Stored Energy Forecasting of Small-Scale Photovoltaic-Pumped Hydro Storage System Based on Prediction of Solar Irradiance, Ambient Temperature, and Rainfall Using LSTM Method

This paper presents the implementation of forecasting of photovoltaic (PV) power and stored energy on small-scale pumped hydro storage (PHS) systems. The proposed forecasting approach considers the results of predicting solar irradiance, ambient temperature, and rainfall. Prediction of these three parameters was done using a one-month weather dataset that was split into 80% for training and 20% data for testing and validation. The prediction algorithm used is the bidirectional long short-term memory (LSTM) method. Furthermore, PV power and stored energy were calculated using power and energy models of the photovoltaic-pumped hydro storage system modified by considering the prediction results of solar irradiance, ambient temperature, and rainfall. The proposed forecasting approach was simulation-tested on a small-scale photovoltaic-pumped hydro storage system with a capacity of PV is 2 KW, as well as a capacity of the upper reservoir is 5 m³, The performance of the forecasting model is done by measuring the mean square error (MSE) and the mean absolute error (MAE) values. From the simulation test, the results obtained that the suggested approach produces PV power forecasting performance with an MSE of 8.942 watts and MAE of 0.044 watts, and excess power forecasting performance with an MSE of 81.203 watts and MAE of 0.075 watts. The stored energy forecasting performance for MSE and MAE parameters are 2.2times 10{-7} Wh and 7.35times 10{-6} Wh, respectively.