Demand forecasting in a power system

被引:11
|
作者
Tripathy, SC
机构
[1] Centre for Energy Studies, Indian Institute of Technology, New Delhi 110 016, Hauz Khas
关键词
demand; forecasting; planning; conservation; energy;
D O I
10.1016/S0196-8904(96)00101-X
中图分类号
O414.1 [热力学];
学科分类号
摘要
This paper is based on the results and experiences gained in the preparation of the report on Projections for Electrical Energy Consumption up to 2006-07 (using Econometric Modelling Techniques) released by the Central Electricity Authority (CEA), Government of India, New Delhi, in April 1992. The constraints felt while building the study under presentation have been appropriately highlighted in the step by step treatment of the long-term load forecasting exercise discussed in the succeeding paragraphs. It is desirable, as well as convenient, to assess and plan energy requirements in totality, inter alia, incorporating the likely demand for coal, oil, gas, etc. besides electricity. Since the CEA is engaged in planning and promoting development of the country's power sector, the study was limited to assessing the demand of electricity only in the foreseeable future. This paper studies the demand forecast in respect of utilities and does not cover the demand of units which will be met by their own captive power plants. (C) 1997 Elsevier Science Ltd.
引用
收藏
页码:1475 / 1481
页数:7
相关论文
共 50 条
  • [41] Incremental Adaptive Time Series Prediction for Power Demand Forecasting
    Vrablecova, Petra
    Rozinajova, Viera
    Ezzeddine, Anna Bou
    [J]. DATA MINING AND BIG DATA, DMBD 2017, 2017, 10387 : 83 - 92
  • [42] Improved grey-based approach for power demand forecasting
    林佳木
    [J]. Journal of Chongqing University(English Edition), 2006, (04) : 229 - 234
  • [43] Power Demand Forecasting Using Stochastic Model: Parameter Estimation
    Ma, Ruihong
    Wu, Rentao
    Khanwala, Mustafa A.
    Li, Dan
    Dang, Shuping
    [J]. 2015 MODERN ELECTRIC POWER SYSTEMS (MEPS), 2015,
  • [44] A NEW APPROACH TO STATISTICAL FORECASTING OF DAILY PEAK POWER DEMAND
    GOH, TN
    ONG, HL
    LEE, YO
    [J]. ELECTRIC POWER SYSTEMS RESEARCH, 1986, 10 (02) : 145 - 148
  • [45] An Integrated Approach to Forecasting Intermittent Demand for Electric Power Materials
    Aiping Jiang
    Qiuguo Chi
    Junjun Gao
    Maoguo Wu
    [J]. Computational Economics, 2019, 53 : 1309 - 1335
  • [46] Demand of Electric Power and Its Forecasting in Iron and Steel Complex
    Dian-min Zhou
    Feng Gao
    Wei Qiao
    [J]. Journal of Iron and Steel Research International, 2006, 13 : 21 - 24
  • [47] Distributed Demand Side Management with Stochastic Wind Power Forecasting
    Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari
    70125, Italy
    不详
    CD 2628, Netherlands
    [J]. IEEE Trans Control Syst Technol, 2022, 1 (97-112):
  • [48] Demand Power Forecasting with Data Mining Method in Smart Grid
    Park, Seunghyeon
    Han, Sekyung
    Son, Yeongik
    [J]. 2017 IEEE INNOVATIVE SMART GRID TECHNOLOGIES - ASIA (ISGT-ASIA), 2017, : 626 - 631
  • [49] Demand of electric power and its forecasting in iron and steel complex
    Zhou Dian-min
    Gao Feng
    Qiao Wei
    [J]. JOURNAL OF IRON AND STEEL RESEARCH INTERNATIONAL, 2006, 13 (05) : 21 - 24
  • [50] Demand of Electric Power and Its Forecasting in Iron and Steel Complex
    ZHOU Dian-min~1
    2. Baosteel Branch Energy Department
    [J]. Journal of Iron and Steel Research(International), 2006, (05) : 21 - 24