Abstract
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.
Highlights
Electricity price forecasting has been a very active research field in the last 15 years because the hourly price for the electric energy that will be settled in the pool constitutes very valuable information if it could be known in advance: any agent involved in the electricity market would use this information to prepare his/her bids strategically in order to obtain the maximum profit
From the recorded information of explanatory variables, hourly values of actual Iberian aggregated power demand and generation of different types of power plants, and by applying a time series autocorrelation analysis of these variables [41], some explanatory information was identified for lags of 48 and 168 h; in accordance with such analysis, in Figure 1, several variables for hour h of day D – 1 and for hour h of day D – 6 were included: power demand (LD), hydropower generation (HG), solar power generation and power cogeneration (SG), coal power generation (CG), combined cycle power generation (CCG), and nuclear power generation (NG)
This paper presents the analysis of the importance of a set of explanatory variables for the day-ahead price forecast in the Iberian Electricity Market (MIBEL)
Summary
Electricity price forecasting has been a very active research field in the last 15 years because the hourly price for the electric energy that will be settled in the pool constitutes very valuable information if it could be known in advance: any agent involved in the electricity market would use this information to prepare his/her bids strategically in order to obtain the maximum profit. This paper is not concentrated on forecasting techniques, but it is focused on the “forecasting modelling”, that is, on the analysis of extensive sets of explanatory variables and their influence in price forecasts This forecasting modelling allows the identification of input data and processing structures suitable to be applied to specific electricity markets, that is, to other electricity markets different from the Iberian Electricity Market (MIBEL). This paper presents mainly two models related to the MIBEL: An Explanatory Model for Price Forecast (EMPF model) for day-ahead hourly price forecasts, at a regional level, which integrates a wide set of explanatory variables, which include regional aggregation of power multi-generations and power demands, recent prices, broad hourly time series records of weather forecasts as well as other chronological information This EMPF model constitutes, as shown later (Sections 2 and 4), the best explanatory model for forecasting purposes from point of view of including the most complete and suitable set of explanatory input variables.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
