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
  • [21] Short Term Electrical Load Forecasting for Mauritius using Artificial Neural Networks
    Bugwan, Tina
    King, Robert T. F. Ah
    2008 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC), VOLS 1-6, 2008, : 3667 - 3672
  • [22] Parametric analysis of parameters for electrical load forecasting using artificial neural networks
    Gerber, WJ
    Gonzalez, AJ
    Georgiopoulos, M
    APPLICATIONS AND SCIENCE OF ARTIFICIAL NEURAL NETWORKS III, 1997, 3077 : 72 - 83
  • [23] Short term load forecasting using artificial neural networks for the west of Iran
    Department of Electrical Engineering, Faculty of Engineering, Razi University, Tagh-e-Bostan, Kermanshah-67149, Iran
    J. Appl. Sci., 2007, 12 (1582-1588): : 1582 - 1588
  • [24] Optimization of power system load forecasting and scheduling based on artificial neural networks
    Jiangbo Jing
    Hongyu Di
    Ting Wang
    Ning Jiang
    Zhaoyang Xiang
    Energy Informatics, 8 (1)
  • [25] A Study of Load Demand Forecasting Models in Electricity Using Artificial Neural Networks and Fuzzy Logic Model
    Al-ani, B. R. K.
    Erkan, E. T.
    INTERNATIONAL JOURNAL OF ENGINEERING, 2022, 35 (06): : 1 - 8
  • [26] Optimizing Load Forecasting Configurations of Computational Neural Networks
    Al-Roomi, Ali R.
    El-Hawary, Mohamed E.
    2018 IEEE CANADIAN CONFERENCE ON ELECTRICAL & COMPUTER ENGINEERING (CCECE), 2018,
  • [27] Load Forecasting based on Neural Networks and Load Profiling
    Sousa, J. C.
    Neves, L. P.
    Jorge, H. M.
    2009 IEEE BUCHAREST POWERTECH, VOLS 1-5, 2009, : 960 - +
  • [28] Grouping Model Application on Artificial Neural Networks for Short-term Load Forecasting
    Zhang, Shunhua
    Lian, Jingjing
    Zhao, Zhiying
    Xu, Huijun
    Liu, Jing
    2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23, 2008, : 6203 - 6206
  • [29] Forecasting diurnal cooling energy load for institutional buildings using Artificial Neural Networks
    Deb, Chirag
    Eang, Lee Siew
    Yang, Junjing
    Santamouris, Mattheos
    ENERGY AND BUILDINGS, 2016, 121 : 284 - 297
  • [30] Application of bacterial foraging technique trained artificial and wavelet neural networks in load forecasting
    Ulagammai, M.
    Venkatesh, P.
    Kannan, P. S.
    Padhy, Narayana Prasad
    NEUROCOMPUTING, 2007, 70 (16-18) : 2659 - 2667