Explanatory information analysis for day-ahead price forecasting in the Iberian electricity market

This paper presents the analysis of the importance of a set of explanatory (input) variables for the day-ahead price forecast in the Iberian Electricity Market (MIBEL). The available input variables include extensive hourly time series records of weather forecasts, previous prices, and regional aggregation of power generations and power demands. The paper presents the comparisons of the forecasting results achieved with a model which includes all these available input variables (EMPF model) with respect to those obtained by other forecasting models containing a reduced set of input variables. These comparisons identify the most important variables for forecasting purposes. In addition, a novel Reference Explanatory Model for Price Estimations (REMPE) that achieves hourly price estimations by using actual power generations and power demands of such day is described in the paper, which offers the lowest limit for the forecasting error of the EMPF model. All the models have been implemented using the same technique (artificial neural networks) and have been satisfactorily applied to the real-world case study of the Iberian Electricity Market (MIBEL). The relative importance of each explanatory variable is identified for the day-ahead price forecasts in the MIBEL. The comparisons also allow outlining guidelines of the value of the different types of input information.

[1]  Cheng Hao Jin,et al.  A SOM clustering pattern sequence-based next symbol prediction method for day-ahead direct electricity load and price forecasting , 2015 .

[2]  Bijaya K. Panigrahi,et al.  Prediction Interval Estimation of Electricity Prices Using PSO-Tuned Support Vector Machines , 2015, IEEE Transactions on Industrial Informatics.

[3]  Peng Kou,et al.  Probabilistic electricity price forecasting with variational heteroscedastic Gaussian process and active learning , 2015 .

[4]  H. Madsen,et al.  Predictive Densities for Day-Ahead Electricity Prices Using Time-Adaptive Quantile Regression , 2014 .

[5]  M. Delimar,et al.  Dynamic Hybrid Model for Short-Term Electricity Price Forecasting , 2014 .

[6]  R. Weron.  Electricity price forecasting: A review of the state-of-the-art with a look into the future , 2014 .

[7]  Jarmo Partanen,et al.  Forecasting electricity price and demand using a hybrid approach based on wavelet transform, ARIMA and neural networks , 2014 .

[8]  Joao P. S. Catalao,et al.  Electricity prices forecasting by a hybrid evolutionary-adaptive methodology , 2014 .

[9]  Najeh Chaâbane,et al.  A hybrid ARFIMA and neural network model for electricity price prediction , 2014 .

[10]  B. K. Panigrahi,et al.  A hybrid wavelet-ELM based short term price forecasting for electricity markets , 2014 .

[11]  Dipti Srinivasan,et al.  A hybrid intelligent model based on recurrent neural networks and excitable dynamics for price prediction in deregulated electricity market , 2013, Eng. Appl. Artif. Intell..

[12]  Yunhe Hou,et al.  A New Recursive Dynamic Factor Analysis for Point and Interval Forecast of Electricity Price , 2013, IEEE Transactions on Power Systems.

[13]  Francesco Lisi,et al.  Combining day-ahead forecasts for British electricity prices , 2013 .

[14]  H. Madsen,et al.  Forecasting Electricity Spot Prices Accounting for Wind Power Predictions , 2013, IEEE Transactions on Sustainable Energy.

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

[16]  Yik-Chung Wu,et al.  Load/Price Forecasting and Managing Demand Response for Smart Grids: Methodologies and Challenges , 2012, IEEE Signal Processing Magazine.

[17]  Joao P. S. Catalao,et al.  Short-term electricity prices forecasting in a competitive market by a hybrid PSO–ANFIS approach , 2012 .

[18]  Hamidreza Zareipour,et al.  Electricity Price and Demand Forecasting in Smart Grids , 2012, IEEE Transactions on Smart Grid.

[19]  Kit Po Wong,et al.  Electricity Price Forecasting With Extreme Learning Machine and Bootstrapping , 2012, IEEE Transactions on Power Systems.

[20]  Rosa Espínola,et al.  The effect of wind generation and weekday on Spanish electricity spot price forecasting , 2011 .

[21]  Yao Dong,et al.  Short-term electricity price forecast based on the improved hybrid model , 2011 .

[22]  Mohammad Kazem Sheikh-El-Eslami,et al.  Price forecasting of day-ahead electricity markets using a hybrid forecast method , 2011 .

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

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

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

[26]  Whei-Min Lin,et al.  An enhanced radial basis function network for short-term electricity price forecasting , 2010 .

[27]  Aldo Cipriano,et al.  Short-term forecasting of electricity prices in the Colombian electricity market , 2009 .

[28]  Nima Amjady,et al.  Day‐ahead price forecasting of electricity markets by a hybrid intelligent system , 2009 .

[29]  Derek W. Bunn,et al.  Forecasting electricity prices: The impact of fundamentals and time-varying coefficients , 2008 .

[30]  R. Weron,et al.  Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models , 2008 .

[31]  Fernando Villada,et al.  Electricity price forecasting using artificial neural networks , 2008 .

[32]  N. Pindoriya,et al.  An Adaptive Wavelet Neural Network-Based Energy Price Forecasting in Electricity Markets , 2008, IEEE Transactions on Power Systems.

[33]  Alvaro I. Arciniegas,et al.  Forecasting short-term power prices in the Ontario Electricity Market (OEM) with a fuzzy logic based inference system , 2008 .

[34]  V. Mendes,et al.  Short-term electricity prices forecasting in a competitive market: A neural network approach , 2007 .

[35]  A. Gil,et al.  Forecasting of electricity prices with neural networks , 2006 .

[36]  N. Amjady.  Day-ahead price forecasting of electricity markets by a new fuzzy neural network , 2006, IEEE Transactions on Power Systems.

[37]  Christopher R. Knittel,et al.  An empirical examination of restructured electricity prices , 2005 .

[38]  J. Contreras,et al.  Forecasting electricity prices for a day-ahead pool-based electric energy market , 2005 .

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

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

[41]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[42]  Sherif Hashem,et al.  Optimal Linear Combinations of Neural Networks , 1997, Neural Networks.

[43]  Steven C. Wheelwright,et al.  Forecasting: Methods and Applications, 3rd Ed , 1997 .

[44]  Steven C. Wheelwright,et al.  Forecasting methods and applications. , 1979 .