DESIGN OF A MATLAB GUI FOR SHORT-TERM SOLAR FORECASTING BASED ON DEEP LEARNING

Abstract

In energy systems, it is crucial to forecast solar energy generation for optimization of operations and to mitigate the im-pact of uncertainty. Forecasting solar energy involves predict-ing solar irradiance, for which historical solar irradiance and weather parameter data are typically required. However, such data are often unavailable for residential and commercial solar microgrids. This study proposes an hourly forecasting model for next-day Solar Power Production (SPP) that doesn't rely on historical solar irradiance data. The forecasting is performed using deep learning techniques like Long Short-Term Memory (LSTM), and the results are integrated into the MATLAB&Simulink simulation platform. A graphical user interface (GUI) within the MATLAB&Simulink software complex is presented as a simulation platform for hourly SPP forecasting. This platform serves as a useful tool for researchers studying energy management and forecasting as well as planning renewable energy-based energy system operations. The study involves predicting the amount of energy generated by Solar Power Production (SPP). The developed GUI was employed to forecast the SPP output power over test days. The forecasted data were then evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Experimental results demonstrated reasonable accuracy, with the proposed model achieving an RMSE of 0.835 W and an MAE of 0.353 W in certain datasets.