Open Access

ARTICLE

Design and Test Verification of Energy Consumption Perception AI Algorithm for Terminal Access to Smart Grid

Sheng Bi^1,2,*, Jiayan Wang¹, Dong Su¹, Hui Lu¹, Yu Zhang¹

1 Guangdong Power Grid Co., Ltd., Guangzhou Power Supply Bureau, Guangzhou, 510620, China
2 South China University of Technology, Guangzhou, 510000, China

* Corresponding Author: Sheng Bi. Email:

Energy Engineering 2025, 122(10), 4135-4151. https://doi.org/10.32604/ee.2025.066735

Received 16 April 2025; Accepted 27 June 2025; Issue published 30 September 2025

Abstract

By comparing price plans offered by several retail energy firms, end users with smart meters and controllers may optimize their energy use cost portfolios, due to the growth of deregulated retail power markets. To help smart grid end-users decrease power payment and usage unhappiness, this article suggests a decision system based on reinforcement learning to aid with electricity price plan selection. An enhanced state-based Markov decision process (MDP) without transition probabilities simulates the decision issue. A Kernel approximate-integrated batch Q-learning approach is used to tackle the given issue. Several adjustments to the sampling and data representation are made to increase the computational and prediction performance. Using a continuous high-dimensional state space, the suggested approach can uncover the underlying characteristics of time-varying pricing schemes. Without knowing anything regarding the market environment in advance, the best decision-making policy may be learned via case studies that use data from actual historical price plans. Experiments show that the suggested decision approach may reduce cost and energy usage dissatisfaction by using user data to build an accurate prediction strategy. In this research, we look at how smart city energy planners rely on precise load forecasts. It presents a hybrid method that extracts associated characteristics to improve accuracy in residential power consumption forecasts using machine learning (ML). It is possible to measure the precision of forecasts with the use of loss functions with the RMSE. This research presents a methodology for estimating smart home energy usage in response to the growing interest in explainable artificial intelligence (XAI). Using Shapley Additive explanations (SHAP) approaches, this strategy makes it easy for consumers to comprehend their energy use trends. To predict future energy use, the study employs gradient boosting in conjunction with long short-term memory neural networks.

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Keywords

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