Forecasting of Solar Radiation for a Cleaner Environment using Robust Machine Learning Techniques

Abstract

An intensified research is going on worldwide to increase renewable energy sources like solar and wind to reduce emissions and achieve the worldwide targets and also to address the depleting fossil fuels resources and meet the increasing energy demand of the population. The Solar Radiation (SR) is intermittent, forecasting the solar radiation beforehand is a must. The objective of this research is to use Modern Machine Techniques for different climatic conditions to forecast SR with higher accuracy.The required dataset is collected from National Solar Radiation Database having features as temperature, pressure, relative humidity, dew point, solar zenith angle, wind speed and direction, with respect to the y-parameter Global Horizontal Irradiance GHI (W/m2). The collected data is first split based on different types of climatic conditions. Each climatic model will be trained on various Machine Learning (ML) algorithms like Multiple Linear Regression(MLR), Support Vector Regression (SVR), Decision Tree Regression (DTR), Random Forest Regression (RFR), Gradient Boosting Regression(GBR), Lasso and Ridge Regression and Deep Learning Algorithm especially Long-short Term Memory (LSTM) using Google Colab Platform. From our analysis, LSTM has the least error approximation of 0.0040 loss at the 100th epoch and of all ML models, Gradient Boosting and RFR top high, when it comes to the Hot weather season – Gradient Boosting leads 2% than RFR and similarly for Cold weather, Autumn and monsoon climate –RFR has 1% higher accuracy than Gradient Boosting. This high accuracy model is deployed in a User Interface (UI) that will be more useful for real-time solar prediction, load operators for maintenance scheduling, stock commitment and load dispatch centers for engineers to decide on setting up solar panels, for household clients and future researchers.