Data Normalisation-Based Solar Irradiance Forecasting Using Artificial Neural Networks
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RESEARCH ARTICLE-ELECTRICAL ENGINEERING
Data Normalisation-Based Solar Irradiance Forecasting Using Artificial Neural Networks Isha Arora1
· Jaimala Gambhir1 · Tarlochan Kaur1
Received: 21 January 2020 / Accepted: 11 November 2020 © King Fahd University of Petroleum & Minerals 2020
Abstract Due to continual day-to-day increase in electricity demand, and hazardous and critical threats of fossil fuels to the environment, researchers are scrutinizing over substitute energy sources. Solar radiation intensity prediction is essential for conducting various research work in the emerging field of Renewable Energy Sources (RESs). This paper has presented development of monthly averaged solar radiation intensity prediction model by employing Artificial Neural Network (ANN) algorithm. Various meteorological parameters have been considered over period of 2 years to execute forecasting for Chandigarh, India. Different normalisation techniques such as min-max, decimal and z-score have been utilised to normalise database. Structure and parameter learning of ANNs has been carried out. Comparative analysis has been done to select optimal architecture based on different performance evaluation measures such as mean square error (MSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and correlation coefficient (R-value) and training time. The network topology with least forecasting errors, higher R-value has been found to be optimum and further simulated for predicting monthly averaged solar radiation intensity for Chandigarh region. Keywords ANN · Data Normalisation · Forecasting · Meteorological Parameters · RESs · Training
List of Abbreviations RESs Renewable Energy Sources ANN Artificial Neural Network MSE Mean Square Error MAPE Mean Absolute Percentage Error MAE Mean Absolute Error R-value Co-relation Coefficient PV Photovoltaic GHI Global Horizontal Irradiance NN Neural Networks FFD Feed Forward Neural Network ENN Elman Neural Network NWP Numerical Weather Prediction ARMA Auto Regressive Moving Average model RMSE Root-Mean-Square Error PSO Particle Swarm Optimisation nRMSE Normalised Root-Mean-Square Error
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Isha Arora [email protected] Punjab Engineering College, Chandigarh, India
MODIS GA
Moderate Resolution Imaging Spectroradiometer Genetic Algorithm
1 Introduction Ever increasing electricity demands, swiftly exhausting fossil fuels, rapid industrialisation and globalisation of society, and rising environmental concerns has witnessed shift towards non-conventional energy resources for wide-scaled energy production. These renewable resources are derived from naturally existing sources such as solar, wind, hydro, fuel cells, tidal and biomass energy that are easily replenishable. Sun is a clean, green, safe, environmental friendly energy source that is inexhaustible. Solar energy is major contributor in renewable energy integration with power grid. The amount of solar energy reaching Earth’s surface each hour is sufficient to meet electricity demands of the entire population for entire
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