Long-term load forecasting using system type neural network architecture

被引：0
|
作者：
Hobbs, Nathaniel J. [1 ]
Kim, Byoung H. [1 ]
Lee, Kwang Y. [2 ]
机构：
[1] Penn State Univ, Dept Elect Engn, University Pk, PA 16802 USA
[2] Baylor Univ, Dept Elect & Comp Engn, Waco, TX 76798 USA
来源：
2007 INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS APPLICATIONS TO POWER SYSTEMS, VOLS 1 AND 2 | 2007年
基金：
美国国家科学基金会;
关键词：
decomposition; load forecasting; neural network; system-type architecture;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
This paper presents a methodology for long-term electric power demands using a semigroup based system-type neural network architecture. The assumption is that given enough data, the next year's loads can be predicted using only components from the previous few years. This methodology is applied to recent load data, and the next year's load data is satisfactorily forecasted. This method also provides a more in depth forecasted time interval than other methods that just predict the average or peak power demand in the interval.
引用
收藏
页码：435 / +
页数：3
相关论文
共 50 条
  • [41] Short term load forecasting using neural network with rough set
    Xiao, Zhi
    Ye, Shi-Jie
    Zhong, Bo
    Sun, Cai-Xin
    ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 2, PROCEEDINGS, 2006, 3972 : 1259 - 1268
  • [42] Short- and Long-term Electricity Load Forecasting Using Classical and Neural Network Based Approach: A Case Study for the Philippines
    Bantugon, Mary Joyce T.
    Gallano, Russel John C.
    PROCEEDINGS OF THE 2016 IEEE REGION 10 CONFERENCE (TENCON), 2016, : 3822 - 3825
  • [43] Implementing Very-Short-Term Forecasting of Residential Load Demand Using a Deep Neural Network Architecture
    Gonzalez, Reynaldo
    Ahmed, Sara
    Alamaniotis, Miltiadis
    ENERGIES, 2023, 16 (09)
  • [44] Long term electric load forecasting based on TS-type recurrent fuzzy neural network model
    Wen, Ziteng
    Xie, Linbo
    Fan, Qigao
    Feng, Hongwei
    ELECTRIC POWER SYSTEMS RESEARCH, 2020, 179
  • [45] A hierarchical hybrid neural model with time integrators in long-term load forecasting
    Carpinteiro, Otavio A. S.
    Lima, Isaias
    Moreira, Edmilson M.
    Pinheiro, Carlos A. M.
    Seraphim, Enzo
    Pinto, J. Vantuil L.
    NEURAL COMPUTING & APPLICATIONS, 2009, 18 (08): : 1057 - 1063
  • [46] LDformer: a parallel neural network model for long-term power forecasting
    Tian, Ran
    Li, Xinmei
    Ma, Zhongyu
    Liu, Yanxing
    Wang, Jingxia
    Wang, Chu
    FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING, 2023, 24 (09) : 1287 - 1301
  • [47] Long-term load forecasting via a hierarchical neural model with time integrators
    Carpinteiro, Otavio A. S.
    Leme, Rafael C.
    de Souza, Antonio C. Zambroni
    Pinheiro, Carlos A. M.
    Moreira, Edmilson M.
    ELECTRIC POWER SYSTEMS RESEARCH, 2007, 77 (3-4) : 371 - 378
  • [48] A collaborative fuzzy-neural approach for long-term load forecasting in Taiwan
    Chen, Toly
    COMPUTERS & INDUSTRIAL ENGINEERING, 2012, 63 (03) : 663 - 670
  • [49] A hierarchical neural model with time windows in long-term electrical load forecasting
    Carpinteiro, Otavio A. S.
    Lima, Isaias
    Leme, Rafael C.
    de Souza, Antonio C. Zambroni
    Moreira, Edmilson M.
    Pinheiro, Carlos A. M.
    NEURAL COMPUTING & APPLICATIONS, 2007, 16 (4-5): : 465 - 470
  • [50] A hierarchical hybrid neural model with time integrators in long-term load forecasting
    Otávio A. S. Carpinteiro
    Isaías Lima
    Edmilson M. Moreira
    Carlos A. M. Pinheiro
    Enzo Seraphim
    J. Vantuil L. Pinto
    Neural Computing and Applications, 2009, 18 : 1057 - 1063
12345