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ORIGINAL RESEARCH article
Front. Energy Res.
Sec. Sustainable Energy Systems
Volume 12 - 2024 |
doi: 10.3389/fenrg.2024.1416201
Novel Hybrid Data-Driven Models for Enhanced Renewable Energy Prediction
Provisionally accepted- 1 Qassim University, Buraidah, Saudi Arabia
- 2 University of Waterloo, Waterloo, Ontario, Canada
- 3 University of Central Punjab, Lahore, Punjab, Pakistan
Global power grid management depends on accurate solar energy estimation, yet present prediction techniques frequently suffer from unreliability as a result of abnormalities in solar energy data. Solar radiation projections are affected by variables such as anticipated horizon length, meteorological classification, and power measuring techniques. Therefore, a Solar Wind Energy Prediction System (SWEPS) is proposed as a solution to these problems. It improves renewable energy projections by taking sun trajectories and atmospheric characteristics into account. In addition to using a variety of optimization methods and pre-processing techniques, such as Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), Least Absolute Shrinkage Selection Operator (LASSO), and recursive feature addition processes (RFA), complemented by a genetic algorithm for feature selection (GAFS). The SWEPS also makes use of sophisticated machine learning algorithms and Statistical Correlation Analysis (SCA) to find important connections. Neural Network Algorithms (NNA) and other metaheuristic techniques like Cuckoo Search Optimization (CSO), Social Spider Optimization (SSO), and Particle Swarm Optimization (PSO) are adopted in this work to increase the predictability and accuracy of models. Utilizing the strengths of machine learning and deep learning techniques (Artificial Neural Networks (ANN), Decision Trees, Support Vector Machine (SVM), Recurrent Neural Networks (RNN), and Long Short Term Memory (LSTM)) for robust forecasting, as well as meta-heuristic optimization techniques to fine-tune hyper-parameters and achieve near-optimal values and significantly improve model performance, are some of this work contributions to the development of a comprehensive prediction system.
Keywords: machine learning, Smart Metering, Solar Energy, wind energy, modelling, metaheuristic optimization, Deep Learning, Hybrid model, Prediction system
Received: 12 Apr 2024; Accepted: 22 Jul 2024.
Copyright: © 2024 Alharbi and Iqbal. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Talal Alharbi, Qassim University, Buraidah, Saudi Arabia
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