Volume 19, Issue 4 (December 2023)                   IJEEE 2023, 19(4): 101-116 | Back to browse issues page

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Asghari P, Zakariazadeh A. Residential Electricity Customers Classification Using Multilayer Perceptron Neural Network. IJEEE 2023; 19 (4) :101-116
URL: http://ijeee.iust.ac.ir/article-1-2861-en.html
Residential Electricity Customers Classification Using Multilayer Perceptron Neural Network
Pardis Asghari , Alireza Zakariazadeh
Abstract:   (1355 Views)
This paper proposes a novel approach to analyzing and managing electricity consumption using a clustering algorithm and a high-accuracy classifier for smart meter data. The proposed method utilizes a multilayer perceptron neural network classifier optimized by an Imperialist Competitive Algorithm (ICA) called ICA-optimized MLP, and a CD Index based on Fuzzy c-means to optimally determine representative load curves. A case study involving a real dataset of residential smart meters is conducted to validate the effectiveness of the proposed method, and the results demonstrate that the ICA-optimized MLP method achieves an accuracy of 98.62%, outperforming other classification methods. This approach has the potential to improve energy efficiency and reduce costs in the power system, making it a promising solution for analyzing and managing electricity consumption.
Keywords: Smart Meter, Fuzzy C-Means, MLP Neural Network, ICA Algorithm, Residential Electricity Customers.
Full-Text [PDF 1599 kb]   (525 Downloads)    
Type of Study: Research Paper | Subject: Artificial Intelligence Techniques
Received: 2023/04/08 | Revised: 2024/04/20 | Accepted: 2023/07/19
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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© 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.