Heat load prediction in district heating systems with adaptive neuro-fuzzy method

Renewable and Sustainable Energy Reviews 48 (2015) 760–767

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Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser

Heat load prediction in district heating systems with adaptive neuro-fuzzy method Shahaboddin Shamshirband a, Dalibor Petković b, Rasul Enayatifar c, Abdul Hanan Abdullah c, Dušan Marković b, Malrey Lee e,n, Rodina Ahmad d a

Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia University of Niš, Faculty of Mechanical Engineering, Aleksandra Medvedeva 14, 18000 Niš, Serbia c Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia d Department of Software Engineering, Faculty of Computer Science and Information Technology, University of Malaya (UM), 50603 Kuala Lumpur, Malaysia e The Research Center for Advanced Image and Information Technology, School of Electronics & Information Engineering, ChonBuk National University, JeonJu 561-756, ChonBuk, Republic of Korea b

art ic l e i nf o

a b s t r a c t

Article history: Received 25 August 2014 Received in revised form 7 March 2015 Accepted 3 April 2015

District heating systems can play significant role in achieving stringent targets for CO2 emissions with concurrent increase in fuel efficiency. However, there are a lot of the potentials for future improvement of their operation. One of the potential domains is control and prediction. Control of the most district heating systems is feed forward without any feedback from consumers. With reliable predictions of consumers heat need, production could be altered to match the real consumers’ needs. This will have effect on lowering the distribution cost, heat losses and especially on lowered return secondary and primary temperature which will result in increase of overall efficiency of combined heat and power plants. In this paper, to predict the heat load for individual consumers in district heating systems, an adaptive neuro-fuzzy inferences system (ANFIS) was constructed. Simulation results indicate that further improvements on model are needed especially for prediction horizons greater than 1 h. & 2015 Elsevier Ltd. All rights reserved.

Keywords: District heating systems Heat load Prediction Neuro-fuzzy ANFIS

Contents 1. 2.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. District heating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Data acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3. Data used for structuring the model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4. Adaptive neuro-fuzzy application for head load prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Results and discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Evaluation of model performances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. ANFIS prediction of heat load . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Sensitivity analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

760 762 762 762 762 763 764 764 764 766 766 767 767

1. Introduction

n

Corresponding author. E-mail address: [email protected] (M. Lee).

http://dx.doi.org/10.1016/j.rser.2015.04.020 1364-0321/& 2015 Elsevier Ltd. All rights reserved.

Global warming is one of the most important issues to handle in the energy sector, due to the high CO2 emissions from fossil fuel based power plants. The district heating sector can play a significant role in reducing the emissions [1–3]. District heating

S. Shamshirband et al. / Renewable and Sustainable Energy Reviews 48 (2015) 760–767

systems (DHS) are based on simple idea of central production of heat and further distribution of produced heat to final consumers [4–6]. Primary energy use for district heat production is dependent not only on the availability of technology and on the considered environmental and social costs but also on the scale of district heat production [7,8]. Difs et al. [9] demonstrated how an increased use of district heating in industrial processes can lead to more resource-efficient energy systems. Truong and Gustavsson [10] found that the district heat production cost increases and that the potential for cogeneration decreases with smaller district heat production systems. Every DHS comprises of three basic elements: heat source, distribution network and consumers, which are in most cases indirectly (through heating substations) connected to distribution network. The results by Hepbasli and Kec [11] indicated that the interconnections of DHS among all the components are not very strong and one should focus on how to reduce the internal inefficiency (destruction) rates of the components. To improve the efficiency of DHS, heat pumps were integrated in some models [12,13]. The development of DHS is gaining more and more interest, but, in some case the space available for the integration is limited and the use of decentralized systems is necessary in order to improve efficiency of DHS [14,15]. Analysis [16] was shown that regulation of the district-heating sector is necessary in principle, particular in terms of pricing. In order to be competitive with individual heating systems, DHS must use one of the five suitable strategic local energy resources: useful waste heat from thermal power stations (cogeneration); heat obtained from refuse incineration; useful waste heat from industrial processes; natural geothermal heat sources and fuels difficult to manage, such as wood waste, peat, straw, or olive stones [17] and have advanced control which will lower operation and distribution costs. Models for the prediction of the temperature at critical points of district heating systems are paramount for heat suppliers to make optimal decisions on the water temperature at the supply point [18]. Control of district heating systems is complex task and comprises of four different control sub-systems:







Heat demand control Flow control Differential pressure control and Supply temperature control [19].

The performance of the DHS controller was characterized by an economic cost function based on predefined operation ranges [20]. Temperature fault detection in DHS has changed from being slow and expensive to becoming fast and inexpensive according to Gadd and Werner [21]. This is a basic condition for more efficient district heating systems in the future. Fang and Lahdelma [22] developed an estimation model to estimate the water flows, temperatures and heat losses in a DH network based on customer measurements. The developed model, which manages detailed calculation of water flows, temperatures and heat losses in DH pipes, enables robust and accurate DH network state estimation. The district heating system for an building with many floors was analyzed in [23] and it was determined that under prescribed total mass flow rate, the mass flow rate of a floor increases with the increase in its heat load, while those of the other floors decrease and the temperature of supply water increases. A controlled case [24], for one real thermal plant in the district heating system for the purpose of predicting the heat supply was shown that the model-based controller successfully regulates the outlet temperature of the boiler, and the total amount of heat duty has been well reduced due to the constraints on inputs considered in the control algorithm. Precise prediction of heat demand is crucial for optimizing DHS. In a district heating system, errors and deviations in customer substations propagates through the network to the heat

761

supply plants [25]. Based on the defined building types, the average absolute deviation of the predicted heat load was about 4–8% according to Ma et al. [26]. The concept introduced in [27] represented a mass flow control model optimizing the primary and secondary water streams to achieve to achieve better results. Heat demand control and flow control are located at consumers’ premises while the differential pressure control and supply temperature control are located at the heating plant. The main objective in optimal district heating control is efficient operation of district heating system which can be achieved with matching the heat production with real consumers need, as close as possible. In that case, average temperature in distribution network is reduced, distribution heat losses are minimized, pumping costs are reduced, and finally more consumers can be connected to existing distribution network with further increase of efficiency [28–36]. This approach can be regarded as demand side management and is used for years in control of electrical grids [37]. According to Kensby et al. [38], Demand Side Management (DSM) is a portfolio of measures to improve the energy system at the side of consumption. In DHS, demand side management was first introduced in [39]. Authors reported the possibility of temporal reduction (for 2–3 h) of heat load by 25% on average for individual buildings connected to DH system, upon the introduction of DSM strategy. The idea was further elaborated by [40] who proposed multi-agent architecture for automatic, distributed control of district heating systems. In addition, author indicated the necessity for changing the focus in control of DH systems from production and distribution to consumers’ side. This is of uttermost importance since the consumers, through substations in DH system, dictate the flow and temperature in DH network. In distributed control strategy, consumers’ heat demand control is essential in structuring overall control strategy for whole district heating system. With precise predictive model of consumers heat load, heat production i.e. primary supply temperature and flow can be adjusted to real needs which will have effect on minimizing the production costs, distribution losses and decreasing the return temperature which is of special importance for combined heat and power plants. One of the first systematic analyses of heat load in whole district systems was provided by Werner [41]. He performed the comprehensive study of factors affecting the value and character of heat load. Additionally, he proposed linear regression models for heat load in DHS. Further analysis of heat load in district heating system was undertaken by Madsen et al. [42]. In article [43], it was found that hourly heat load variation is just below 40% of the annual average heat load. In this study different nonparametric and parametric methods and models were developed and tested with sampled data from Esbjerg district heating system. It was concluded that for prediction horizon of less than 24 h ARMAX model with trigonometric profile yields best results. Review of parametric, non- and semi-parametric methods and models for heat load was presented in [44]. Additionally, in this report methods for on-line prediction of heat load with available online meteorological forecasts were introduced. Dotzauer [45] presented very simple heat load model and reported acceptable prediction results. Nielsen and Madsen derived grey-box model for heat load of DHS consumers [46]. Additionally, they performed likelihood ratio tests of significance of environmental conditions on heat load. In some recent studies, SARIMA, Kalman filter [47] and neural networks [48] were used for heat load prediction. Even though a number of models have been proposed for heat load prediction, there are still disadvantages of the models in terms of demanding calculation time and accuracy. Artificial neural networks (ANN) can be used as alternative to analytical approach since ANN offers advantages such as no required knowledge of internal system parameters, compact solution for multivariable problems and fact calculation [49–53]. Objective of this paper is development and testing of predictive models of heat load

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for individual consumers in district heating systems for on-line short term forecasting based on adaptive neuro-fuzzy inference system (ANFIS) [54] method, which is a specific type of the ANN family. Developed models will be used for improving local (on the heat substation level) and overall control strategies of district heating system. ANFIS shows very good learning and prediction capabilities, which makes it an efficient tool to deal with encountered uncertainties in any system. Fuzzy inference system does not oblige learning of the physical process as a precondition for its application [55]. ANFIS merges the fuzzy inference system with a neural network learning algorithm. The key objective of this examination is to create an ANFIS for forecasting of the heat load for individual consumers in district heating systems for on-line short term forecasting. The fundamental concept behind the soft computing technique is to gather input/output data sets and to take in the proposed system from these data. This method gives fuzzy logic the ability to adjust the participation capacity parameters that best permit the related. An investigation is completed to concentrate the training and checking data for the ANFIS system. For the presently developed neural network, head load data from heating substation connected to heating plant “Krivi vir” which is independent part of the Nis district heating system in Serbia, was measured and acquired as case studies i.e. as ANFIS training data. The main purpose of this study is to analyses the performances of ANFIS for heat load prediction purposes and possibility of implementation in novel distributed intelligent control strategy of DH system.

2. Materials and methods 2.1. District heating A district heating system consists of a heat producer, a transmission network of pipes, and local substations in which heat from the district heating water is transferred to the radiator circuit and the hot water circuit of the heat consumer. The district heating network is called the primary side, and the consumer circuit is called the secondary side. 2.2. Data acquisition Measurement and acquisition of data, which were lately used for structuring the ANFIS model, was performed in heating substation connected to heating plant “Krivi vir” ( independent part of the Nis district heating system, Serbia), with installed capacity of heat source (gas fired boilers) of 128 МW. The heating substation is indirectly connected to district heating system and hydraulic separation is accomplished through Schmidth-Bretten plate heat exchanger, model SIGMA X13-NCL. Table 1 contains the technical specification of heat exchanger. The substation installation is schematically shown in Fig. 1.

No domestic hot water preparation is envisaged. Heat from substation is delivered across the two-pipe system to cast iron radiators in 60 apartments. Delivered heat in apartments is manually regulated and no thermostatic radiator valves exist. Additionally, there is no measurement of indoor temperature. Flow control and consequently control of delivered heat to consumers is achieved through Danfoss AVQM (DN40) motorized flow control valve. In addition to motorized control, valve has control diaphragm for mechanical flow limitation in order to limit the excessive flow in substation. Circulation of water on secondary side is performed with constant speed Grundfoss twin pump UPSD 50–180/F. Regulation of delivered heat is achieved by Danfoss ECL comfort 300 controller which is placed in control box with other electrical equipment. Controller works on weather compensation principle and controls the temperature of delivered water to consumers/ secondary side through temperature control curve, according to momentary measured outside temperature. There is no feedback from indoor temperature measurement. Two additional modules are integrated in controller: ECA 84 (for measuring of delivered heat) and ECA 87 for storage of measured values. Controller was connected with HCP HAWK high speed GPRS modem for remote data transfer. Archived data, from ECA 87 module were read off regularly during the heating season. Delivered heat is regularly measured and archived through Danfoss Ultrasonic heat meter. 2.3. Data used for structuring the model Gathered data were sampled on 1, 15, 30 and 60 min. Data were collected during the heating season 2007/2008, from November 2007 to April 2008. Preprocessing of data was not taken into consideration. The aim was in developing recursive and robust model capable of producing the on-line predictions of consumers heat load for further use in control of DH systems. Following variables were simultaneously measured:









Outdoor temperature [1C] Primary supply temperature [1C] Primary return temperature [1C] Secondary supply temperature [1C] Secondary return temperature [1C] Flow on primary side [m3/h].

Heat load was calculated based on measured values of primary supply and return temperatures and flow on primary side. Statistical properties of the head load parameters are shown in Table 2. The heat power provided to the district heating network by the heat plant depends on the supply and return temperature and the flow of the water. These values can be measured, and the supplied power can be calculated as _ ðT S ðt Þ  T R ðt ÞÞ P sup ðt Þ ¼ cp mðtÞ

ð1Þ

where P sup ðt Þ is the power supplied to the district heating system _ by the heat plant; cp is the specific heat capacity of water; mðtÞ is

Table 1 Substation plate heat exchanger data Schmidth SIGMA X13-NCL. Heat exchanger characteristic

Value

Inlet temperatures Outlet temperatures Pressure drop Max working pressure Max. working temperature Flows Capacity

On primary On primary On primary 16 kPa 150 1C On primary 650 kW

side 135 1C; on secondary side 70 1C side 75 1C; on secondary side 90 1C side 10 kPa; on secondary side 20 kPa

side 2.7 kg/s; on secondary side 7.9 kg/s

S. Shamshirband et al. / Renewable and Sustainable Energy Reviews 48 (2015) 760–767

763

Fig. 1. Scheme of heating substation.

Table 2 Statistical summary of gathered data. Heat load parameters

Minimum value

Outdoor temperature [1C]  10.60 Primary supply temperature 30.70 [1C] Primary return temperature 25.70 [1C] Secondary supply 27.20 temperature [1C] Secondary return temperature 20.70 [1C] 0.10 Flow on primary side [m3/h] Heat load [kW]  8.74

Maximum value 4.40 88.70

Mean

Median

 1.19  0.30 65.71 72.30

54.30

43.75

46.50

59.10

47.39

50.80

49.70

41.64

44.60

7.20 273.40

6.16 6.20 157.83 182.48

Fig. 2. ANFIS structure.

To build an ANFIS that can predict xðt þ pÞ from the past values of head load, the following training data format was used: ½xðt  10Þ;

xðt  9Þ;

xðt  4Þ; the mass flow of the water; T S is the supply temperature at the power plant; T R is the return temperature at the power plant.

The past values of heat load up to time t are used to predict the value in future tþ p. The standard method for this type of prediction is to create a mapping from D points of the time series spaced “Δ” apart; that is ½xðt  ðD  1ÞΔ⋯xðt  ΔÞ; xðtÞ to predict a future value xðt þ pÞ, where D ¼ 4 and Δ ¼ p ¼ 1 are used. However, another time steps were tested to investigate differences.

xðt 3Þ;

xðt  7Þ;

xðt  2Þ;

xðt  6Þ;

xðt  1Þ;

xðt Þ;

xðt 5Þ;

xðt þ 4Þ :

As can be seen, there are 11 inputs and one output. The inputs are xðt  10Þ;

2.4. Adaptive neuro-fuzzy application for head load prediction

xðt  8Þ;

xðt  9Þ;

xðt  4Þ;

xðt  8Þ;

xðt  3Þ;

xðt  7Þ;

xðt 2Þ;

xðt  6Þ;

xðt  1Þ;

xðt  5Þ;

xðt Þ

and the output is xðt þ 4Þ. The ANFIS network has one input (time) used in the study and three membership functions. In this study bell-shaped membership functions are used since these memberhsip functions hafe the best generalization capatiblities for nonlinear time series. Fig. 2 presents an ANFIS structure with one input (time).

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3. Results and discussion 3.1. Evaluation of model performances To evaluate the performance of the ANFIS models for heat load prediction, following statistical indicators were used: (1) root-mean-square error (RMSE) vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uP u n u ðP Oi Þ2 ti ¼ 1 i RMSE ¼ ; ð8Þ n

Fig. 3. Generalized bell-shaped membership function (a¼ 2, b¼ 4, c ¼6).

(2) coefficient of determination (R2)

Tthe first-order Sugeno model with two inputs is used: if x is A then f 1 ¼ p1 x þ q

" ð2Þ

The first layer is composed of input membership functions (MFs). This layer supplies the input values. In the first layer every node is an adaptive node with a node function O ¼ μðxÞi ; where μðxÞi is MF and i ¼ 1; 2: In this study, bell-shaped MFs (3) with maximum equal to 1 and minimum equal to 0 is chosen f ðx; a; b; cÞ ¼




1

1 þ x  c=a

2b

ð3Þ

where the function depends on three parameters a, b and c as it is shown in Fig. 3. The second layer test for the weights of each MFs. It receives the input values from the first layer and represents the fuzzy sets of the input variables. Every node in the second layer is nonadaptive and this layer multiplies the incoming signals and transfer the output like wi ¼ μðxÞi nμðxÞi þ 1

ð4Þ

Third layer computes the activation level of each rule. Each node of third layer calculates the weights which are normalized. The third layer is also non-adaptive and every node calculates the ratio of the rule’s firing strength to the sum of all rules’ firing strengths like wi w1 þ w2 i ¼ 1; 2:

wni ¼

ð5Þ

The fourth layer is defuzzification layer and it provides the output values resulting from the inference of rules. Every node in the fourth layer is an adaptive node with node function O4i ¼ wni  f ¼ wni pi x þq ð6Þ   where pi ; q is the parameter set and in this layer is referred to as consequent parameters. The fifth layer is called the output layer which sums up all the inputs coming from the fourth layer and transforms the fuzzy classification results into crisp (binary) values. The single node in the fifth layer is not adaptive and this node computes the overall output as the summation of all incoming signals P X i wi  f O5i ¼ wni  f ¼ P ð7Þ i wi i To identify the parameters of the ANFIS architecture hybrid learning algorithm was applied.

R2 ¼

n  P i¼1


 Oi  Oi U P i  P i

n  P

i¼1

#2


 Oi  Oi U P i  P i

ð9Þ

where Pi and Oi are the experimental and forecast values, respectively, and n is the total number of test data. 3.2. ANFIS prediction of heat load At the beginning the ANFIS network is trained with the extracted data from the experimental measurement procedure. 50% data was used for ANFIS training and 50% for ANFIS testing. Three bell-shaped membership functions are used for input fuzzyfication during the training procedure. The ANFIS network has 80 linear parameters and 24 nonlinear parameters. There are 16 fuzzy rules in the ANFIS network. Three experimental datasets were created according to time steps to check influence of the time step range on the ANFIS prediction. Prediction of the four steps ahead was performed for each dataset and compared the results. However, different prediction steps were tested to compare differences. Results for nine different ANFIS prediction models for heat load are shown in Fig. 4. In these figures predicted heat load values are plotted against the observed, in the form of scatter plots. The performances of the ANFIS models for heat load prediction in defined district heating system have been appraised via the wellknown statistical indicators of the root-mean-squared error (RMSE) and coefficient of determination (R2). Performance results of proposed models are summarized in Table 3. Fig. 4 shows that it is obvious that for all models prediction results slightly differ for different time steps and different prediction steps. General pattern is preserved and it seems that prediction results are sensitive to change of time steps and prediction steps. It is obvious that the results are more sensitive of changing of time steps than prediction steps. This observation can be easily checked from Table 3. As to the prediction results, ANFIS had the smallest RMSE of 17.4607 and 12.7018 in training and checking, respectively, for time step of 1 min and with four prediction step ahead (Fig. 4(a)). With increasing of time steps, the ANFIS RMSE increase dramatically. However, for the small time step and high prediction steps the ANFIS prediction RMSE does not changed drastically. The worst prediction results were obtained with ANFIS model with time step of 60 min and four stepes ahead prediction (Fig. 4(d)). To determined influence of the time step on the ANFIS prediction error different time steps were analyzed. Corresponding training and checking errors and testing coefficient of determination are shown in Table 3. Here can be seen that the checking error increases according to time step increasing. Training error was largest for time step of 15 min and smallest for time step of 1 min.

S. Shamshirband et al. / Renewable and Sustainable Energy Reviews 48 (2015) 760–767

Time step 1 min. - 4 steps ahead prediction

y = 0.9597x + 4.6555 R2 = 0.9596

Time step 30 min. - 4 steps ahead prediction

765

Time step 15 min. - 4 steps ahead prediction y = 0.5581x + 51.613 R2 = 0.5243

Time step 60 min. - 4 steps ahead prediction

y = 0.6402x + 38.842 R2 = 0.5363

y = 0.1412x + 124.29 R2 = 0.0116

Time step 1 min. - 30 steps ahead prediction y = 0.8343x + 19.49 R2 = 0.8292

Time step 30 min. - 1 steps ahead prediction

y = 0.7442x + 28.308 R2 = 0.6382

Time step 60 min. - 1 steps ahead prediction y = 0.5556x + 44.557 R2 = 0.3986

Time step 15 min. - 1 steps ahead prediction y = 0.8659x + 14.603 R2 = 0.8184

Time step 15 min. - 2 steps ahead prediction y = 0.7801x + 24.431 R2 = 0.7399

Fig. 4. Performance of ANFIS in heat load prediction for (a) time step of 1 min and 4 steps prediction, (b) time step of 15 min and 4 steps prediction, (c) time step of 30 min and 4 steps prediction, (d) time step of 60 min and 4 steps prediction, (e) time step of 1 min and 30 steps prediction, (f) time step of 60 min and 1 step prediction, (f) time step of 30 min and 1 step prediction, (g) time step of 15 min and 1 step prediction, (u) time step of 15 min and 2 step prediction.

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In the other hand, checking RMS error was smallest for time step of 1 min and largest for time step of 60 min. Fig. 5 shows the forecasting of the heat load (Q[kW]) using ANFIS for the time step of 1 min. The performed prediction is for four steps ahead. The ANFIS prediction results were represented by dashed line and the measure data by solid line.

3.3. Sensitivity analysis ANFIS process for variable selection (sensitivity analysis) was implemented in order to detect the predominant variables affecting the head load prediction. This process includes several ways to discover a subset of the total set of recorded parameters, showing good predictive capability. The ANFIS network was used to perform a Table 3 ANFIS prediction training and checking errors for three data time steps. Time step (min)

Prediction steps

Training RMS error

Checking RMS error

R2

1 15 30 60 1 60 30 15 15

4 steps 4 steps 4 steps 4 steps 30 steps 1 step 1 step 1 step 2 steps

17.4607 56.7497 45.0386 34.1247 36.5679 38.2129 35.5375 27.1322 38.4541

12.7018 54.3545 58.3654 149.723 30.6826 53.1957 38.1751 22.8757 32.6354

0.9603 0.5243 0.5363 0.0116 0.8303 0.3986 0.6382 0.8184 0.7399

4 steps ahead prediction of head load for time step 1 min.

Heat load [kW]

300 250 200 Measured

150

ANFIS

100 50 0

0

500

1000

1500

2000

Time [min]

Fig. 5. ANFIS four steps ahead prediction of the heat load for time step of 1 min.

variable search or to determine how 11 input parameters xðt  10Þ; xðt  4Þ;

xðt 9Þ; xðt 3Þ;

xðt  8Þ; xðt  2Þ;

xðt  7Þ;

xðt  6Þ;

xðt  1Þ;

influence head load prediction Q ðt þ 4Þ: A comprehensive search was performed from the given inputs in order to choose the set of the ultimate optimal combination inputs which has the most impact and influence on the output parameter (head load). Basically, an ANFIS model is built by the functions for each combination and they are then respectively trained for single epoch. Subsequently, the achieved performance is reported. From the outset, the most impactful input in the prediction of the output was identified and determined, as depicted in Fig. 6. The left-most input variables have the lowest number of errors or the most relevance in regards to the outcome (Fig. 6). As it can be clearly seen from the function’s plot and results depicted in Fig. 2, the input variable xðt Þ is the most influential for the head load prediction of the district heating system. The fact that both the checking errors and training are comparable is an indirect indication that suggests that there is no over fitting. The parameter xðt 10Þ has the smallest influence on the head load prediction.

4. Conclusion In this paper a new model based on ANFIS strategy that allows predictions of the heat load for individual consumers in district heating systems for on-line short term forecasting was presented. Three datasets with different time steps was examined. The study carried out a systematic approach to select the most dominant parameters (sensitivity analysis) to predict the head load in district heating systems by the ANFIS methodology. The simulations also employed MATLAB, and outcomes were checked on the corresponding output blocks. The ANFIS network was used to perform to determine how 11 input parameters affect output head load. The results indicated that input variable xðt Þ is the most influential for the head load prediction of the district heating system and the parameter xðt  10Þ has the smallest influence on the head load prediction.

48 46 44 Error

xðt  5Þ;

xðt Þ

42 Training

40

Checking 38 36

Parameters Fig. 6. Every input parameter’s influence on head load prediction.

S. Shamshirband et al. / Renewable and Sustainable Energy Reviews 48 (2015) 760–767

The ANFIS based heat load prediction model proposed in this article is unique and novel as it is simple, reliable and easily accessible for different heating conditions. However, as it was expected, the prediction ability of ANFIS model considerably deteriorates as the prediction horizon increases. The potential improvement could be achieved through introduction of exogenous inputs e.g. outdoor temperature, which has considerable influence on magnitude and character of heat load. Additionally, influence of delayed inputs should be investigated through sensitivity analysis. This will be the objective of our future research.

Acknowledgments This research was supported by next generation information computing development program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning(2012M3C4A7033348). This research was also supported by the Ministry of Education, Malaysia and in collaboration with Research Management Center, Universiti Teknologi Malaysia. References [1] Böttger D, Götz M, Lehr N, Kondziellaa Hendrik, Bruckner Thomas. Potential of the power-to-heat technology in district heating grids in Germany. Energy Procedia 2014;46:246–53. [2] Ancona MA, Bianchi M, Branchini L, Melino F. District heating network design and analysis. Energy Procedia 2014;45:1225–34. [3] Gebremedhin A. Optimal utilization of heat demand in district heating system —a case study. Renewable Sustainable Energy Rev 2014;30:230–6. [4] Di Luciaa L, Ericssona K. Low-carbon district heating in Sweden—examining a successful energy transition. Energy Res Soc Sci 2014;4:10–20. [5] Gopalakrishnan H, Kosanovic Dragoljub. Economic optimization of combined cycle district heating systems. Sustainable Energy Technol Assess 2014;7: 91–100. [6] Aste N, Buzzetti M, Caputo P. District heating in Lombardy Region (Italy): effects of supporting mechanisms. Sustainable Cities Soc 2015;14:43–55. [7] Le Truong N, Gustavsson L. Minimum-cost district heat production systems of different sizes under different environmental and social cost scenarios. Appl Energy 2014;136:881–93. [8] Difs Kristina, Bennstam Marcus, Trygg Louise, Nordenstam Lena. Energy conservation measures in buildings heated by district heating—a local energy system perspective. Energy 2010;35:3194–203. [9] Difs K, Danestig M, Trygg L. Increased use of district heating in industrial processes—impacts on heat load duration. Appl Energy 2009;86:2327–34. [10] Le Truong N, Gustavsson L. Cost and primary energy efficiency of small-scale district heating systems. Appl Energy 2014;130:419–27. [11] Hepbasli A, Kec A. A comparative study on conventional and advanced exergetic analyses of geothermal district heating systems based on actual operational data. Energy Build 2013;61:193–201. [12] Marx R, Bauer D, Drueck Harald. Energy efficient integration of heat pumps into solar district heating systems with seasonal thermal energy storage. Energy Procedia 2014;57:2706–15. [13] Ying W, Yufeng Z. Analysis of the dilatancy technology of district heating system with high-temperature heat pump. Energy Build 2012;47:230–6. [14] Paulus Cedric, Papillon Philippe. Substations for decentralized solar district heating: design, performance and energy cost. Energy Procedia 2014;48: 1076–85. [15] Hassine IB, Eicker U. Control aspects of decentralized solar thermal integration into district heating networks. Energy Procedia 2014;48:1055–64. [16] Wissner M. Regulation of district-heating systems. Util Policy 2014;31:63–73. [17] Werner S. District heating and cooling. Encycl Energy 2004:841–8. [18] Pinson P, Nielsen TS, Nielsen HAa, Poulsen NK, Madsen H. Temperature prediction at critical points in district heating systems. Eur J Oper Res 2009;194:163–76. [19] Frederiksen S, Werner S. District heating and cooling. Lund, Sweden: Student Literature; 2013. [20] Dobos L, Abonyi J. Controller tuning of district heating networks using experiment design techniques. Energy 2011;36:4633–9. [21] Gadd H, Werner S. Achieving low return temperatures from district heating substations. Appl Energy 2014;136:59–67. [22] Fang T, Lahdelma R. State estimation of district heating network based on customer measurements. Appl Therm Eng 2014;73:1211–21. [23] Wang W, Cheng X, Liang X. Optimization modeling of district heating networks and calculation by the Newton method. Appl Therm Eng 2013;61: 163–70.

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