A hybrid data mining driven algorithm for long term electric peak load and energy demand forecasting

被引:104
作者
Kazemzadeh, Mohammad-Rasool [1 ]
Amjadian, Ali [1 ]
Amraee, Turaj [1 ]
机构
[1] KN Toosi Univ Technol, Dept Elect Engn, Tehran, Iran
关键词
Yearly peak load forecasting; Yearly energy demand forecasting; Hybrid method; Time series; Support vector regression; Particle swarm optimization; CONVOLUTIONAL NEURAL-NETWORKS; PARTICLE SWARM OPTIMIZATION; SUPPORT VECTOR MACHINES; MODEL; CONSUMPTION; REGRESSION; PREDICTION;
D O I
10.1016/j.energy.2020.117948
中图分类号
O414.1 [热力学];
学科分类号
摘要
Load forecasting is one of the main required studies for power system expansion planning and operation. In order to capture the nonlinear and complex pattern in yearly peak load and energy demand data, a hybrid long term forecasting method based on data mining technique and Time Series is proposed. First, a forecasting algorithm based on the Support Vector Regression (SVR) method is developed. The parameters of the SVR technique along with the dimension of input samples are optimized using a Particle Swarm Optimization (PSO) method. Secondly, in order to minimize the forecasting error, a hybrid forecasting method is presented for long term yearly electric peak load and total electric energy demand. The proposed hybrid method acts based on the combination of Auto-Regressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) and the proposed Support Vector Regression technique. The parameters of the ARIMA method are determined based on the autocorrelation and partial autocorrelation of the original and differenced time series. The proposed hybrid forecasting method prioritizes each forecasting method based on the resulted error over the existing data. The hybrid forecasting method is used to forecast the yearly peak load and total energy demand of Iran National Electric Energy System. (C) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页数:20
相关论文
共 44 条
[1]   Medium-term electric load forecasting using singular value decomposition [J].
Abu-Shikhah, Nazih ;
Elkarmi, Fawwaz .
ENERGY, 2011, 36 (07) :4259-4271
[2]   Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment [J].
Ahmad, Tanveer ;
Chen, Huanxin .
ENERGY, 2018, 160 :1008-1020
[3]   Two-phase particle swarm optimized-support vector regression hybrid model integrated with improved empirical mode decomposition with adaptive noise for multiple-horizon electricity demand forecasting [J].
AL-Musaylh, Mohanad S. ;
Deo, Ravinesh C. ;
Li, Yan ;
Adamowski, Jan F. .
APPLIED ENERGY, 2018, 217 :422-439
[4]  
Al-Shamma Basil R Gosselin, 2018, SPE EUR FEAT 80 EAGE
[5]   Long term electric load forecasting based on particle swarm optimization [J].
AlRashidi, M. R. ;
El-Naggar, K. M. .
APPLIED ENERGY, 2010, 87 (01) :320-326
[6]   Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types [J].
Ardakani, F. J. ;
Ardehali, M. M. .
ENERGY, 2014, 65 :452-461
[7]   A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran [J].
Azadeh, A. ;
Ghaderi, S. F. ;
Sohrabkhani, S. .
ENERGY POLICY, 2008, 36 (07) :2637-2644
[8]   Integration of artificial neural networks and genetic algorithm to predict electrical energy consumption [J].
Azadeh, A. ;
Ghaderi, S. F. ;
Tarverdian, S. ;
Saberi, M. .
APPLIED MATHEMATICS AND COMPUTATION, 2007, 186 (02) :1731-1741
[9]   Season specific approach for short-term load forecasting based on hybrid FA-SVM and similarity concept [J].
Barman, Mayur ;
Choudhury, Nalin Behari Dev .
ENERGY, 2019, 174 :886-896
[10]   Deep learning framework to forecast electricity demand [J].
Bedi, Jatin ;
Toshniwal, Durga .
APPLIED ENERGY, 2019, 238 :1312-1326