Artificial Intelligence and the Future of Renewable Energy

Abstract

This chapter explores the application of multi-layer feed-forward artificial neural networks (ANNs) in forecasting solar photovoltaic (PV) power generation. Emphasising the growing need for reliable energy sources amidst escalating demands, it delves into integrating renewable energy into the electric grid, a priority for sustainable development. By leveraging historical data and employing backpropagation training algorithms, the chapter demonstrates how ANNs can enhance the accuracy of solar PV power forecasts. This advancement is critical for grid management, allowing for better planning, scheduling, and optimisation of energy resources. The methodology involves data preprocessing, model training, and performance evaluation using root mean square error (RMSE) and correlation coefficients, employing MATLAB for simulation. The chapter asserts that ANN-based models surpass traditional forecasting methods, offering robust and efficient solutions for the renewable energy sector.