Electric power load forecasting on a 33/11 kV substation using artificial neural networks

被引：26

作者：

Veeramsetty, Venkataramana ^{[1
]}

Deshmukh, Ram ^{[1
]}

机构：

[1] SR Engn Coll, Ctr Artificial Intelligence & Deep Learning, Dept Elect & Elect Engn, Warangal, Telangana, India

来源：

SN APPLIED SCIENCES | 2020年 / 2卷 / 05期

关键词：

Artificial neural networks; Electric power load forecasting; Machine learning; Mean square error; Mean absolute percentage error; MODEL;

D O I：

10.1007/s42452-020-2601-y

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Estimation of electric power load on electric power substation is an essential task for system operator in order to operate the system in a reliable and optimal manner. In this paper, machine learning with artificial neural network is used for forecasting the load at a particular hour of the day on an electric power substation. Historical load data at each hour of the day for the period from September-2018 to November-2018 is taken from 33/11 kV substation near Kakatiya University in Warangal. A new artificial neural network architecture is developed based on the approach used to forecast the load. The developed model is simulated in MATLAB with available historical data to forecast the load on 33/11 kV electric power substation. Based on the analysis it is observed that the proposed architecture forecasts the load with better accuracy.

引用

页数：10

共 27 条

[21] Forecasting municipal solid waste generation using artificial intelligence models-a case study in India [J].

Soni, Umang ;

Roy, Akashdeep ;

Verma, Ayush ;

Jain, Vipul .

SN APPLIED SCIENCES, 2019, 1 (02)

[22] Recent Trends in Load Forecasting Technology for the Operation Optimization of Distributed Energy System [J].

Su, Pengwei ;

Tian, Xue ;

Wang, Yan ;

Deng, Shuai ;

Zhao, Jun ;

An, Qingsong ;

Wang, Yongzhen .

ENERGIES, 2017, 10 (09)

[23]

Temurtas F, 2004, LECT NOTES COMPUT SC, V3046, P389

[24] Comparative study on the effects of meteorological and pollutant parameters on ANN modelling for prediction of SO2 [J].

Unnikrishnan, Reshma ;

Madhu, G. .

SN APPLIED SCIENCES, 2019, 1 (11)

[25] Proportional nucleolus game theory-based locational marginal price computation for loss and emission reduction in a radial distribution system [J].

Veeramsetty, Venkataramana ;

Chintham, Venkaiah ;

Kumar, D. M. Vinod .

INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS, 2018, 28 (08)

[26] LMP computation at DG buses in radial distribution system [J].

Veeramsetty, Venkataramana ;

Chintham, Venkaiah ;

Kumar, Vinod D. M. .

INTERNATIONAL JOURNAL OF ENERGY SECTOR MANAGEMENT, 2018, 12 (03) :364-385

[27] Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods [J].

Yang, Zhang ;

Ce, Li ;

Lian, Li .

APPLIED ENERGY, 2017, 190 :291-305

← 1 2 3 →