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BIMLP Model Based on Deep Learning for Predicting Electrical Load Demand
Somayeh Talebzadeh , Reza Radfar , Abbas Toloei Ashlaghi
Abstract:   (93 Views)
The accurate prediction of electricity demand is crucial for efficient energy management and grid operation. However, the complexities of demand patterns, weather variability, and socioeconomic factors make it challenging to forecast demand with high accuracy. To address this challenge, this research proposes a novel hybrid machine learning approach for predicting electricity demand. In this research, first, different regression methods were investigated to solve the problem, the results showed that the multi-layer perceptron (MLP) regression model has the best performance in predicting electricity demand. Furthermore, the proposed system, BIMLP (Bagging-Improved MLP), is designed to iteratively improve its parameters using a binary search algorithm and reduce the learning error using bagging, a technique for ensemble learning. The proposed system was applied on the Electric Power Consumption data set and achieved a value of 0.9734 in the r2 criterion. The results of implementing and evaluating the proposed system demonstrate its satisfactory performance compared to existing techniques.
Keywords: MLP, Bagging, Regression, Electrical load demand.
Full-Text [PDF 2156 kb]   (24 Downloads)    
Type of Study: Research Paper | Subject: Artificial Intelligence Techniques
Received: 2024/07/21 | Revised: 2025/01/22 | Accepted: 2025/01/01
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Creative Commons License
© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.