Short Term Load Forecasting of Indian System Using Linear Regression and Artificial Neural Network

被引：0
|
作者：
Patel, Harsh [1 ]
Pandya, Mahesh [1 ]
Aware, Mohan [2 ]
机构：
[1] Lukhadhirj Engn Coll, Dept Elect Engn, Morbi, India
[2] Visvesvaraya Natl Inst Technol, Dept Elect Engn, Nagpur, Maharashtra, India
来源：
2015 5TH NIRMA UNIVERSITY INTERNATIONAL CONFERENCE ON ENGINEERING (NUICONE) | 2015年
关键词：
Short Term Load Forecasting (STLF); Linear Regression (LR); Artificial Neural Network (ANN); Levenberg-Marquardt Back Propagation (LMBP); MAPE; MAE;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The hour ahead load forecasting is used for the reliable and proactive operation of the power system. The hour ahead load forecasting is a one type of Short Term Load Forecasting (STLF). The mostly STLF is used for the spinning reserve capacity, unit commitment and maintenance planning in the power system. In this paper the Linear Regression (LR) and the Artificial Neural Network (ANN) are used to study the STLF. In the ANN feed forward network is used for the hourly load forecasting. One fast training algorithm the Levenberg-Marquardt Back Propagation (LMBP) is used to train the neural network. The neuron model is trained using the historical load data of Indian distribution system. The sensitivity of the weather data for the STLF is verified. Both the techniques the LR and the ANN are compared according to the Mean Absolute Error (MAE) and the Mean Absolute Percentage Error (MAPE). The accuracy of the ANN technique for the STLF with the weather data is proved for the residential and the industrial feeder.
引用
收藏
页数：5
相关论文
共 50 条
  • [21] Research on a genetic neural artificial network in short term load forecasting
    Wang Luchao
    Deng Yongping
    ICCSE'2006: Proceedings of the First International Conference on Computer Science & Education: ADVANCED COMPUTER TECHNOLOGY, NEW EDUCATION, 2006, : 823 - 825
  • [22] Study of Artificial Neural Network Based Short Term Load Forecasting
    Webberley, Ashton
    Gao, David Wenzhong
    2013 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING (PES), 2013,
  • [23] Short term load forecasting for Iran national power system using artificial neural network and fuzzy expert system
    Ansarimehr, P
    Barghinia, S
    Habibi, H
    Vafadar, N
    POWERCON 2002: INTERNATIONAL CONFERENCE ON POWER SYSTEM TECHNOLOGY, VOLS 1-4, PROCEEDINGS, 2002, : 1082 - 1085
  • [24] Short-Term Multinodal Load Forecasting Using a Modified General Regression Neural Network
    Nose-Filho, Kenji
    Plasencia Lotufo, Anna Diva
    Minussi, Carlos Roberto
    IEEE TRANSACTIONS ON POWER DELIVERY, 2011, 26 (04) : 2862 - 2869
  • [25] Short Term Load Forecasting Using ANN and Multiple Linear Regression
    Kumar, Sharad
    Mishra, Shashank
    Gupta, Shashank
    2016 SECOND INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE & COMMUNICATION TECHNOLOGY (CICT), 2016, : 184 - 186
  • [26] A REAL-TIME SHORT-TERM LOAD FORECASTING SYSTEM BY ARTIFICIAL NEURAL NETWORK
    陈钧
    吴捷
    华南理工大学学报(自然科学版), 1998, (11) : 144 - 149
  • [27] Short Term Load Forecasting by Using Neural Network Structure
    Mirhosseini, M.
    Marzband, M.
    Oloomi, M.
    ECTI-CON: 2009 6TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING/ELECTRONICS, COMPUTER, TELECOMMUNICATIONS AND INFORMATION TECHNOLOGY, VOLS 1 AND 2, 2009, : 219 - +
  • [28] Short term load forecasting using a hybrid neural network
    Yap, Keem Siah
    Abidin, Izham Zainal
    Lim, Chee Peng
    Shah, Mohd Suhairi
    First International Power & Energy Conference (PECon 2006), Proceedings, 2006, : 123 - 128
  • [29] Short Term Wind Forecasting Using Logistic Regression Driven Hypothesis in Artificial Neural Network
    Sreenivasa, Sheshnag Chitlur
    Agarwal, Saurabh Kumar
    Kumar, Rajesh
    2014 6th IEEE Power India International Conference (PIICON), 2014,
  • [30] Application of Generalized Regression Neural Network in Short-term Load Forecasting
    Bai, Guang-ya
    Li, Yong
    INTERNATIONAL CONFERENCE ON MATERIAL SCIENCE AND CIVIL ENGINEERING, MSCE 2016, 2016, : 424 - 430
12345