Machine Learning Techniques for Solar Power Output Predicting

Abstract

It is a challenge the world has never faced to shift away from energy sources that use fossil fuels and toward ones that are more sustainable and better for the environment. The development of solar photovoltaic (PV) systems is one of the most exciting developments in the field of renewable energy. However, because these devices operate inconsistently and only occasionally, integrating them into the energy grid presents several significant issues. Recent research examined how artificial intelligence (AI) and machine learning (ML) could be used to enhance the management, control, monitoring, maintenance, and performance of renewable energy systems. The aim of this article is to investigate if it is possible to predict the amount of power that photovoltaic (PV) systems will produce using machine learning long short-term memory (LSTM) neural networks and the Nadam optimizer. A particular kind of neural network that has performed well in time series forecasting is the long short-term memory (LSTM) design. The objective of this research is to develop a new method of weather forecasting that, when used over a time horizon of 24 hours, can produce reliable and precise projections of electricity output. The LSTM models are compared to the SARIMA and ARIMA time series models in the article. In comparison with modern approaches, the Nadam optimizer-based LSTM model provides predictions that are more accurate. In an attempt to enhance accuracy and dependability, the article also looks at how climate impacts predict solar energy. The Nadam optimizer and LSTM are combined in this work to anticipate solar power. The article's conclusions will assist in solar power system optimization, operation, and design, which will increase dependability and profitability.