Data-driven prediction models of photovoltaic energy for smart grid applications

Due to the low total cost of production, photovoltaic energy is a key component of installed renewable energy worldwide. However, photovoltaic energy is volatile in nature as it depends on weather conditions. This makes the integration, control, and operation of this type of energy difficult for network operators. Therefore, renewable energy forecasting is emerging as a key solution to effectively manage renewable energy in the power system. In this paper, we present a time series based energy forecasting system using a solar home database. We worked on Australian data recorded from 2010 to 2013. Three large databases have been combined to obtain better results. The objective is to test and compare the three prediction algorithms, namely: LSTM, FFNN and GRU. We also compared the performances via RMSE, MAE and R2_score. The results showed that neural networks accurately and reliably predict the energy consumed with R2_score up to 0.92 and MSE not exceeding 0.23, with a slight difference between the three models tested in this work. (c) 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This paper presents a time series based energy forecasting system using a solar home database. Neural networks accurately predict energy consumption with high R2_score and low MSE.