In this modern world, human life has become increasingly reliant on electricity, which serves to be one of the basic needs of human to lead a normal life. As the usage of electricity has increased over the years, the consumers are very much concerned about its consumption rate and the electricity bill generated out of it. Hence monitoring the consumption rate stands first which is very trivial and challenging by designing an efficient energy management system. This paper outlines the survey on energy management systems implemented with artificial intelligence techniques towards finding a viable solution to the aforementioned issue. This study elucidates how artificial intelligent systems are incorporated in different energy management systems to match demand and supply. A comparative analysis of different intelligent techniques with optimization goals, issues and solutions, applied to the energy management systems for effective functioning is also presented. Finally, Home Energy Management System (HEMS) using Binary Particle Swarm Optimization Algorithm (BPSO) is presented. 26% reduction in the daily bill with optimization of HVAC and non-interruptible appliances was attained. Due to the interrupted supply of energy sources, effective storage model is determined to be an alternate viable option owing to technological advancement and capacity of ensuring excellent grid services. Future directions in terms of developing hybrid systems using hybrid energy sources and intelligent systems are also suggested.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Energy management system using binary particle swarm optimization technique



    Conference:

    5TH INTERNATIONAL CONFERENCE ON INNOVATIVE DESIGN, ANALYSIS & DEVELOPMENT PRACTICES IN AEROSPACE & AUTOMOTIVE ENGINEERING: I-DAD’22 ; 2022 ; Chennai, India


    Published in:

    Publication date :

    2023-06-07


    Size :

    10 pages





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    SAAB 340B Aerodynamic Model Development Using Binary Particle Swarm Optimization

    Millidere, Murat / Alam, Mushfiqul / Place, Simon et al. | AIAA | 2024




    Optimal Maneuvers Around Binary Asteroids Using Particle Swarm Optimization and Machine Learning

    D’Ambrosio, Andrea / Carbone, Andrea / Curti, Fabio | AIAA | 2023


    Particle Swarm Optimization

    Gerhard Venter / Jaroslaw Sobieszczanski-Sobieski | AIAA | 2003