Randomization-based Machine Learning in Renewable Energy Prediction Problems: Critical Literature Review, New Results and Perspectives

TL;DR

This paper reviews randomization-based machine learning methods for renewable energy prediction, highlighting their effectiveness and efficiency, and provides new experimental results demonstrating their superior performance in real-world applications.

Contribution

It offers a comprehensive review of randomization-based approaches in renewable energy prediction and presents new experimental evidence of their advantages over traditional methods.

Findings

Randomization-based algorithms outperform traditional models in accuracy.

These methods achieve lower computational costs.

They show promising results across solar, wind, and hydro-power energy predictions.

Abstract

Randomization-based Machine Learning methods for prediction are currently a hot topic in Artificial Intelligence, due to their excellent performance in many prediction problems, with a bounded computation time. The application of randomization-based approaches to renewable energy prediction problems has been massive in the last few years, including many different types of randomization-based approaches, their hybridization with other techniques and also the description of new versions of classical randomization-based algorithms, including deep and ensemble approaches. In this paper we review the most important characteristics of randomization-based machine learning approaches and their application to renewable energy prediction problems. We describe the most important methods and algorithms of this family of modeling methods, and perform a critical literature review, examining prediction problems related to solar, wind, marine/ocean and hydro-power renewable sources. We support our critical analysis with an extensive experimental study, comprising real-world problems related to solar, wind and hydro-power energy, where randomization-based algorithms are found to achieve superior results at a significantly lower computational cost than other modeling counterparts. We end our survey with a prospect of the most important challenges and research directions that remain open this field, along with an outlook motivating further research efforts in this exciting research field.