Amit Kumar Yadav

Artificial Neural Network based Prediction of Solar Radiation for Indian Stations

ABSTRACT The Artificial Neural Network (ANN) fitting tool is used for the prediction of solar radiation. Solar radiation data from 12 Indian stations with different climatic conditions are used for training and testing the ANN. The Levenberg-Marquard (LM) algorithm is used in this analysis. The results of ANN model are compared with measured data on the basis of root mean square error (RMSE) and mean bias error (MBE). It is found that RMSE in the ANN model varies 0.0486–3.562 for Indian region. Keywords Solar radiation, Levenberg-Marquard (LM) algorithm , Artificial neural network. knowledge. ANN has 1. INTRODUCTION Solar radiation data are required for a number of solar thermal and Solar photovoltaic applications like solar power generation, solar heating, cooking, drying and solar passive design of buildings [1–4]. The measured solar radiation data are not available for most of the sites due to high cost, maintenance of the measuring instruments. As such, various empirical models have been used to predict monthly mean daily solar radiation all over the world [5–9]. The Artificial neural networks (ANNs) are used to solve a number of scientific problems. It has the capability to approximate any continuous non-linear function to arbitrary accuracy [10]. A multi-layer feed-forward neural network can approximate a continuous function due to its robustness, parallel architecture and fault tolerance capability. In past years, ANN models are used by a number of researchers to estimate solar radiation [11–15] and concluded that ANN model are proven to be superior to other empirical regression models. Reddy [11] used Radial Basis Functions (RBF) and Multilayer Perceptron (MLP) models to predict solar radiation using data from eight stations in Oman. So¨zen [12] determined the solar-energy potential in Turkey using artificial neural networks. A Rehman and Mohandes [15] estimated function asdaily global solar radiation for Abha city in Saudi Arabia by taking air temperature, number of day and relative humidity as inputs to neural networks. The results obtained indicate that the mean absolute percentage error (MAPE) is 4.49%. M.A. Behrang et. al [17] predicted DATAdaily global solar radiation for Dezful city in Iran by using different ANN techniques based on different combination of meteorological variables (day of the year, daily mean air temperature, relative humidity, sunshine hours, evaporation and wind speed). The MAPE for the Multilayer Perceptron (MLP) network is 5.21% while this value is 5.56% for Radial Basis Function (RBF) network. Mohandes et al. [16] has used RBF network for modeling solar radiation and compares its performance with MLP model by using latitude, longitude, altitude and sunshine duration as input parameters. The average MAPE for the MLP network is 12.6 and the average MAPE for RBF networks is 10.1. In the present study, an ANN model is developed which can be used to predict solar radiation at any given location in India.

Comparison of different artificial neural network techniques in prediction of solar radiation for power generation using different combinations of meterological variables

The main objective of present study is to compare ANN model develop with neural network fitting tool (nftool), Radial Basis Function Neural Network (RBFNN) in predicting solar radiation for power generation. The three combinations of input variables are considered for prediction. The RBFNN utilizing input parameters as latitude, longitude, height above sea level and sunshine hours has mean absolute percentage error (MAPE) of 4.94% and absolute fraction of variance (R2) of 96.18% respectively and it give better results than conventional solar radiation prediction models (Angstrom, Akinoglu and Ecevit, Bahel, Almorox and Hontoria). Therefore RBFNN can be used for prediction of solar radiation for solar power generation.

ANN based prediction of daily global solar radiation for photovoltaics applications

Measured value of minimum air temperature, maximum air temperature, average air temperature and solar radiation between 1 January 2012 to 31 April 2014 for Hamirpur city in Himachal Pradesh, India are used for prediction of daily global solar radiation (DGSR) with artificial neural network (ANN) technique. The prediction of DGSR are made with three combinations of input variables namely: (i) average air temperature, maximum air temperature and minimum air temperature, (ii) average air temperature and maximum air temperature, (iii) average air temperature. The results shows that ANN model with input variable as average air temperature and maximum air temperature predict DGSR with mean absolute percentage error of 5.35%. It can be used for predicting DGSR for sites where measured solar radiation is not available, proving useful for sizing of solar photovoltaic systems.

Techno economic analysis of PV-Wind-Grid connected systems for power generation in India

The power generation through hybrid systems is becoming important day by day throughout the world. Therefore to find out best hybrid combinations for clean and green city in India has become an important research area. In this study, different hybrid systems such as PhotoVoltaic (PV)-Wind-Grid, PV-Grid and Wind-Grid are investigated for power generation in Chandigarh, India. For analysis 4.8 kWh/day practical load of Embedded lab in Visvesvaraya lecture hall complex at Electrical Engineering Department, National Institute of Technical Teachers Training and Research (NITTTR) Chandigarh has been used for analysis in Hybrid Optimization Model Electric Renewable (HOMER) software. The results show that PV-Wind-Grid combinations produce more power in comparison to PV-Grid and Wind-Grid system. The Cost Of Energy (COE) for calculated proves that PV-Wind-Grid and PV-Grid systems are useful combinations for power generation.