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A Novel Two-Stage Hybrid Model Optimization with FS-FCRBM-GWDO for Accurate and Stable STLF
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Version 1
: Received: 24 August 2024 / Approved: 26 August 2024 / Online: 26 August 2024 (15:35:31 CEST)
How to cite: Uwimana, E.; Zhou, Y. A Novel Two-Stage Hybrid Model Optimization with FS-FCRBM-GWDO for Accurate and Stable STLF. Preprints 2024, 2024081852. https://doi.org/10.20944/preprints202408.1852.v1 Uwimana, E.; Zhou, Y. A Novel Two-Stage Hybrid Model Optimization with FS-FCRBM-GWDO for Accurate and Stable STLF. Preprints 2024, 2024081852. https://doi.org/10.20944/preprints202408.1852.v1
Abstract
Accurate, rapid, and stable prediction of electrical energy consumption is essential for decision-making, energy management, efficient planning, and reliable power system operation. Errors in forecasting can lead to electricity shortages, wasted resources, power supply interruptions, and even grid failures. Accurate forecasting enables timely decisions for secure energy management. However, predicting future consumption is challenging due to the variable behavior of customers, requiring flexible models that capture random and complex patterns. Existing forecasting methods, both traditional and modern, have limitations and do not fully meet accuracy expectations. To address these issues, this research introduces a hybrid models that combine FCRBM based forecaster, and GWDO based optimizer, namely FS-FCRBM-GWDO approach to enhance the model performance in STLF have been developed. While some models excel in accuracy and others in convergence rate, both aspects are crucial. The main objective is to create a forecasting model that provides reliable, consistent, and precise predictions for effective energy management. This led to the development of a novel two-stage hybrid model. The first stage predicts electrical energy usage through four modules using deep learning, support vector machines, and optimization algorithms. The second stage optimizes energy management based on predicted consumption, focusing on reducing costs, managing demand surges, and balancing electricity expenses with customer inconvenience. This approach benefits both consumers and electricity corporations by lowering bills and enhancing power system stability. Simulation results validate the proposed model's efficacy and efficiency compared to benchmark models.
Keywords
Genetic wind-driven optimization algorithm; Short-term load forecasting; Factored conditional deep belief network; Efficiency energy consumption
Subject
Engineering, Electrical and Electronic Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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