Evolutionary short term load forecasting based on ANN: A comparison with the most well-known algorithms

Precise prediction of load demand is necessary in power systems which can be used in electricity industry and market. Most of load forecasting methods are based on Artificial Neural Networks (ANNs). However ANN parameters need to be adjusted to have an accurate forecasting. For this purpose evolutionary algorithms have been used to optimize the ANN based forecasting. The main obstacle in the use of evolutionary algorithms for training ANNs is the problem of requiring so much time for the training process as well as the weak stability of the evolutionary-based training methods to adapt ANNs weighting factors. In this paper, first several forecasting methods such as AR, ARMA, ANN and SVR are evaluated using a real case study. Then in the second stage a complete comparison between some of the most well-known evolutionary algorithms such as; GA, PSO, HBMO, and MHBMO is done which are utilized to optimizes the ANN based load forecasting. A real case study is investigated and the simulation results are described, precisely.

[1]  M. Jaidane-Saidane,et al.  Variability of Predictability of the Daily Peak Load Using Lyapunov Exponent Approach : Case of Tunisian Power System , 2007, 2007 IEEE Lausanne Power Tech.

[2]  Barry J. Adams,et al.  Honey-bee mating optimization (HBMO) algorithm for optimal reservoir operation , 2007, J. Frankl. Inst..

[3]  Saifur Rahman,et al.  Identification of a standard for comparing short-term load forecasting techniques , 1992 .

[4]  Yinghua Lin,et al.  A new approach to fuzzy-neural system modeling , 1995, IEEE Trans. Fuzzy Syst..

[5]  Zhou Quan,et al.  RBF Neural Network and ANFIS-Based Short-Term Load Forecasting Approach in Real-Time Price Environment , 2008, IEEE Transactions on Power Systems.

[6]  Ying Chen,et al.  Short-Term Load Forecasting: Similar Day-Based Wavelet Neural Networks , 2010, IEEE Transactions on Power Systems.

[7]  Wenyu Zhang,et al.  Chaotic Time Series Forecasting Base on Fuzzy Adaptive PSO for Feedforward Neural Network Training , 2008, 2008 The 9th International Conference for Young Computer Scientists.

[8]  Masahito Yamada,et al.  Structural Time Series Models and the Kalman Filter , 1989 .

[9]  Wenyu Zhang,et al.  Prediction by Integration of Phase Space Reconstruction and a Novel Evolutionary System under Deregulated Power Market , 2009, 2009 Second International Workshop on Knowledge Discovery and Data Mining.

[10]  Zun-Xiong Liu,et al.  Chaotic Load Series Forecasting Based on MPMR , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[11]  Haidar Samet,et al.  Power system load forecasting based on MHBMO algorithm and neural network , 2011, 2011 19th Iranian Conference on Electrical Engineering.

[12]  N Amjady,et al.  Midterm Demand Prediction of Electrical Power Systems Using a New Hybrid Forecast Technique , 2011, IEEE Transactions on Power Systems.

[13]  Ali Maroosi,et al.  Application of honey-bee mating optimization algorithm on clustering , 2007, Appl. Math. Comput..

[14]  R. Buizza,et al.  Neural Network Load Forecasting with Weather Ensemble Predictions , 2002, IEEE Power Engineering Review.

[15]  Ehsan Yavari,et al.  Recurrent Wavelet Network with New Initialization and its Application on Short-Term Load Forecasting , 2009, 2009 Third UKSim European Symposium on Computer Modeling and Simulation.

[16]  Caiqing Zhang,et al.  Short-Term Electricity Price Forecasting Based on PSO Algorithm and RBF Neural Network Algorithm , 2010, 2010 International Conference on Measuring Technology and Mechatronics Automation.

[17]  Arash Ghanbari,et al.  Prediction of electrical energy demand by hybridization of Particle Swarm Optimization and Noise filtering , 2010, 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE).

[18]  Li Yan,et al.  Hybrid Genetic Algorithm and Support Vector Regression in Cooling Load Prediction , 2010, 2010 Third International Conference on Knowledge Discovery and Data Mining.

[19]  Zongheng Jiang.  Short - term load forecasting of power system , 2017 .

[20]  C. Cañizares,et al.  ANN-based short-term load forecasting in electricity markets , 2001, 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194).

[21]  Wei Liu,et al.  Study on Power System Load Forecasting Based on MPSO Artificial Neural Networks , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[22]  David S. Stoffer,et al.  Time series analysis and its applications , 2000 .