Artificial Neural Networks Based on Optimization Technique for Short-Term Electricity Demand Forecasting: Uttaradit Rajabhat University Data Analysis
Artificial Intelligence and Big Data Strategies for Predictive Maintenance in Industry 4.0: A Systematic Review from 2019 to 2024 |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-6 |
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| Year of Publication : 2025 | ||
| Author : Rattapon Dulyala, Sitthisak Audomsi, Worawat Sa-Ngiamvibool | ||
| DOI : 10.14445/22315381/IJETT-V73I6P116 | ||
How to Cite?
Rattapon Dulyala, Sitthisak Audomsi, Worawat Sa-Ngiamvibool, "Artificial Intelligence and Big Data Strategies for Predictive Maintenance in Industry 4.0: A Systematic Review from 2019 to 2024," International Journal of Engineering Trends and Technology, vol. 73, no. 6, pp.183-190, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I6P116
Abstract
The research examines the efficacy of Artificial Neural Networks (ANNs). When combined, it is accompanied by optimization techniques for predicting electrical load demand. The models include a standalone ANN and an ANN combined with optimization methodologies. The models' performance was assessed utilizing Mean Absolute Percentage Error, and the ANN, accompanied by the Bee Algorithm, demonstrated the highest accuracy and a MAPE of 2.1559%. The standalone ANN had the lowest accuracy, accompanied by a MAPE of 4.3038%. The study found that ANN accompanied by BA effectively matches predicted and actual load values, while the standalone ANN had considerable prediction errors. The study used a notebook with high-end specifications to process and enhance the models, ensuring consistent parameter settings and neural network configurations. The results underscore the importance of combining optimization methods accompanied by ANNs for improved forecasting precision. Future research could explore hybrid optimization methods and expand their applications to additional datasets for more extensive validation.
Keywords
Artificial Neural Network, Particle Swarm Optimization, Genetic Algorithm, Bee Algorithm, Electrical Load Forecasting.
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