Utilization Of Artificial Intelligence (AI) And Machine Learning (ML) in the Field of Energy Research

Aug 4, 2026

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Many governments commit to carbon neutrality by 2050, but solar and wind generation is inherently uncertain, leading to energy surplus or shortage. This study evaluates current machine-learning-based approaches to forecast renewable energy demand and capacity using algorithms such as SVM, RNN, NN, and ELM, with accuracy enhanced through optimization via metaheuristics and evolution. Hybrid MLS strategies are compared with standalone optimizers, and gaps are identified for probabilistic forecasting, richer ANN data, NWP integration, and shorter prediction horizons.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Abstract

Introduction

Research Objectives of the Study

Utilization Of Artificial Intelligence (AI) And Machine Learning (ML) in the Field of Energy Research Dr. Mohammed Saleh Al Ansari Associate Professor College of Engineering, Department of Chemical Engineering University of Bahrain [Malansari@uob.edu.bh](Malansari@uob.edu.bh) Abstract Many governments have committed to becoming carbon neutral by 2050. The main argument is that renewable resources are more eco-friendly than fossil fuels. However, the unpredictable nature of solar and wind power results in either excess or lack of energy generation. This article will evaluate the current machine-learning-based solutions for forecasting renewable energy demand and capacity. Many researchers have used machine learning (ML) to anticipate the amount of generated wind or solar energy. SVM, RNN, NN, and ELM are the most utilized algorithms. Prediction accuracy is improved through optimization (metaheuristics and evolution). These methods can forecast renewable energy for periods ranging from seconds to months. This article compares several ML methodologies and metaheuristic strategies and reviews the current state of research. The hybrid MLS outperforms the standalone optimizers. A more extensive data set for ANN, the introduction of NWP, and a shorter prediction timeframe are suggested as alternatives to Bayesian and random grid tuning. Further research on probabilistic predictions and mathematical relationships between inputs and outputs is needed to close the research gap. Keywords: Renewable energy; Machine learning; Energy forecasting; Metaheuristics; literature review, Recycled aggregate concrete, Durability Introduction Solar and wind power are the two forms of renewable energy that are now the most common and have the most potential. These are renewable forms of natural energy. The application of their use is beneficial to people without being detrimental to the environment. Solar energy, in both its direct (heat) and indirect (photovoltaic) forms, is the most environmentally friendly form of energy (Arevalo, Santos, Rivera, & conversion, 2019) . Electricity can also be produced through the use of wind's kinetic energy (Shoaib, Siddiqui, Rehman, Khan, & Alhems, 2019) . Because both sources are affected by the weather, it is hard to predict their expected patterns, which results in the requirement for grid maintenance. Solar and wind energy are susceptible to interference from a variety of factors, including air pressure, temperature, humidity, wind speed and direction, insulation time, and many more. Asa result, photovoltaic (PV) power is an important component of most renewable energy sources. As a result, a large number of researchers have been interested in its modeling and its prediction in order to improve the control of the electric structures that are composed of PV arrays. Synthetic neural networks are one of the prevalent methodologies, and they have demonstrated their overall effectiveness within the context of the prediction of solar radiation. However, the currently available models of neural networks are unable to satisfy the requirements of certain one-of-a-kind scenarios, like the one that was investigated for this article. The goal of this research project is to power a racing sailboat using only non-renewable resources, such as solar panels and wind turbines. The answer that has been evolved anticipates the direct sunlight radiation on a surface that is horizontal. For this purpose, a neural community known as a Nonlinear Autoregressive Exogenous (NARX) model is utilized. We take into account every one of the sailboat operation's one-ofa-kind circumstances. The results demonstrate that first-rate prediction overall performance can be obtained even while the training component of the neural community is being carried out on a periodic basis (Boussaada, Curea, Remaci, Camblong, & Mrabet Bellaaj, 2018) . Machine learning techniques were used to make hourly, daily, weekly, monthly, and ye

FAQs

Why is forecasting solar and wind power challenging in energy systems?

Solar and wind output vary with weather conditions such as air pressure, temperature, humidity, and wind speed and direction, making patterns difficult to predict and requiring grid maintenance.

Which machine learning algorithms are commonly used for renewable energy prediction?

The study highlights SVM, RNN, NN, and ELM as widely used algorithms for anticipating generated wind or solar energy.

How do metaheuristics and evolutionary optimization improve forecasting accuracy?

Optimization using metaheuristics and evolution enhances prediction accuracy by improving model performance during tuning and search for better solutions.

Aug 4, 2026

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