Modeling of electricity demand forecast for power system

The emerging complex circumstances caused by economy, technology, and government policy and the requirement of low-carbon development of power grid lead to many challenges in the power system coordination and operation. However, the real-time scheduling of electricity generation needs accurate modeling of electricity demand forecasting for a range of lead times. In order to better capture the nonlinear and non-stationary characteristics and the seasonal cycles of future electricity demand data, a new concept of the integrated model is developed and successfully applied to research the forecast of electricity demand in this paper. The proposed model combines adaptive Fourier decomposition method, a new signal preprocessing technology, for extracting useful element from the original electricity demand series through filtering the noise factors. Considering the seasonal term existing in the decomposed series, it should be eliminated through the seasonal adjustment method, in which the seasonal indexes are calculated and should multiply the forecasts back to restore the final forecast. Besides, a newly proposed moth-flame optimization algorithm is used to ensure the suitable parameters of the least square support vector machine which can generate the forecasts. Finally, the case studies of Australia demonstrated the efficacy and feasibility of the proposed integrated model. Simultaneously, it can provide a better concept of modeling for electricity demand prediction over different forecasting horizons.

[1]  Ponnuthurai N. Suganthan,et al.  Ensemble incremental learning Random Vector Functional Link network for short-term electric load forecasting , 2018, Knowl. Based Syst..

[2]  S. Fan,et al.  Short-term load forecasting based on an adaptive hybrid method , 2006, IEEE Transactions on Power Systems.

[3]  Mansour Sheikhan,et al.  Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data , 2012, Neural Computing and Applications.

[4]  Marie Bessec,et al.  Short-run electricity load forecasting with combinations of stationary wavelet transforms , 2018, Eur. J. Oper. Res..

[5]  C.D. Vournas,et al.  Unit Commitment by an Enhanced Simulated Annealing Algorithm , 2006, 2006 IEEE PES Power Systems Conference and Exposition.

[6]  Dipti Srinivasan,et al.  A SOM-based hybrid linear-neural model for short-term load forecasting , 2011, Neurocomputing.

[7]  Jianzhou Wang,et al.  A trend fixed on firstly and seasonal adjustment model combined with the ε-SVR for short-term forecasting of electricity demand , 2009 .

[8]  Jianzhou Wang,et al.  Combined forecasting models for wind energy forecasting: A case study in China , 2015 .

[9]  Bo Li,et al.  Slope stability analysis based on quantum-behaved particle swarm optimization and least squares support vector machine , 2015 .

[10]  Ping Jiang,et al.  Two combined forecasting models based on singular spectrum analysis and intelligent optimized algorithm for short-term wind speed , 2016, Neural Computing and Applications.

[11]  Seyed Mohammad Mirjalili,et al.  Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm , 2015, Knowl. Based Syst..

[12]  L. J. Soares,et al.  Forecasting electricity demand using generalized long memory , 2003 .

[13]  B. Vahidi,et al.  A Solution to the Unit Commitment Problem Using Imperialistic Competition Algorithm , 2012, IEEE Transactions on Power Systems.

[14]  刘峰,et al.  An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed , 2015 .

[15]  Yi-Ming Wei,et al.  Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology , 2013 .

[16]  Jian Wang,et al.  Short, medium and long term load forecasting model and virtual load forecaster based on radial basis function neural networks , 2010 .

[17]  Ali Akbar Abdoos,et al.  A new intelligent method based on combination of VMD and ELM for short term wind power forecasting , 2016, Neurocomputing.

[18]  Haiyan Lu,et al.  Chaotic time series method combined with particle swarm optimization and trend adjustment for electricity demand forecasting , 2011, Expert Syst. Appl..

[19]  Yanjun Huang,et al.  Model predictive control power management strategies for HEVs: A review , 2017 .

[20]  Changcun Wen,et al.  Agricultural environmental total factor productivity in China under technological heterogeneity: characteristics and determinants , 2018, Environmental Science and Pollution Research.

[21]  Li-Chih Ying,et al.  Using adaptive network based fuzzy inference system to forecast regional electricity loads , 2008 .

[22]  Wei-Chiang Hong,et al.  Application of chaotic ant swarm optimization in electric load forecasting , 2010 .

[23]  Frédéric Proïa,et al.  A SARIMAX coupled modelling applied to individual load curves intraday forecasting , 2012, 1207.0360.

[24]  Seyed Farid Ghaderi,et al.  Energy demand forecasting in Iranian metal industry using linear and nonlinear models based on evolutionary algorithms , 2012 .

[25]  Patrick Gaydecki,et al.  The use of the Hilbert transform in ECG signal analysis , 2001, Comput. Biol. Medicine.

[26]  Mohammed E. El-Telbany,et al.  Short-term forecasting of Jordanian electricity demand using particle swarm optimization , 2008 .

[27]  Bijaya K. Panigrahi,et al.  Solution to unit commitment in power system operation planning using binary coded modified moth flame optimization algorithm (BMMFOA): A flame selection based computational technique , 2017, J. Comput. Sci..

[28]  Arash Ghanbari,et al.  A Cooperative Ant Colony Optimization-Genetic Algorithm approach for construction of energy demand forecasting knowledge-based expert systems , 2013, Knowl. Based Syst..

[29]  Feng Wan,et al.  Adaptive Fourier decomposition based ECG denoising , 2016, Comput. Biol. Medicine.

[30]  Lambros Ekonomou,et al.  Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models , 2008 .

[31]  Tommy W. S. Chow,et al.  Object-Level Video Advertising: An Optimization Framework , 2017, IEEE Transactions on Industrial Informatics.

[32]  Helge V. Larsen,et al.  Long-term forecasting of hourly electricity load: Identification of consumption profiles and segmentation of customers , 2013 .

[33]  MirjaliliSeyedali.  Moth-flame optimization algorithm , 2015 .

[34]  Chia-Yon Chen,et al.  Regional load forecasting in Taiwanapplications of artificial neural networks , 2003 .

[35]  Chaoqing Yuan,et al.  Forecasting China’s energy demand and self-sufficiency rate by grey forecasting model and Markov model , 2015 .

[36]  Haiyan Lu,et al.  Combined modeling for electric load forecasting with adaptive particle swarm optimization , 2010 .

[37]  Feng Liu,et al.  A hybrid forecasting model based on date-framework strategy and improved feature selection technology for short-term load forecasting , 2017 .

[38]  Ping-Feng Pai,et al.  Solar power output forecasting using evolutionary seasonal decomposition least-square support vector regression , 2016 .

[39]  Haijun Zhang,et al.  Understanding Subtitles by Character-Level Sequence-to-Sequence Learning , 2017, IEEE Transactions on Industrial Informatics.

[40]  Aboul Ella Hassanien,et al.  An improved moth flame optimization algorithm based on rough sets for tomato diseases detection , 2017, Comput. Electron. Agric..

[41]  Saeed Shojaee,et al.  Hybridizing two-stage meta-heuristic optimization model with weighted least squares support vector machine for optimal shape of double-arch dams , 2015, Appl. Soft Comput..

[42]  Risto Lahdelma,et al.  Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system , 2016 .

[43]  Turan Paksoy,et al.  A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey , 2012 .

[44]  Guoqiang Peter Zhang,et al.  Neural network forecasting for seasonal and trend time series , 2005, Eur. J. Oper. Res..

[45]  Waree Kongprawechnon,et al.  Application of multiple tabu search algorithm to solve dynamic economic dispatch considering generator constraints , 2008 .

[46]  S. F. Ghaderi,et al.  Integration of Artificial Neural Networks and Genetic Algorithm to Predict Electrical Energy consumption , 2006, IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics.

[47]  Dalia Yousri,et al.  Parameters extraction of the three diode model for the multi-crystalline solar cell/module using Moth-Flame Optimization Algorithm , 2016 .

[48]  Mayur Barman,et al.  A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India , 2018 .

[49]  Mohammad Masoumi,et al.  Evolving Smart Model to Predict the Combustion Front Velocity for In Situ Combustion , 2015 .

[50]  Mohammad Hassan Moradi,et al.  Optimal siting and sizing of renewable energy sources and charging stations simultaneously based on Differential Evolution algorithm , 2015 .