Deep Learning and Heuristic Optimization for Secure and Eficient Energy Management in Smart Communities

作     者：Murad Khan Mohammed Faisa Fahad R.Albogamy Muhammad Diyan 

作者机构：Department of Computer Science and EngineeringKuwait College of Science and TechnologyDoha DistrictKuwait CityP.O.Box 35001Kuwait Computer Sciences Programpartment of MathematicsTurabah University CllegeTaifUniversityO.Box99Taif2944 Saudi Arabia Department of Computing&GamesTeesside UniversityMiddlesbroughTS13BXUK 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模(英文))

年 卷 期：2025年第143卷第5期

页      面：2027-2052页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Funded by Kuwait Foundation for the Advancement of Sciences(KFAS)under project code:PN23-15EM-1901 

主　　题：Community-centric internet of things energy management micro-grids smart homes deep learning prediction security 

摘      要：The rapid advancements in distributed generation technologies,the widespread adoption of distributed energy resources,and the integration of 5G technology have spurred sharing economy businesses within the electricity *** technologies such as blockchain,5G connectivity,and Internet of Things(IoT)devices have facilitated peer-to-peer distribution and real-time response to fluctuations in supply and ***,sharing electricity within a smart community presents numerous challenges,including intricate design considerations,equitable allocation,and accurate forecasting due to the lack of well-organized temporal *** address these challenges,this proposed system is focused on sharing extra electricity within the smart *** working of the proposed system is composed of five main *** phase 1,we develop a model to forecast the energy consumption of the appliances using the Long Short-Term Memory(LSTM)integrated with the attention *** phase 2,based on the predicted energy consumption,we designed a smart scheduler with attention-induced Genetic Algorithm(GA)to schedule the appliances to reduce energy *** phase 3,a dynamic Feed-in Tariff(dFIT)algorithm makes real-time tariff adjustments using LSTM for demand prediction and SHapley Additive exPlanations(SHAP)values to improve model *** phase 4,the energy saved from solar systems and smart scheduling is shared with the community ***,in phase 5,SDP security ensures the integrity and confidentiality of shared energy *** evaluate the performance of energy sharing and scheduling for houses with and without solar support,we simulated the above phases using data obtained from the energy consumption of 17 household appliances in our IoT ***,the simulation results show that the proposed scheme reduces energy consumption and ensures secure and efficient distribution with peers,promoting a more sustainable energy management and res