Innovation in Energy Management: Optimizing Electricity Use with the LVQ Perceptron Method
Received: 24 December 2025 | Revised: 13 January 2026 and 30 January 2026 | Accepted: 11 February 2026 | Online: 4 April 2026
Corresponding author: Indrianto Indrianto
Abstract
This study proposes an energy management model based on the Learning Vector Quantization (LVQ) Perceptron to improve classification accuracy and support structured decision-making in electricity management. The model classifies customer electricity consumption into three categories: Low, Medium, and High usage. These classifications are then used as recommendations to regulate power plant operations according to actual demand patterns. A hybrid LVQ Perceptron algorithm was developed to select power generation units based on consumption levels. The model performance was evaluated through experiments using different training and testing data compositions, with a constant learning rate of 0.01 and variations in the number of hidden neurons. The results show that increasing the number of hidden neurons improved the classification accuracy. The model achieved 93.7% accuracy with 10 neurons, 93.8% with 20 neurons, and 95.8% with 50 neurons. These findings suggest that the LVQ-based approach is effective in classifying electricity usage data and can serve as an alternative algorithm for optimizing energy distribution. The proposed method contributes to more efficient energy management by aligning electricity generation with consumer demand patterns.
Keywords:
energy management, optimization, LVQ Perceptron, accuracy, electricityDownloads
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