Smart home energy management systems: Research challenges and survey

被引:29
作者
Raza, Ali [1 ]
Li, Jingzhao [1 ]
Ghadi, Yazeed [2 ]
Adnan, Muhammad [3 ]
Ali, Mansoor [4 ]
机构
[1] Anhui Univ Sci & Technol AUST, Sch Elect & Informat Engn, Huainan, Peoples R China
[2] Al Ain Univ, Dept Software Engn, Al Ain, U Arab Emirates
[3] Natl Univ Comp & Emerging Sci FAST, Dept Elect Engn, Chiniot Faisalabad Campus, Chiniot, Pakistan
[4] Ecole Technol Super, Elect Engn Dept, Montreal, PQ, Canada
关键词
Home Energy Management System; Load forecasting; Load scheduling; Environment; Sustainability; Smart Grid; Demand response; Home appliances; Optimization; Renewable Energy Resource; RESIDENTIAL DEMAND RESPONSE; ARTIFICIAL NEURAL-NETWORK; REWEIGHTED LEAST-SQUARES; SUPPORT VECTOR MACHINES; FUZZY INFERENCE SYSTEM; RENEWABLE ENERGY; MOVING-AVERAGE; MULTIOBJECTIVE OPTIMIZATION; ELECTRICITY CONSUMPTION; HOUSEHOLD APPLIANCES;
D O I
10.1016/j.aej.2024.02.033
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Electricity is establishing ground as a means of energy, and its proportion will continue to rise in the next generations. Home energy usage is expected to increase by more than 40% in the next 20 years. Therefore, to compensate for demand requirements, proper planning and strategies are needed to improve home energy management systems (HEMs). One of the crucial aspects of HEMS are proper load forecasting and scheduling of energy utilization. Energy management systems depend heavily on precise forecasting and scheduling. Considering this scenario, this article was divided into two parts. Firstly, this article gives a thorough analysis of forecasting models in HEMs with the primary goal of determining whichever model is most appropriate in a given situation. Moreover, for optimal utilization of scheduling strategies in HEMs, the current literature has discussed a number of scheduling optimization approaches. Therefore, secondly in this article, these approaches will be examined thoroughly to develop effective operating scheduling and to make wise judgments regarding usage of these techniques in HEMs. Finally, this paper also presents the future technical advancements and research gaps in load forecasting and scheduling and how they affect HEMs activities in the near future.
引用
收藏
页码:117 / 170
页数:54
相关论文
共 583 条
  • [1] A Novel Dynamic Load Scheduling and Peak Shaving Control Scheme in Community Home Energy Management System Based Microgrids
    Abbasi, Ayesha
    Khalid, Hassan Abdullah
    Rehman, Habibur
    Khan, Adnan Umar
    [J]. IEEE ACCESS, 2023, 11 : 32508 - 32522
  • [2] Solving the Multivariant EV Routing Problem Incorporating V2G and G2V Options
    Abdulaal, Ahmed
    Cintuglu, Mehmet H.
    Asfour, Shihab
    Mohammed, Osama A.
    [J]. IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, 2017, 3 (01): : 238 - 248
  • [3] Adnan M., 2019, P 2019 15 INT C EM T, P8138, DOI DOI 10.1109/ICET48972.2019.8994461
  • [4] Adnan M, 2015, Int J Sci Eng Res, P6
  • [5] Adnan M.M., 2023, IEEE Access
  • [6] Cascading Failures Assessment in Renewable Integrated Power Grids Under Multiple Faults Contingencies
    Adnan, Muhammad
    Khan, Muhammad Gufran
    Amin, Arslan Ahmed
    Fazal, Muhammad Rayyan
    Tan, Wen-Shan
    Ali, Mansoor
    [J]. IEEE ACCESS, 2021, 9 : 82272 - 82287
  • [7] Cascading overload failure analysis in renewable integrated power grids
    Adnan, Muhammad
    Tariq, Muhammad
    [J]. RELIABILITY ENGINEERING & SYSTEM SAFETY, 2020, 198
  • [8] Preventing Cascading Failure Through Fuzzy Co-Operative Control Mechanism Using V2G
    Adnan, Muhammad
    Ali, Mansoor
    Basalamah, Anas
    Tariq, Muhammad
    [J]. IEEE ACCESS, 2019, 7 : 142607 - 142622
  • [9] Load flow balancing and transient stability analysis in renewable integrated power grids
    Adnan, Muhammad
    Tariq, Muhammad
    Zhou, Zhenyu
    Poor, H. Vincent
    [J]. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2019, 104 : 744 - 771
  • [10] Convergence of Iteratively Re-weighted Least Squares to Robust M-estimators
    Aftab, Khurrum
    Hartley, Richard
    [J]. 2015 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV), 2015, : 480 - 487