Prediction of solar radiation values on Indian cities using machine learning and deep learning techniques

Abstract

The use of renewable resources has grown rapidly as non - renewable resources become exhausted, electricity consumption rises, and environmental problems develop. Rapid improvements in energy sources have opened possibilities for using solar energy to generate electricity. Solar panels were used for various purposes, including sun heating, dining, and building lighting. These regular long estimates of global solar radiation from a specific place were critical for effective solar power generation and utilization. The climate patterns of a region determine the quantity of sunlight radiation that reaches the planet's surface. For sustainable energy systems, global solar radiance is regarded as being an essential metric. It was vital to examine the sunlight available in each region before installing any photovoltaic technology. In this study, solar radiation is analyzed with the help of the meteorological dataset of Indian cities (Mumbai, Chennai & Delhi) from meteoblue (meteoblue.com). Comparison is made in two parts; of each town, the data from 2015-2019 is used for training, and data from 2020 is taken for testing. In the second case, data for 2015-2020 for two cities is used for training, and the data for the third city is taken for testing. The independent variables of the dataset include sunshine duration, month, cloud cover, soil temperature, mean monthly temperature, 2m temperature (air temperature at 2 meters above the surface), sea level pressure, wind speed, max temp, min temp. First, independent variables are used for the regression model, and then a stepwise Multi Non-Linear Regression (MNLR) model is applied to find the optimal input variables. Three classifiers were applied to find and compare the performance of the models, namely Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Recurrent Neural Network (RNN), which is a deep neural network mode. The accuracy results of all the SVM, MNLR, ANN, and RNN were comparable, but ANN gave better results with Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE)