmoid ‘tansig’ function while the linear activation function ‘purelin’ is placed in the output layer. The pre-processing of data includes: 1) Finding the trend of each indicator using MATLAB functions ‘polyfit’ (for finding the first-degree polynomial) and ‘polyval’ for calculating new values in the given period, 2) Data normalizing by means of the above-mentioned algorithm.
2.5.1. Training process Original data was used for the network training (the period 1990 to 2015) while both original data and calculated indicator trends were used for simulations (the period 1990 to 2050). Data is randomly divided into two groups: 1) 80% of data for training and 2) 20% of data for network testing. The training of the neural network depends on several factors that directly affect the outcome of the prediction: a. Choice of a good data set for learning, b. Choice of a sequence for data presentation from the training set, c. Initial weights of each neuron. If the data was badly selected, the ANN would not recognize essential characteristics of the input parameters. Bearing in mind the fact that the ANN randomly chooses neuron weights at the start that are corrected during training, and that the sequence in which input data is entered in the network during training directly affects the correction of weights during the error backward propagation, it is clear that both the above-mentioned factors directly affect the final values of weights, that is, the ANN behaviour. These problems can lead the network to reach local optimum, which causes the network to lose its power of generalization. For this reason, it is very important to train a large number of networks (with different input sets) in order to avoid reaching a local optimum due to badly selected (non-characteristic) inputs. 2.5.2. Simulation and validation process and error analysis In this research, 100 artificial neural networks were modelled and simulated. Out of them, only those networks that fulfil the two criteria specified below were retained: a. The root mean square error (RMSE) for tested data is less than 0.02 (2%). b. The prediction of CO2 emissions in 2050 (simulated data) is between 50% and 70%. The first requirement refers to the accuracy of training (in the period 1990e2015) and it is calculated with the formula
RMSE ¼
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u 2 uPT u y t yt t t¼1 b T
493
less than 2%. The European Union has anticipated that if it continues with the current policy until 2050, CO2 emissions will be reduced from 100% (1990) to some 62%. This information was obtained on the basis of data on the current share of different sectors in the consumption of energy, and the share of specified energy resources in the production of energy and the emission coefficients of certain fuels (RES ¼ 0; Nuclear ¼ 0, Natural Gas ¼ 0.1836, Total Petroleum Products ¼ 0.2517, Solid Fuels ¼ 0.3325 kg/CO2/kWh). Bearing in mind the complexity of the system and the fact that there is no method that is able to predict the future accurately, it is necessary to take this result with a certain reserve. On the basis of that, another criterion for selecting the network was formed and that is the result of network simulation using the current policy should be ±10% of the predicted value for the European Union in 2050. Taking a lower deviation (for example, ± 5%) would increase the importance of the EU results, i.e. it would mean that these results and the method used are more trusted. On the other hand, if the range is increased (to, for example, ± 20%), this would increase the significance of this model and its results, but the possibility of error would also be increased (due to the inaccuracy of ANNs). Therefore, the range of ±10% of the EU predicted value of CO2 emissions in 2050 if the current policy is continued (i.e. 50e70%), becomes logical and acceptable. 3. Results Out of 100 networks that were modelled and simulated, 78 satisfy the first criterion (the root mean square error less than 2%) and 30 of them fulfil both of the above mentioned criteria. Fig. 5 shows the measured (input) data and the result of the simulation of one of the 30 networks. The simulated network completely follows the measured values indicating that the network has managed to fully imitate the analysed energy system. For all 30 qualified results, a root mean square error after the simulation was calculated for the period from 1990 to 2015. In this case, the root mean square error was calculated for the measured normalized values of CO2 emissions and for the values obtained by the ANN simulation in which: 1) trends for the first five inputs and indicators, and 2) measured data for the five other inputs that refer to the share of the certain type of energy in the TPES were used. Fig. 5 shows the simulation curve of the network that had the least root mean square error. Out of all curves, this curve best describes
(3)
where b y t represents simulated value and yt original value, while the second condition is directed to network performances until 2050, i.e. simulated data. The first criterion is significant because it allows a small error (which is necessary to exist in order to be sure that there is no network overfitting, i.e., learning to work only with data from a training session). On the other hand, the error should not be too big either in order to avoid underfitting, i.e., the network cannot generalize at all (it fails to find significant dependencies between inputs and outputs). In this research, we have assumed that the network is sufficiently capable of modelling the system if RMSE is
Fig. 5. Measured and best simulated data of CO2 emissions for the training period (1990e2015).
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the analysed period (1990e2015), that is, the ANN has been trained the best. Therefore, it was assumed that it will also best reflect changes to CO2 emissions as a consequence of current energy policies in periods outside the specified one. In other words, if these energy policies are to be applied in the period after the analysed one (the period after training, 2015e2050), the model estimates that in 2050, CO2 emissions will be 67.72% in relation to those measured in 1990 (Fig. 6). 4. Discussion The research has confirmed the results from pilot tests that the CFBP ANN can be used for efficient modelling of very complex energy systems. Taking into account the fact that 78 out of 100 networks produced results with an error (RMSE) of less than 2%, it can be concluded that the CFBP ANN model is properly chosen and that it is capable of learning such a complex system efficiently.
The calculation and the use of parameter trends (the continuation of the former policy) in the system modelled in the above way have forecast CO2 emissions in the atmosphere in relation to the year 1990. It is necessary to point out that linear regression is used to estimate these indicators until 2050 and that these indicators in the period from 2016 to 2050 are a straight line. Therefore, the logical ANN result for that period, is that CO2 emissions are also a straight line (Fig. 6). The EU Energy Roadmap 2050 anticipates that in 2050, with the continuation of current energy policies in all EU countries, CO2 emission will fall to 62% of those measured in 1990. Taking into account the complexity of the problem and the long-term nature of the predictions, the result obtained in this paper (67.72%) is satisfactory. This assessment can also be made by means of linear regression, that is, by the extrapolation of the CO2 trend for the period 1990e2015, however, since the system is nonlinear by its nature, linear regression cannot provide realistic predictions for the future.
4.1. Inputs for neural network 4.3. Problems and limitations of presented approach In the literature [27,29e31], various indicators are used (global temperature assessment, air humidity, primary energy production and consumption, energy capacity, electricity consumption, population, etc.) to estimate energy parameters (fuel consumption, electricity consumption, total primary energy consumption, solar radiation, emission of CO2 and other gases, etc.) in order to amend and improve policies, achieve an increase in energy efficiency and to reduce the influence of mankind on the environment. However, the combination of parameters used in this paper for the prediction of CO2 emissions, was not appeared in the literature so far. Therefore, in addition to the model itself, it should also be emphasized that the 10 selected indicators represent current energy policies accurately and that it is possible to establish the dependence of the effects of these policies on CO2 emissions and to establish efficient control for the implementation of these policies. 4.2. The results To identify 30 out of 100 networks is a very good result. It is necessary to bear in mind the fact that there are several ways to bring the network into a local optimum during training. Therefore, it is good practice to take as many neural networks as possible in order to increase the chance of finding a model that will credibly correspond to the problem. The fact that as many as 30 networks have been singled out speaks in favor of the ability of this type of ANN to solve a concrete problem, as well as of the fact that parameters have been very well selected and pre-processed.
Fig. 6. Measured data (1990e2015) along with the ANN output result e estimated CO2 emissions by 2050.
As with the all prediction methods, the ANN also has its own problems and limitations. As opposed to axiomatic methods such as linear regression, the way in which conclusions are made relevant to input and output dependence in ANNs is not exact and it is not precisely defined. ANNs are based on setting weights (priorities) for each node, which represent the probability of its activation [36], giving it the power to generalize on one hand and on the other hand, reducing the accuracy of results. For ANNs it is necessary to have a sufficient quantity of representative data based on which it will be formed (trained). ANNs are of limited use in cases when there is not enough information. A small training data set prevents the network from learning all dependencies between inputs and outputs and therefore, to have the power of generalization. Although an increase in the quantity of data increases the probability of better training of the system, it also increases the probability of the problems that occur in large data systems [37]. Furthermore, a badly chosen network structure can lead to overfitting or underfitting. Overfitting (overtraining) occurs when the network has learned the set well by training but in this way, it has lost the power of generalization and prediction (the error when presenting new data is very large). In other words, the model describes current data so well (it provides very small error) that it cannot predict any new patterns. This can happen if the network structure is too complex for the given problem. On the other hand, underfitting (undertraining) occurs when there is insufficient data or when the network structure does not correspond to the problem. Finding a good network structure is a great challenge and it always concerns the way in which to make a good trade-off between overfitting and model complexity. The sequence of presenting input-output pairs during learning is very important to artificial neural networks so that weights can be better corrected. The back-propagation algorithm directly relies on the inputs that are presented to the network, as well as on the initial values of weights which are randomly selected at the beginning of the training process. Therefore, it is necessary to model as many networks as possible with different starting values in order to reduce the probability of network entrance into local optimum and for the purpose of creating a network that provides the best results. It is also very important to be aware that ANNs learn from existing data and that therefore, they use acquired knowledge for decision making and therefore, cannot be expected to predict scenarios that they have not seen before. Bearing in mind the existence of all the above-mentioned
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problems, the only thing that can be done is to minimize their probability and to take the results with a certain dose of reserve. In this research, a sufficiently large set of data was used so that the network can learn essential characteristics of the system, but not so large that it creates new problems. The carefully selected set of ten representative indicators (inputs) and their preparation (normalization) have, therefore, generated logical and very promising results. Also, it is important to emphasize that this model (which incorporates the impact of each indicator on the output) has the potential to predict new CO2 values by giving it modified values of indicators in the future period. In other words, it is possible to predict the impact of a specific (new) policy and strategy of the EU (by changing the values of indicators) and then, in line with the results, to adjust the policy in the real system in order to achieve the desired impact. 5. Conclusion A method for modelling the highly complex energy system of the European Union is presented. The main goal is to determine CO2 emissions until 2050 under the conditions of current EU energy policies using the CFBP ANN model. The IEA database for the period 1990e2015 was used. Ten key indicators were selected for training and simulation. One hundred CFBP networks were modelled, out of which 30 networks were singled out by means of two criteria. One network out of 30 with the smallest root mean square error in comparison to measured data from 1990 to 2015 was used for the final result. The results indicate that the CFBP network can learn the behaviour of such a system with acceptable accuracy (RMSE < 2%) and that CO2 emissions will drop to 67.72% in relation to 1990 emissions if the current EU28 policy is continued. This gives the model the potential for the assessment and analysis of CO2 emissions in the future through modification of indicators (simulating different energy policy and strategy scenarios), which can be a very important factor for reaching the EU goal for CO2 emissions in an easier, quicker and more economical way. Acronyms ANN BP CFBP CLT CO2 EBP EU28 FFBP GA GDP GDPppp GHG GRNN GWP IEA MLP MLR NG RES RMSE SF SVM
Artificial Neural Network Back Propagation Cascade Forward Back Propagation Cross Laminated Timber Carbon Dioxide Elman Back Propagation European Union (Including 28 Countries) Feed Forward Back Propagation Genetic Algorithm Gross Domestic Product Gross Domestic Product based on purchasing power parity Green House Gasses Generalized Regression Neural Network Global Warming Potential International Energy Agency Multi-Layer Perceptron Multiple Linear Regression Natural Gas Renewable Energy Source Root Mean Square Error Solid Fuels Support Vector Machine
TPES TPP
495
Total Primary Energy Supply Total Petroleum Products
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