Application of macromodeling method as bases for forecasting electrical consumption of multiflat houses

被引:0
|
作者
Lezhniuk, P.D. [1 ]
Bondarchuk, A.S. [2 ]
Hoholiuk, O.P. [3 ]
机构
[1] Vinnitsa National Technical University, Khmelnytske shosse, 95, Vinnytsia,21021, Ukraine
[2] Odessa National Polytechnic University, pr. Shevchenka, 1, Odesa,65044, Ukraine
[3] National University Lviv Polytechnic, str. S. Bandera, 12, Lviv,79013, Ukraine
来源
Technical Electrodynamics | 2019年 / 2019卷 / 06期
关键词
Electric utilities - Electric power utilization - Equivalent circuits;
D O I
10.15407/techned2019.06.074
中图分类号
TM7 [输配电工程、电力网及电力系统];
学科分类号
080802 ;
摘要
The application of the method of macromodelling is proposed for single-factor modeling and analysis of daily schedules of household electricity consumers, which creates the basis for their prediction at various time intervals. The advantage of the method is that it allows, with an accuracy sufficient for practical application, to create deterministic models of power consumption based on the initial information without the need for its preliminary processing. © Institute of Electrodynamics, National Academy of Sciences of Ukraine.
引用
收藏
页码:1 / 3
相关论文
共 50 条
  • [41] An empirical method for forecasting energy consumption in material extrusion
    Quarto, Mariangela
    [J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2023, 127 (5-6): : 2911 - 2920
  • [42] Electrical Load Consumption and Photovoltaic Power Forecasting using Deep CNN
    Dehghan, Fariba
    [J]. 2024 11TH IRANIAN CONFERENCE ON RENEWABLE ENERGY AND DISTRIBUTION GENERATION, ICREDG 2024, 2024,
  • [43] Hybrid intelligent strategy for multifactor influenced electrical energy consumption forecasting
    Heydari, Azim
    Garcia, Davide Astiaso
    Keynia, Farshid
    Bisegna, Fabio
    De Santoli, Livio
    [J]. ENERGY SOURCES PART B-ECONOMICS PLANNING AND POLICY, 2019, 14 (10-12) : 341 - 358
  • [44] Neural networks in forecasting electrical energy consumption: univariate and multivariate approaches
    Nasr, GE
    Badr, EA
    Younes, MR
    [J]. INTERNATIONAL JOURNAL OF ENERGY RESEARCH, 2002, 26 (01) : 67 - 78
  • [45] A review on applications of ANN and SVM for building electrical energy consumption forecasting
    Ahmad, A. S.
    Hassan, M. Y.
    Abdullah, M. P.
    Rahman, H. A.
    Hussin, F.
    Abdullah, H.
    Saidur, R.
    [J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2014, 33 : 102 - 109
  • [46] Forecasting electrical consumption by integration of Neural Network, time series and ANOVA
    Azadeh, A.
    Ghaderi, S. F.
    Sohrabkhani, S.
    [J]. APPLIED MATHEMATICS AND COMPUTATION, 2007, 186 (02) : 1753 - 1761
  • [48] APPLICATION OF SIMILARITY THEORY IN FORECASTING ELECTRICAL ENERGY GENERATION
    ASTAKHOV, YN
    ZUBANOV, KK
    KAVCHENKOV, VP
    PASHENKOVA, TY
    [J]. ELECTRICAL TECHNOLOGY, 1993, (01): : 139 - 154
  • [49] Application of the combination forecasting method in coal mine accidents forecasting
    Li, Jinsan
    Ning, Yuncai
    [J]. PROGRESS IN MINE SAFETY SCIENCE AND ENGINEERING II, 2014, : 653 - 657
  • [50] A HYBRID METHOD BASED ON SVM INTEGRATED IMPROVED PSO ALGORITHM FOR ELECTRICAL ENERGY CONSUMPTION FORECASTING OF CRUDE OIL PIPELINE
    Xu, Lei
    Hou, Lei
    Zhu, Zhenyu
    Li, Yu
    Lei, Ting
    [J]. PROCEEDINGS OF THE ASME 2020 13TH INTERNATIONAL PIPELINE CONFERENCE (IPC2020), VOL 3, 2020,