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

arXiv: https://arxiv.org/abs/2103.14624 · HTML: https://mlsanity.com/paper/2103.14624/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 208 references that are in the map.
- First posted on arXiv 26 Mar 2021.

## Builds on (most cited first, 12 of 12)

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- [Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning](https://mlsanity.com/paper/2012.09816/) (2020)
- [Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates](https://mlsanity.com/paper/1305.5029/) (2013)
- [Explainable AI for Trees: From Local Explanations to Global Understanding](https://mlsanity.com/paper/1905.04610/) (2019)
- [Deep Echo State Networks with Uncertainty Quantification for Spatio-Temporal Forecasting](https://mlsanity.com/paper/1806.10728/) (2018)
- [On the Post-hoc Explainability of Deep Echo State Networks for Time Series Forecasting, Image and Video Classification](https://mlsanity.com/paper/2102.08634/) (2021)
- [Random Forest Ensemble of Support Vector Regression Models for Solar Power Forecasting](https://mlsanity.com/paper/1705.00033/) (2017)
- [Deep Echo State Networks for Short-Term Traffic Forecasting: Performance Comparison and Statistical Assessment](https://mlsanity.com/paper/2004.08170/) (2020)
- [Uncertainty Quantification in Extreme Learning Machine: Analytical Developments, Variance Estimates and Confidence Intervals](https://mlsanity.com/paper/2011.01704/) (2020)
- [Genesis of Basic and Multi-Layer Echo State Network Recurrent Autoencoders for Efficient Data Representations](https://mlsanity.com/paper/1804.08996/) (2018)

## Built on by (most cited first, 1 of 1)

- [Random vector functional link network: recent developments, applications, and future directions](https://mlsanity.com/paper/2203.11316/) (2022)

## Abstract (arXiv)

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.