Forecasting Portugal global load with artificial neural networks

被引:0
|
作者
Fidalgo, J. Nuno [1 ]
Matos, Manuel A.
机构
[1] Univ Porto, INESC Porto, Power Syst Unit, Oporto, Portugal
来源
ARTIFICIAL NEURAL NETWORKS - ICANN 2007, PT 2, PROCEEDINGS | 2007年 / 4669卷
关键词
artificial neural networks; load forecasting;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper describes a research where the main goal was to predict the future values of a time series of the hourly demand of Portugal global electricity consumption in the following day. In a preliminary phase several regression techniques were experimented: K Nearest Neighbors, Multiple Linear Regression, Projection Pursuit Regression, Regression Trees, Multivariate Adaptive Regression Splines and Artificial Neural Networks (ANN). Having the best results been achieved with ANN, this technique was selected as the primary tool for the load forecasting process. The prediction for holidays and days following holidays is analyzed and dealt with. Temperature significance on consumption level is also studied. Results attained support the adopted approach.
引用
收藏
页码:728 / +
页数:3
相关论文
共 50 条
  • [41] Use of Artificial Neural Networks for GHI Forecasting
    Carmo, Naiara Rinco de Marques e
    REVISTA VIRTUAL DE QUIMICA, 2022, 14 (01) : 56 - 60
  • [42] Electric power load forecasting on a 33/11 kV substation using artificial neural networks
    Veeramsetty, Venkataramana
    Deshmukh, Ram
    SN APPLIED SCIENCES, 2020, 2 (05):
  • [43] Middle anatolian region short-term load forecasting using artificial neural networks
    Demiroren, A
    Ceylan, G
    ELECTRIC POWER COMPONENTS AND SYSTEMS, 2006, 34 (06) : 707 - 724
  • [44] Short-term load forecasting in an autonomous power system using artificial neural networks
    Kiartzis, SJ
    Zoumas, CE
    Theocharis, JB
    Bakirtzis, AG
    Petridis, V
    IEEE TRANSACTIONS ON POWER SYSTEMS, 1997, 12 (04) : 1591 - 1596
  • [45] Electric power load forecasting on a 33/11 kV substation using artificial neural networks
    Venkataramana Veeramsetty
    Ram Deshmukh
    SN Applied Sciences, 2020, 2
  • [46] Application and comparison of several artificial neural networks for forecasting the Hellenic daily electricity demand load
    Ekonomou, L.
    Oikonomou, D. S.
    ADVANCES ON ARTIFICIAL INTELLIGENCE, KNOWLEDGE ENGINEERING AND DATA BASES, PROCEEDINGS, 2008, : 67 - +
  • [47] Short-Term Load Forecasting using Artificial Neural Networks and Multiple Linear Regression
    Govender, Sahil
    Folly, Komla A.
    2019 IEEE PES/IAS POWERAFRICA, 2019, : 273 - 278
  • [48] Artificial neural networks as applied to long-term demand forecasting
    Al-Saba, T
    El-Amin, I
    ARTIFICIAL INTELLIGENCE IN ENGINEERING, 1999, 13 (02): : 189 - 197
  • [49] A Global Modeling Framework for Load Forecasting in Distribution Networks
    Grabner, Miha
    Wang, Yi
    Wen, Qingsong
    Blazic, Bostjan
    Struc, Vitomir
    IEEE TRANSACTIONS ON SMART GRID, 2023, 14 (06) : 4927 - 4941
  • [50] Bayesian Neural Networks for Short Term Load Forecasting
    Shi, Hui-Feng
    Lu, Yan-Xia
    PROCEEDINGS OF 2009 INTERNATIONAL CONFERENCE ON WAVELET ANALYSIS AND PATTERN RECOGNITION, 2009, : 160 - 165