Short-Term Load Forecasting using Artificial Neural Networks and Multiple Linear Regression

被引：0
|
作者：
Govender, Sahil [1 ]
Folly, Komla A. [1 ]
机构：
[1] Univ Cape Town, Dept Elect Engn, Cape Town, South Africa
来源：
2019 IEEE PES/IAS POWERAFRICA | 2019年
关键词：
Artificial intelligence; artificial neural networks; multiple linear regression; short term load forecasting;
D O I：
10.1109/powerafrica.2019.8928857
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
In this paper, two methods for short-term load forecasting are compared; namely, artificial neural networks (ANNs) and multiple linear regression (MLR). Only input features that had a very large correlation with the load were used. Historic load data are shown to have the strongest correlation with the current load data than other weather variables such as temperature and humidity. Simulation results show that the MLR give better results for the seasonal forecasts, whereas the ANN showed an overall lower mean absolute percentage error (MAPE) for the daily forecasts.
引用
收藏
页码：273 / 278
页数：6
相关论文
共 50 条
  • [31] Application of improved artificial neural networks in short-term power load forecasting
    Wei, Sun
    Mohan, Liu
    [J]. JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY, 2015, 7 (04)
  • [32] Electric Short-Term Load Forecasting Using Artificial Neural Networks and Fuzzy Expert System
    Sun HeRu
    Wang Wei
    [J]. PROCEEDINGS OF THE 2011 INTERNATIONAL CONFERENCE ON INFORMATICS, CYBERNETICS, AND COMPUTER ENGINEERING (ICCE2011), VOL 3: COMPUTER NETWORKS AND ELECTRONIC ENGINEERING, 2011, 112 : 699 - +
  • [33] Short-term heat load forecasting in district heating systems using artificial neural networks
    Benalcazar, P.
    Kaminski, J.
    [J]. INTERNATIONAL CONFERENCE ON THE SUSTAINABLE ENERGY AND ENVIRONMENTAL DEVELOPMENT, 2019, 214
  • [34] Weather sensitive short-term load forecasting using artificial neural networks and time series
    Karaki, S.-H.
    [J]. International Journal of Power and Energy Systems, 1999, 19 (03): : 251 - 256
  • [35] Short-Term Load Forecasting Using Deep Neural Networks (DNN)
    Hossen, Tareq
    Plathottam, Siby Jose
    Angamuthu, Radha Krishnan
    Ranganathan, Prakash
    Salehfar, Hossein
    [J]. 2017 NORTH AMERICAN POWER SYMPOSIUM (NAPS), 2017,
  • [36] Residential Short-Term Load Forecasting Using Convolutional Neural Networks
    Voss, Marcus
    Bender-Saebelkampf, Christian
    Albayrak, Sahin
    [J]. 2018 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS, CONTROL, AND COMPUTING TECHNOLOGIES FOR SMART GRIDS (SMARTGRIDCOMM), 2018,
  • [37] Improved short-term load forecasting using bagged neural networks
    Khwaja, A. S.
    Naeem, M.
    Anpalagan, A.
    Venetsanopoulos, A.
    Venkatesh, B.
    [J]. ELECTRIC POWER SYSTEMS RESEARCH, 2015, 125 : 109 - 115
  • [38] Neural networks application in short-term load forecasting
    Tudose, Andrei
    Picioroaga, Irina
    Sidea, Dorian
    Bulac, Constantin
    [J]. UPB Scientific Bulletin, Series C: Electrical Engineering and Computer Science, 2021, 83 (02): : 231 - 240
  • [39] NEURAL NETWORKS APPLICATION IN SHORT-TERM LOAD FORECASTING
    Tudose, Andrei
    Picioroaga, Irina
    Sidea, Dorian
    Bulac, Constantin
    [J]. UNIVERSITY POLITEHNICA OF BUCHAREST SCIENTIFIC BULLETIN SERIES C-ELECTRICAL ENGINEERING AND COMPUTER SCIENCE, 2021, 83 (02): : 231 - 240
  • [40] Recurrent neural networks for short-term load forecasting
    Vermaak, J
    Botha, EC
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 1998, 13 (01) : 126 - 132
12345