Forecasting Global Horizontal Irradiance in Pakistan to Optime Solar Energy Output using Deep Learning Techniques

Abstract

One of the primary challenges in this context is to predict with high accuracy Global Horizontal Irradiance (GHI) for an efficient integration of solar energy into power grids, especially within areas that possess different climates. In this work, we tackle the solar irradiance forecasting problem using a substantially large and complicated data instead of typical ones used in existing literature. The study attempts to improve the generalizability and accurate prediction of GHI by using historical time series for Pakistan. This approach employs several machine and and deep learning models (e.g., Multilayer perceptron, Random Forest, AdaBoost, SVR, RNN, GRU and LSTM). The performance was evaluated on each dataset separately, as well as over their combination. All together to sum up, in deep learning models the LSTM made a perfect job — R2=0.99; RMSE = 2.0134; MAE = 1.2354. While in machine learning models the Multilayer Perceptron model again had a high R2 value of 0.9998, RMSE of 4.3135 and MAE was found to be only at 2.5969. As a result, the usage of bigger datasets for GHI forecasting provided better and dependable predictions. In addition to providing new fundamental understanding, the work also stands to inform practical applications related to operational optimization of photovoltaic (PV) power plants and acumen regarding energy trading strategies that could be leveraged for better integration of solar energy onto a grid. Moreover, the developed methodologies are transferable to other areas as well and thus support globally sustainable energy facilitation.