Short‐term electricity load and price forecasting based on clustering and next symbol prediction

Short-term electricity load and price forecasting is an important issue in competitive electricity markets. In this paper, we propose a new direct time series forecasting method based on clustering and next symbol prediction. First, the cluster label sequence is obtained from time series clustering. Then a lossless compression algorithm of prediction by partial match version C coder (PPMC) is applied on this obtained discrete cluster label sequence to predict the next cluster label. Finally, the whole time series values of one-step-ahead can be directly forecast from the predicted cluster label. The proposed method is evaluated on electricity time series datasets, and the numerical experiments show that the proposed method can achieve promising results in day-ahead electricity load and price forecasting. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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

[2]  Christophe Croux,et al.  Robust Forecasting with Exponential and Holt-Winters Smoothing , 2007 .

[3]  Jianzhou Wang,et al.  Short-term electricity prices forecasting based on support vector regression and Auto-regressive integrated moving average modeling , 2010 .

[4]  Jinliang Zhang,et al.  Day-ahead electricity price forecasting by a new hybrid method , 2012, Comput. Ind. Eng..

[5]  Z. Tan,et al.  Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models , 2010 .

[6]  Alicia Troncoso Lora,et al.  Discovery of motifs to forecast outlier occurrence in time series , 2011, Pattern Recognit. Lett..

[7]  Ashwani Kumar,et al.  Electricity price forecasting in deregulated markets: A review and evaluation , 2009 .

[8]  Alistair Moffat,et al.  Implementing the PPM data compression scheme , 1990, IEEE Trans. Commun..

[9]  Francisco Martinez Alvarez,et al.  Energy Time Series Forecasting Based on Pattern Sequence Similarity , 2011, IEEE Transactions on Knowledge and Data Engineering.

[10]  Narayanan Kumarappan,et al.  Day-Ahead Deregulated Electricity Market Price Forecasting Using Recurrent Neural Network , 2013, IEEE Systems Journal.

[11]  Ran El-Yaniv,et al.  On Prediction Using Variable Order Markov Models , 2004, J. Artif. Intell. Res..

[12]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Farshid Keynia,et al.  Day-ahead electricity price forecasting by modified relief algorithm and hybrid neural network , 2010 .

[14]  Ying-Yi Hong,et al.  Day-Ahead Electricity Price Forecasting Using a Hybrid Principal Component Analysis Network , 2012 .

[15]  Marios M. Polycarpou,et al.  Short Term Electric Load Forecasting: A Tutorial , 2007, Trends in Neural Computation.

[16]  Farshid Keynia,et al.  A new cascade NN based method to short-term load forecast in deregulated electricity market , 2013 .

[17]  Farshid Keynia,et al.  Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm , 2009 .

[18]  V M F Mendes,et al.  Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Electricity Prices Forecasting , 2011, IEEE Transactions on Power Systems.

[19]  Alicia Troncoso Lora,et al.  Time-Series Prediction: Application to the Short-Term Electric Energy Demand , 2003, CAEPIA.

[20]  Cheng Hao Jin,et al.  Improved pattern sequence‐based forecasting method for electricity load , 2014 .

[21]  Ian H. Witten,et al.  Data Compression Using Adaptive Coding and Partial String Matching , 1984, IEEE Trans. Commun..

[22]  N. Kumarappan,et al.  Day-ahead deregulated electricity market price forecasting using neural network input featured by DCT , 2014 .

[23]  Cheng Hao Jin Non-member,et al.  Improved pattern sequence-based forecasting method for electricity load , 2014 .