A new hybrid correction method for short-term load forecasting based on ARIMA, SVR and CSA

Accurate load-forecasting problem is a significant and vital issue, especially in the new competitive electricity market. The models that are employed for forecasting purposes would determine how reliable the last forecasted results are. Therefore, this paper proposes a new hybrid correction method based on autoregressive integrated moving average (ARIMA) model, support vector regression (SVR) and cuckoo search algorithm (CSA) to achieve a more reliable forecasting model. The proposed method gets use of the autocorrelation function (ACF) and the partial ACF to search the stationary or non-stationary behaviour of the investigated time series. In the case of non-stationary data, it will be differenced one or more times to become stationary. After that, Akaike information criterion is utilised to find the appropriate ARIMA model such that the linear component of the data would be captured. Therefore, the ARIMA residuals would contain the non-linear components that should be modelled by use of the SVR model. The role of CSA as a successful optimisation algorithm is to find the optimal SVR parameters for more accurate forecasting. Meanwhile, a novel self-adaptive modification method based on CSA is proposed to empower the total search ability of the algorithm effectively. The proposed method is applied to the empirical peak load data of Fars Electrical Power Company in Iran.

[1]  Kwang-Ho Kim,et al.  Implementation of hybrid short-term load forecasting system using artificial neural networks and fuzzy expert systems , 1995 .

[2]  Taher Niknam,et al.  Impact of thermal recovery and hydrogen production of fuel cell power plants on distribution feeder reconfiguration , 2012 .

[3]  Ping-Feng Pai,et al.  A hybrid ARIMA and support vector machines model in stock price forecasting , 2005 .

[4]  T. Hesterberg,et al.  A regression-based approach to short-term system load forecasting , 1989, Conference Papers Power Industry Computer Application Conference.

[5]  James V. Hansen,et al.  Time-series analysis with neural networks and ARIMA-neural network hybrids , 2003, J. Exp. Theor. Artif. Intell..

[6]  Abdollah Kavousi-Fard,et al.  Reliability enhancement using optimal distribution feeder reconfiguration , 2013, Neurocomputing.

[7]  Clifford T. Brown,et al.  Lévy Flights in Dobe Ju/’hoansi Foraging Patterns , 2007 .

[8]  Ping-Feng Pai,et al.  Software reliability forecasting by support vector machines with simulated annealing algorithms , 2006, J. Syst. Softw..

[9]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[10]  Jiejin Cai,et al.  Applying support vector machine to predict hourly cooling load in the building , 2009 .

[11]  Wei-Jen Lee,et al.  Short-Term Load Forecasting Using Comprehensive Combination Based on Multimeteorological Information , 2009, IEEE Transactions on Industry Applications.

[12]  Taher Niknam,et al.  A new modified teaching-learning algorithm for reserve constrained dynamic economic dispatch , 2013, IEEE Transactions on Power Systems.

[13]  C. N. Lu,et al.  A Data Mining Approach for Spatial Modeling in Small Area Load Forecast , 2002, IEEE Power Engineering Review.

[14]  Wei-Chiang Hong,et al.  Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model , 2009 .

[15]  Taher Niknam,et al.  Multi-Objective Stochastic Distribution Feeder Reconfiguration in Systems With Wind Power Generators and Fuel Cells Using the Point Estimate Method , 2013, IEEE Transactions on Power Systems.

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

[17]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[18]  Taher Niknam,et al.  Optimal operation management of fuel cell/wind/photovoltaic power sources connected to distribution networks , 2011 .

[19]  Taher Niknam,et al.  An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation , 2012 .

[20]  Sridhar Krishnan,et al.  Combining least-squares support vector machines for classification of biomedical signals: a case study with knee-joint vibroarthrographic signals , 2011, J. Exp. Theor. Artif. Intell..

[21]  James V. Hansen,et al.  Some experimental evidence on the performance of GA-designed neural networks , 2001, J. Exp. Theor. Artif. Intell..

[22]  Taher Niknam,et al.  An efficient hybrid evolutionary algorithm based on PSO and HBMO algorithms for multi-objective Distribution Feeder Reconfiguration , 2009 .

[23]  Taher Niknam,et al.  Multi-objective stochastic distribution feeder reconfiguration problem considering hydrogen and thermal energy production by fuel cell power plants , 2012 .

[24]  Tomonobu Senjyu,et al.  Next Day Peak Load Forecasting Using Neural Network With Adaptive Learning Algorithm Based On Similarity , 2000 .

[25]  Haidar Samet,et al.  Consideration effect of uncertainty in power system reliability indices using radial basis function network and fuzzy logic theory , 2011, Neurocomputing.

[26]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[27]  Sayan Mukherjee,et al.  Choosing Multiple Parameters for Support Vector Machines , 2002, Machine Learning.

[28]  Saeid Nahavandi,et al.  Construction of Optimal Prediction Intervals for Load Forecasting Problems , 2010, IEEE Transactions on Power Systems.

[29]  Wenjian Wang,et al.  Online prediction model based on support vector machine , 2008, Neurocomputing.

[30]  N. Amjady,et al.  Short-Term Bus Load Forecasting of Power Systems by a New Hybrid Method , 2007, IEEE Transactions on Power Systems.

[31]  Mohamed Mohandes,et al.  Support vector machines for wind speed prediction , 2004 .

[32]  Shyh-Jier Huang,et al.  Short-term load forecasting via ARMA model identification including non-Gaussian process considerations , 2003 .

[33]  Tomonobu Senjyu, Hirokazu Sakihara, Yoshinori Tamaki, Katsu.  Next Day Peak Load Forecasting Using Neural Network With Adaptive Learning Algorithm Based On Similarity , 2000 .

[34]  Taher Niknam,et al.  Distribution feeder reconfiguration considering fuel cell/wind/photovoltaic power plants , 2012 .

[35]  S. A. Soliman,et al.  Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model , 2004 .

[36]  Taher Niknam,et al.  Scenario-based multiobjective distribution feeder reconfiguration considering wind power using adaptive modified particle swarm optimisation , 2012 .

[37]  Ping-Feng Pai,et al.  Potential assessment of the support vector regression technique in rainfall forecasting , 2007 .