Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market

Abstract

This paper presents novel intraday session models for price forecasts (ISMPF models) for hourly price forecasting in the six intraday sessions of the Iberian electricity market (MIBEL) and the analysis of mean absolute percentage errors (MAPEs) obtained with suitable combinations of their input variables in order to find the best ISMPF models. Comparisons of errors from different ISMPF models identified the most important variables for forecasting purposes. Similar analyses were applied to determine the best daily session models for price forecasts (DSMPF models) for the day-ahead price forecasting in the daily session of the MIBEL, considering as input variables extensive hourly time series records of recent prices, power demands and power generations in the previous day, forecasts of demand, wind power generation and weather for the day-ahead, and chronological variables. ISMPF models include the input variables of DSMPF models as well as the daily session prices and prices of preceding intraday sessions. The best ISMPF models achieved lower MAPEs for most of the intraday sessions compared to the error of the best DSMPF model; furthermore, such DSMPF error was very close to the lowest limit error for the daily session. The best ISMPF models can be useful for MIBEL agents of the electricity intraday market and the electric energy industry.

Probabilistic Price Forecasting for Day-Ahead and Intraday Markets: Beyond the Statistical Model

“…These findings are supported by [33], where higher forecast quality was obtained by exclusively relying on the hourly prices of previous intraday and day-ahead sessions as model inputs. The present work shows that the same assumption is valid for probabilistic forecasts.…”

“…Moreover, each ID session is highly correlated with the previous one. This observation was also confirmed by the data analysis presented in [33] for the MIBEL price data. …”

“…The analysis showed that forecasted wind speed and the price in the previous day are the most relevant variables, although other variables like hydropower and nuclear generation levels from past days give explanatory information for price forecasting. A similar analysis was conducted for the six intraday markets in MIBEL and the results showed that the best variables were the hourly prices of the daily session and previous intraday sessions, as well as calendar variables [33]. Automatic selection algorithms can be applied to select the best set of variables [34].…”

“…With this publicly available information, an accurate forecast for the daily average price is generated and used to rescale the point forecast generated by a gradient boosting tree model, which is later used to feed a linear quantile regression that produces conditional quantile forecasts; • Applies probabilistic forecasting techniques to both day-ahead and intraday markets, which contrasts with the work in [32,33] that only proposed point forecast models.…”

A Novel Hybrid BND-FOA-LSSVM Model for Electricity Price Forecasting

“…For artificial intelligent-based electricity price forecasting method, there is no longer the assumption of linear relationships between electricity price and its influencing factors, which can effectively cover the shortages of conventional time-series statistical method. This kind of forecasting technique mainly includes artificial neural networks [12,13], fuzzy neural networks [14], extreme learning machines [15,16], and support vector machines [17,18]. However, there is also weakness for artificial intelligent forecasting method, namely the model parameters need to be set first, such as the kernel parameters of support vector machines and neuron number of neural networks.…”