Hourly and Day Ahead Power Prediction of Building Integrated Semitransparent Photovoltaic System

Abstract

The building integrated semitransparent photovoltaic (BISTPV) system is an emerging technology which replaces the conventional building material envelopes and roof. The performance prediction of the BISTPV system places a vital role in the reduction of the energy consumption in the building. In this work, the artificial neural network (ANN) is used to predict the performance of this system by optimizing the important parameter of the feature selection. The Elman neural network (EN) algorithm, feed forward neural network (FN), and generalized regression neural network model (GRN) are investigated in this study. The performance metrics of the errors are analysed such as the root mean square error (RMSE), mean absolute percentage error (MAPE), and mean square root (MSE). According to the findings, the model behaves consistently at the specified time and place in the experiment. Forecasters utilizing neural network models will have better accuracy if they use techniques like EN, FFN, and GRN having the RMSE of 0.25, 0.37, and 0.45, respectively.