Development of ANN Based Model for Solar Potential Assessment Using Various Meteorological Parameters

Available online at www.sciencedirect.com

ScienceDirect Energy Procedia 90 (2016) 587 – 592

5th International Conference on Advances in Energy Research, ICAER 2015, 15-17 December 2015, Mumbai, India

Development of ANN Based Model for Solar Potential Assessment Using Various Meteorological Parameters Sanjay Kumara*, Tarlochan Kaura a

Electrical Engineering Department, PEC University of Technology, Chandigarh 160012, India

Abstract Solar potential assessment is very useful for various applications like solar heating, agriculture, solar lighting system and solar power plant erection etc. The objective of the current study is to identify theoretical potential of solar radiation for solar energy applications in hilly state Himachal Pradesh. Artificial Neural Network (ANN) is used to predict solar radiation using site specific measured data of Hamirpur for training and testing. The input variables used are temperature, rainfall, sunshine hours, humidity & barometric pressure to predict solar radiations. To identify the effect of various input parameters on solar radiations three ANN based models have been developed represented by ANN-I5, ANN-I4 & ANN-I3.To obtain best prediction result, the number of input parameters of the input layer have been varied between 3 to 5 and hidden layer neuron have also been varied between 10 to 20. The best mean absolute percentage error (MAPE) calculated for these models (ANN-I5, ANN-I4 & ANN-I3) are 16.45%, 18.77% and 19.39% respectively. The ANN-I5 (temperature, humidity, barometric pressure, rainfall and sun shine hours), model has shown good prediction accuracy as compared to other two models. This study shows that various numbers of meteorological parameters mostly affect the forecasting of solar radiation. The method in this paper can also be used to identify the solar energy potential of any location worldwide where it is not possible to install direct measuring instrument. © Published by Elsevier Ltd. This ©2016 2016The TheAuthors. Authors. Published by Elsevier Ltd. is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of ICAER 2015. Peer-review under responsibility of the organizing committee of ICAER 2015 Keywords: Solar potential; ANN; Solar radiation prediction

* Corresponding author. Tel.: +91-9418527365; fax: +0-000-000-0000 . E-mail address: [email protected]

1876-6102 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of ICAER 2015 doi:10.1016/j.egypro.2016.11.227

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1. Introduction For solar potential assessment, the conventional methods, empirical, analytical, mathematical simulation have been used by various researchers. A precise knowledge of the level of global radiation is essential in designing and studying solar energy applications. As the cost of the solar radiation measuring instruments are very high, so it is not possible to install these measuring instruments everywhere [1]. Moreover solar potential assessment has become more important in the recent years due to increase interest in solar application areas. Nomenclature ANN BP H LM MLP MAPE MSE SH P T

Artificial neural network back propagation humidity Levenberg–Marquardt multi layer perceptron mean absolute percentage error mean square error sun shine hours barometric pressure temperature

Mohandes et al. [2], used various geographic and meteorological parameters such as latitude, longitude, altitude and sunshine duration to develop a artificial neural network model for Saudi Arabia. The multi-layer feed-forward neural network with back propagation (BP) algorithm is used to train the network. Ouammi et al. [3], developed artificial neural network (ANN) model for estimating monthly solar irradiation of 41 Moroccansites. The data period used is from1998 to 2010 and inputs for the networks are normalized values of longitude, latitude and elevation. The predicted solar irradiation varies from 5030 to 6230 Wh/m2/day. Hontoria et al. [4], proposed multi layer perceptron (MLP) model for solar potential assessment in the form of map in Spain and concluded that this method is better than classical method. Şenkal and Kuleli [5], used ANN and physical model to predict solar radiation for 12 cities in Turkey by taking different inputs parameters like latitude, longitude, altitude, month, mean diffuse radiation and mean beam radiation. The data for training and testing was divided by taking 9 cities data are used for training and remaining three cities data for testing. The RMSE calculated for training and testing cities turned out as 91W/m2 and 125 W/m2. Neural tool box is used by Fadare [6], to design multi layered feed-forward back-propagation neural networks for prediction of solar energy potential over 195 cities in Nigeria. Geographical and meteorological data of these cities for period of 10 years (1983–1993) were taken from the NASA website for training and testing purpose. The reliability of model was assessed on the basis of correlation coefficient. Yadav et al. [7], found a suitable variables for accurate solar radiation prediction using ,Waikato Environment for Knowledge Analysis (WEKA) software and applied to 26 Indian locations having different climatic conditions to find most influencing input parameters for solar radiation prediction in ANN models. Hasni et al. [8], modeled global solar radiation using air temperature, relative humidity as inputs in south-western region of Algeria. The training is done using LM feed-forward back Propagation algorithm and transfer function in hidden, output layers is hyperbolic tangent sigmoid, purelin respectively. Elminir et al. [9], estimated hourly and daily values of the diffuse fraction (KD) using ANN in Egypt. The network incorporates inputs as the global solar radiation, long-wave atmospheric emission, air temperature, relative humidity and atmospheric pressure to predict hourly KD. From the literature it can conclude that solar potential assessment is depend upon geographical location and meteorological parameters.

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2. ARTIFICIAL NEURAL NETWORKS (ANNS) Artificial neural network (ANN) is a combination of three layers network consists of large number of neurons that perform the task of processing input data into desirable output. A MLP feed-forward network is commonly used, which comprises three layers namely of input and output layer with one hidden layer [10]. These layers associate with specific number of neurons. The neurons in each layer are associated to some synaptic weights. The input parameters are given to input layer and number of neurons in the layer associates with input parameters. The data from input layer is process to hidden layer and further it process output layers. The number of hidden layers neurons is randomly varied till the error not reaches minimum value. The output neurons are fixed. In the training algorithm synaptic weights of the neurons are updated in each iteration till the error between output and input is not with in tolerance. In the present work Levenberg-Marquardt algorithm has been used for training purpose because it is very fast as compared to gradient descent and gradient descent with momentum. In the algorithm weighted sum of input neuron is applied to activation function to produce an output signal is given by equation:  

 




(1)

Where Y is output, x is input neuron, w synaptic weight respectively and φ is nonlinear activation function. 3. Methodology 3.1. Study Area and ANN data preparation for prediction model The area chosen for this study is district Hamirpur of Himachal Pradesh. District Hamirpur occupies total area of 4,121.9 square kilometers. The Hamirpur District is surrounded by Shivalik hill range and located between North latitude 31.6333° N and Longitude 76.5167° E. The altitude of the study area is 785 meters [11] . It is located at lower elevation and is generally warmer as compared to other districts. So solar potential assessment for this region has been done from the point of view of solar applications. In the present study, temperature (T), humidity (H) , barometric pressure (P), rainfall (RF) and sun shine hours (SH) are the inputs and daily solar radiation are output, taken from energy and environment center of NIT Hamipur. After the preparation of input parameters all inputs are used to predict the prediction models. The combinations of the input parameters are varied from 5 to 3 to get three different ANN model. The model having 5 inputs has been represented by ANN-I5, and the model with four inputs by ANN-I4 and the model with three inputs by ANN-I3 . In first model, all five inputs are taken together for the network such that: ANNI 5 = F [T , H , P, RF, SH]

(2)

In second model, four parameters are taken together as inputs: ANNI 4= F [T , H , P, RF ]

(3)

In the last and final model, three parameters are taken as inputs: ANNI 3 = F [T , H , SH ]

(4)

The Levenberg-Marquardt optimization algorithm is used for the training, testing and validation. For the feed forward network data has been randomly divided for training, testing and validation. The data set distribution for training 70%, testing15% and validation15% randomly. The hidden layer neurons are varied in the network from 1020, the neural networks are trained number of time to check best network with good accuracy. The proposed methodology for the ANN model is shown in figure 1.

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Preparation of database for different ANN Change of hidden layer neurons

Designing of different models

Change the number of inputs parameters

Training & Testing of Different models

Calculation of MAPE

Is MAPE with in tolerance?

Selection of best accurate model from ANN

Solar potential Fig. 1. Implementation of proposed methodology for ANN

4. Results and discussion ANN based models have been proposed using artificial neural networks tool (nftool). The performance of these models has been evaluated on the basis of MAPE. The MAPE is given by Eq. (5) and ANN model with minimum MAPE can be used for prediction of solar radiation. ⎛1 MAPE = ⎜ ⎜n ⎝

n

∑ i =1

SRi ( ANN ) − SRi (actual ) ⎞ ⎟ × 100 ⎟ SRi (actual ) ⎠

(5)

Where n is number of samples. SRi(ANN) is the output values of solar radiations for the ith day and SRi(actual) is the target values of solar radiations for the ith day. The range of forecasting accuracy is defined in terms of MAPE, the MAPE ≤ 10% has high prediction accuracy, the MAPE between 10% to 20% shows good prediction and MAPE 20 to 50% shows normal accuracy and greater than 50% shows bad accuracy [12]. The performance indices for ANN-I5, ANN-I4 & ANN-I3 models in terms of MAPE are evaluated to be 16.45%, 18.77%, 19.39% respectively, representing that as the input meteorological parameters decrease the accuracy of the network go on decreasing. The error evaluation for all the models shown in Table 1. The back-propagation algorithm used in this model minimizes the error function can be expressed as:

⎛1 n ⎞ e = ⎜ ∑ (d i − oi ) 2 ⎟ ⎝ n i =1 ⎠

(6)

Where e is the error, ‘n’ is the number of samples, ‘d’ is desired output and ‘o’ is the output of ANN model

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Table 1 Error Evaluation for different Models Inputs, Output and Hidden Layers

MLP architecture

R for training

MAPE

First model: ANNI 5 = F [T , H , P, RF, SH]

5-20-1

82.76

18.30

5-19-1 5-18-1 5-17-1 5-16-1 5-15-1 5-14-1 5-13-1 5-12-1 5-11-1 5-10-1 4-20-1 4-19-1 4-18-1 4-17-1 4-16-1 4-15-1 4-14-1 4-13-1 4-12-1 4-11-1 4-10-1 3-20-1 3-19-1 3-18-1 3-17-1 3-16-1 3-15-1 3-14-1 3-13-1 3-12-1 3-11-1 3-10-1

85.62 85.44 82.92 88.41 84.12 80.09 80.85 82.76 80.72 81.24 78.76 83.20 81.74 83.89 82.15 86.50 81.27 83.04 84.52 86.35 81.18 81.22 81.31 83.67 85.26 84.91 80.00 83.85 83.56 75.46 85.96 80.83

17.45 17.78 18.43 16.45 20.91 20.72 20.91 18.23 20.71 19.75 20.58 19.54 21.69 20.03 19.84 18.77 20.14 18.92 20.36 18.99 21.03 20.48 21.05 23.12 19.53 20.78 19.83 20.33 20.21 21.47 25.59 19.39

Second model:

ANNI 4= F [T , H , P, RF ]

Third model: ANNI 3 = F [T , H , SH ]

Best Predicted Model The ANN model with MLP (5-16-1) with 5 neurons in input, 16 neurons in hidden layer and 1 neuron in output layer is best as it has least MAPE.

The ANN model with MLP (4-15-1) with 4 neurons in input, 15 neurons in hidden layer and 1 neuron in output layer is best as it has least MAPE.

The ANN model with MLP (3-10-1) with 3 neurons in input, 10 neurons in hidden layer and 1 neuron in output layer is best as it has least MAPE.

For obtaining best performance networks are run number of times. The performance plot of first model which has minimum MAPE is shown in Figure 2. The performance plot represents that as number of iteration go on increasing MSE becomes minimum. The plot also evaluates that validation and testing data set error have almost similar characteristics and best performance of the model occurred at iteration (epoch) 27.The regression plot of correlation coefficient (R) shown in the figure 3, shows the relationship connection between the outputs and targets for ANN-I5 model. The R-value of 0.85 shows good relationships between target and ANN output.

Fig.2. Performance plot for ANN-I5 model with best MAPE

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Fig. 3. Regression plot for ANN-I5 model with best MAPE

5. Conclusion In this paper, three ANN-models are proposed for solar potential assessment in district Hamirpur (Himachal Pradesh). Daily solar radiation has been estimated using these three models and a comparison of these three models has been made on the basis of their performance indices. Obtained MAPE from these models show that first model (AAN-I5) has better results as compared to other models. In case of each model, number of hidden layer neurons are varied between 10-20 to check the prediction accuracy. It can be concluded that the model which has more number of meteorological parameters give more accuracy as compared to other two models. In the future, study of the most significant input variables which influence the solar potential assessment, can be carried out. References [1] Sebaii, F. S. Hazmi, A. A. Ghamdi and S. J. Yaghmour, Global direct and diffuse solar radiation on horizontal and tilted surfaces in Jeddah Saudi Arabia, Applied Energy. 87 (2010) 568-576. [2] M. Mohandes, S. Rehman and Halawani, To estimation of global solar radiation using artificial neural networks, Renewable Energy. 14(1998) 179-184. [3] A. Ouammi, D. Zejli, H. Dagdougui and R. Benchrifa, Artificial neural network analysis of Moroccan solar potential, Renewable and Sustainable Energy Reviews. 16(2012) 4876-4889. [4] L. Hontoria, J. Aguilera and P. Zufiria, “An application of the multilayer perceptron: solar radiation maps in spain, Solar Energy. 79 (2005) 523–530. [5] O. Senkal and T. Kuleli., Estimation of solar radiation over Turkey using artificial neural network and satellite data, Applied Energy. 86 (2009) 1222-1228 [6] D. A. Fadare, Modeling of solar energy potential in Nigeria using an artificial neural network model, Applied Energy. 86 (2009) 14101422 [7] A. K. Yadav, H. Malik and S. S. Chandel, Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models,” Renewable and Sustainable Energy Reviews. 31 (2014) 509–519 [8] A. Hasni, A. Sehli, B. Draoui, A. Bassou and B. Amieur, “Estimating global solar radiation using artificial neural network and climate data in the south western region of Algeria. 18 (2012) 531-537 [9] H.K. Elminir, Y.A. Azzam and F. I. Younes, Prediction of hourly and daily diffuse fraction using neural network, as compared to linear regression models,” Energy , 2007 32 (2007) 1513-1523. [10] Haykin S. Neural networks, a comprehensive foundation. New York,MacMillan, 1999. [11] http://www.himachalpradesh.co.in/category/locations/ [12] Lewis CD. International and business forecasting methods. London: Butter- worths, 1982.