A Survey on Machine Learning Techniques for The Prediction of Solar Power Production

Abstract

Renewable energy sources are needed globally to support the available non-renewable energy sources our day-to-day living. There is high demand for renewable energy sources in both the developed and developing economies. Solar power is a good example of renewable energy source and people are currently embracing it globally for both domestic and industrial uses. Generally, these energy sources are meant to support the hydro, thermal and other energy sources that are available in different countries of the world. With the popularity of solar energy for both domestic and industrial usage, it can be argued that the estimation of the production level of such energy source is necessary so as to achieve proper planning and management. Due to the fact that the availability of the solar energy power depends largely on a number of environmental and weather conditions, predicting its production or generation can be very important. This study surveyed different works in the area of using machine learning techniques for solar power production prediction. The articles sourced were from notable research repositories. This study focuses on articles that were published between 2013 and 2023 on the subject matter. Different types of machine learning (ML) algorithms that have been used to build models from solar energy datasets are reported in this study. It is believed that the work can provide better insights for the researchers working in the problem area. Thus, the insights in this study can lead to building of improved machine learning-based models for solar power forecasting

Keywords

• Energy Forecasting
• Solar Energy Generation
• PV Panel
• Machine Learning
• Energy Prediction