Discrete cosine transform-based predictive model extended in the least-squares sense for hourly load forecasting

Electric load forecasting is essential to economic operation and power system control in the electric power industry. Modelling and forecasts for different time horizons are of increasingly importance for different operations. Discrete cosine transform (DCT) is one Fourier-related computational technique. It is widely used in signal and image processing for its remarkable characteristics on optimal decorrelation and energy compaction. From time-series analysis perspective, this study proposes a DCT-based predictive model for forecasting hourly load movement. Based on finite hourly load observations, the proposed DCT-based predictive model combines with the least-squares approach to get the optimum DCT coefficients, which are approximated in the least-squares sense. Then the obtained least-squares-optimum DCT coefficients are employed for forecast modelling to predict the load future movement. Experimental results and analysis show workability of the proposed forecast modelling. It is indicated that the DCT-based least-squares predictive model predicts hourly load movement most fitting with about 12-term DCT coefficients.

[1]  Silja Meyer-Nieberg,et al.  Electric load forecasting methods: Tools for decision making , 2009, Eur. J. Oper. Res..

[2]  Hoay Beng Gooi,et al.  Ultra-short-term multi-node load forecasting – a composite approach , 2012 .

[3]  Rui Zhang,et al.  Short-term load forecasting of Australian National Electricity Market by an ensemble model of extreme learning machine , 2013 .

[4]  L. Suganthi,et al.  Energy models for demand forecasting—A review , 2012 .

[5]  Yang Zong-chang,et al.  Fourier analysis-based air temperature movement analysis and forecast , 2013, IET Signal Process..

[6]  Jianzhou Wang,et al.  Short-term load forecasting using a kernel-based support vector regression combination model , 2014 .

[7]  J. E. Van Ness,et al.  Response of Large Power Systems to Cyclic Load Variations , 1966 .

[8]  R. Adapa,et al.  The impacts of temperature forecast uncertainty on Bayesian load forecasting , 1998 .

[9]  Cheng-Ting Lin,et al.  A novel economy reflecting short-term load forecasting approach , 2013 .

[10]  Sajad Najafi Ravadanegh,et al.  Probabilistic optimal robust multistage feeder routing under load forecasting uncertainty , 2015 .

[11]  Mohammad Shahidehpour,et al.  Hourly demand response in day-ahead scheduling for managing the variability of renewable energy , 2013 .

[12]  B. Hobbs,et al.  Analysis of the value for unit commitment of improved load forecasts , 1999 .

[13]  Turan Paksoy,et al.  Swarm intelligence approaches to estimate electricity energy demand in Turkey , 2012, Knowl. Based Syst..

[14]  Der-Chiang Li,et al.  Forecasting short-term electricity consumption using the adaptive grey-based approach—An Asian case , 2012 .

[15]  Zong-Chang Yang.  Modeling and forecasting monthly movement of annual average solar insolation based on the least-squares Fourier-model , 2014 .

[16]  Ricardo Menezes Salgado,et al.  A Hybrid Approach to the Load Forecasting Based on Decision Trees , 2013 .

[17]  Khalid Sayood,et al.  Use of residual redundancy in the design of joint source/channel coders , 1991, IEEE Trans. Commun..

[18]  L. Jenkins,et al.  Studies on power systems that are subjected to cyclic loads , 1988 .

[19]  Zong-chang Yang,et al.  Electric Load Movement Evaluation and Forecasting Based on the Fourier-Series Model Extend in the Least-Squares Sense , 2015 .

[20]  Hesham K. Alfares,et al.  Electric load forecasting: Literature survey and classification of methods , 2002, Int. J. Syst. Sci..

[21]  N. Rostamkolai,et al.  Evaluation of the impact of a large cyclic load on the LILCO power system using time simulation and frequency domain techniques , 1994 .

[22]  Patrik Söderholm,et al.  Industrial electricity demand and energy efficiency policy: the case of the Swedish mining industry , 2014 .

[23]  Mohammad Shahidehpour,et al.  A hybrid model for integrated day-ahead electricity price and load forecasting in smart grid , 2014 .