An Integrated Machine Learning Model for Day-Ahead Electricity Price Forecasting

This paper proposes a novel model for short-term electricity price forecasting based on an integration of two machine learning technologies: Bayesian clustering by dynamics (BCD) and support vector machine (SVM). The proposed forecasting system adopts an integrated architecture. Firstly, a BCD classifier is applied to cluster the input data set into several subsets in an unsupervised manner. Then, groups of 24 SVMs for the next day's electricity price profile are used to fit the training data of each subset in a supervised way. To demonstrate the effectiveness, the proposed model has been trained and tested on the data of the historical energy prices from the New England electricity market

[1]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[2]  P. Luh,et al.  Selecting input factors for clusters of Gaussian radial basis function networks to improve market clearing price prediction , 2003 .

[3]  J. Contreras,et al.  ARIMA Models to Predict Next-Day Electricity Prices , 2002, IEEE Power Engineering Review.

[4]  A.M. Gonzalez,et al.  Modeling and forecasting electricity prices with input/output hidden Markov models , 2005, IEEE Transactions on Power Systems.

[5]  Gunnar Rätsch,et al.  Predicting Time Series with Support Vector Machines , 1997, ICANN.

[6]  Shu Fan,et al.  Next-day electricity-price forecasting using a hybrid network , 2007 .

[7]  Paul R. Cohen,et al.  Bayesian Clustering by Dynamics Contents 1 Introduction 1 2 Clustering Markov Chains 2 , 2022 .

[8]  D. Bunn.  Modelling prices in competitive electricity markets , 2004 .

[9]  C. Rodriguez,et al.  Energy price forecasting in the Ontario competitive power system market , 2004, IEEE Transactions on Power Systems.

[10]  J. Contreras,et al.  Forecasting Next-Day Electricity Prices by Time Series Models , 2002, IEEE Power Engineering Review.

[11]  P. Luh,et al.  Improving market clearing price prediction by using a committee machine of neural networks , 2004, IEEE Transactions on Power Systems.

[12]  P.B. Luh,et al.  Neural network-based market clearing price prediction and confidence interval estimation with an improved extended Kalman filter method , 2005, IEEE Transactions on Power Systems.

[13]  A.J. Conejo,et al.  Day-ahead electricity price forecasting using the wavelet transform and ARIMA models , 2005, IEEE Transactions on Power Systems.

[14]  Marco van Akkeren,et al.  A GARCH forecasting model to predict day-ahead electricity prices , 2005, IEEE Transactions on Power Systems.

[15]  Ming-Wei Chang,et al.  Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001 , 2004, IEEE Transactions on Power Systems.

[16]  Paola Sebastiani,et al.  Clustering Continuous Time Series , 2001, ICML.

[17]  Feng Gao,et al.  Application of accurate online support vector regression in energy price forecast , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).

[18]  T. Dillon,et al.  Electricity price short-term forecasting using artificial neural networks , 1999 .