Load Forecasting in Electrical Distribution Grid of Medium Voltage

被引:11
|
作者
Chemetova, Svetlana [1 ]
Santos, Paulo [1 ]
Ventim-Neves, Mario [2 ]
机构
[1] Polytech Inst Setubal, Dept Elect Engn ESTSetubal, Rua Vale Chaves Estefanilha, P-2910761 Setubal, Portugal
[2] Univ Nova Lisboa, Dept Elect Engn, Fac Sci & Technol Quinta Torre, P-2829516 Caparica, Portugal
关键词
Electric power systems; Load forecasting; Smart-grids; Distribution systems; Electric substations; Artificial Neural Networks; NEURAL-NETWORK; DISTRIBUTION-SYSTEMS; ALGORITHM; ANN;
D O I
10.1007/978-3-319-31165-4_33
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The importance of forecasting has become more evident with the appearance of the open electricity market and the restructuring of the national energy sector. This paper presents a new approach to load forecasting in the medium voltage distribution network in Portugal. The forecast horizon is short term, from 24 h up to a week. The forecast method is based on the combined use of a regression model and artificial neural networks (ANN). The study was done with the time series of telemetry data of the DSO (EDP Distribution) and climatic records from IPMA (Portuguese Institute of Sea and Atmosphere), applied for the urban area of Evora - one of the first Smart Cities in Portugal. The performance of the proposed methodology is illustrated by graphical results and evaluated with statistical indicators. The error (MAPE) was lower than 5 %, meaning that chosen methodology clearly validate the feasibility of the test.
引用
收藏
页码:340 / 349
页数:10
相关论文
共 50 条
  • [31] Smart distribution grid multistage expansion planning under load forecasting uncertainty
    Ravadanegh, Sajad Najafi
    Jahanyari, Nazanin
    Amini, Arman
    Taghizadeghan, Navid
    IET GENERATION TRANSMISSION & DISTRIBUTION, 2016, 10 (05) : 1136 - 1144
  • [32] Predictive models for short-term load forecasting in the UK's electrical grid
    Sha'aban, Yusuf A.
    PLOS ONE, 2024, 19 (04):
  • [33] When Weather Matters: IoT-Based Electrical Load Forecasting for Smart Grid
    Li, Liangzhi
    Ota, Kaoru
    Dong, Mianxiong
    IEEE COMMUNICATIONS MAGAZINE, 2017, 55 (10) : 46 - 51
  • [34] Wind and load forecasting for integration of wind power into a meso-scale electrical grid
    Fellows, A.
    Hill, D.
    European Community Wind Energy Conference, 1990,
  • [35] Forecasting Smart Grid Load on the Wire
    Xu, Jin
    Bhattacharyya, Shilpi
    Katramatos, Dimitrios
    Yoo, Shinjae
    Yue, Meng
    2018 NEW YORK SCIENTIFIC DATA SUMMIT (NYSDS), 2018,
  • [36] Optimization of Division and Reconfiguration Locations of the Medium-Voltage Power Grid Based on Forecasting the Level of Load and Generation from Renewable Energy Sources
    Sidor, Karol
    Miller, Piotr
    Malkowski, Robert
    Izdebski, Michal
    ENERGIES, 2024, 17 (19)
  • [37] Dynamic load modelling based on measurements in medium voltage distribution network
    Stojanovic, Dobrivoje P.
    Korunovic, Lidija M.
    Milanovic, J. V.
    ELECTRIC POWER SYSTEMS RESEARCH, 2008, 78 (02) : 228 - 238
  • [38] Load Forecasting Using Neural Networks and Blockchains for Low Voltage Distribution Networks
    Qaisieh, Lauren
    Tawalbeh, Nabeel
    2022 6TH INTERNATIONAL CONFERENCE ON GREEN ENERGY AND APPLICATIONS (ICGEA 2022), 2022, : 210 - 214
  • [39] Short term and medium term power distribution load forecasting by neural networks
    Yalcinoz, T
    Eminoglu, U
    ENERGY CONVERSION AND MANAGEMENT, 2005, 46 (9-10) : 1393 - 1405
  • [40] Statistical Error Analysis of Household Load Profile for Medium Voltage Grid State Estimation
    Xiang, Yu
    Cobben, J. F. G.
    Ribeiro, P. F.
    2014 POWER SYSTEMS COMPUTATION CONFERENCE (PSCC), 2014,