A Hybrid Machine Learning Model for Solar Power Forecasting

Abstract

The paper presents a near investigation of different AI procedures for solar power forecasting. The objective of the research is to identify the most accurate and efficient machine learning algorithms for solar power forecasting. The paper also considers different parameters such as weather conditions, solar radiation, and time of day in the forecasting model. This paper proposes a hybrid machine learning model for solar power forecasting that consolidates the strengths of multiple algorithms, including support vector regression, random forest regression, and artificial neural network. However, the study also highlights the importance of incorporating domain knowledge and feature engineering in machine learning models for better forecasting accuracy.

A Solar and Wind Energy Evaluation Methodology Using Artificial Intelligence Technologies

“…The peculiarity of the proposed methodology is the generalization of the main used intellectual approaches that can be used at the stages of implementation of model building for the estimation and forecasting of solar or wind power generation, with the addition of the stage of building digital doubles that include the estimation and forecasting models necessary for controlling the energy system. There are a number of works that focus on the implementation of hybrid forecasting models [73][74][75][76][77], but they discuss specific approaches used at a particular model implementation stage without summarizing the main methods and algorithms that can be used at each specific implementation stage.…”