Abstract:
Challenges in utilising fossil fuels for generating energy call for the use
of renewable energy. This study focuses on modelling and forecasting
solar energy and optimum tilt angle of solar energy acceptance using
historical time series data collected from one of the South African radiometric
stations, USAid Venda station in Limpopo province. In the study
we carried out a comparative analysis of Random Forest and Bayesian
linear regression in short-term forecasting of global horizontal irradiance
(GHI). To compare the predictive accuracy of the models, k-Nearest
Neighbors (KNN) and Long short-term memory (LSTM) are used as
benchmark models. The top two models with the best performances
were then used in hourly forecasting of optimum tilt angles for harvesting
solar energy. The performance measures such as MAE, MSE, and
RMSE were used and the results showed RF to have better performance
in forecasting GHI than other models, followed by the LSTM and the
third best model was the KNN whereas the BLR was the least performing
model. RF and LSTM were then used in modelling and forecasting
the tilt angles of optimal solar energy acceptance and as thus, the LSTM
outperformed the RF by a small margin.