The influence of renewables on electricity price forecasting: a robust approach

In this paper a robust approach to modelling electricity spot prices is introduced. Differently from what has been recently done in the literature on electricity price forecasting, where the attention has been mainly drawn by the prediction of spikes, the focus of this contribution is on the robust estimation of nonlinear SETARX models (Self-Exciting Threshold Auto Regressive models with eXogenous regressors). In this way, parameters estimates are not, or very lightly, influenced by the presence of extreme observations and the large majority of prices, which are not spikes, could be better forecasted. A Monte Carlo study is carried out in order to select the best weighting function for Generalized M-estimators of SETAR processes. A robust procedure to select and estimate nonlinear processes for electricity prices is introduced, including robust tests for stationarity and nonlinearity and robust information criteria. The application of the procedure to the Italian electricity market reveals the forecasting superiority of the robust GM-estimator based on the polynomial weighting function respect to the non-robust Least Squares estimator. Finally, the introduction of external regressors in the robust estimation of SETARX processes contributes to the improvement of the forecasting ability of the model

The Forecasting Accuracy of Electricity Price Formation Models.

In this paper we present an extensive comparison of four different classes of models for daily forecasting of spot electricity prices, including ARMAX, constant and time-varying parameter regression models as well as non linear Markov regime-switching regressions. They are selected for particular reasons related to the emerging body of research on the price formation processes observed in electricity markets. The analyses are conducted for representative trading periods of the day in the UK Power Exchange prompt market, with the price series adjusted for their deterministic components and spikes. They show that relative out-of-sample forecasting performances are distinctly different for each trading period, season and across the actual performance metrics. No model consistently outperforms the others, but the ARMAX approach performs well in most cases and the Diebold and Mariano test indicates that, when it is not the best, the ARMAX model is not statistically different from the best. Nevertheless, we suggest that subtle differences in performance between different methods under different conditions are consistent with the apparent variations in the price formation processes by time of day and by season. We conclude with some observations on the disparities between the model specifications appropriate for understanding in-sample price formation and those for accurate out-of-sample predictions

Combining day-ahead forecasts for British electricity prices

This paper considers how well the approach of combining forecasts extends to the context of electricity prices. With the increasing popularity of regime switching and time-varying parameter models for predicting power prices, the multi model and evolutionary considerations that usually support the combining of simpler time series methods may be less applicable when the individual models incorporate these features. We address this question with a backtesting analysis on British day-ahead prices. Furthermore, given the volatility of power prices and concerns about accurate forecasting under extreme price excursions, we evaluate the results using various error metrics including expected shortfall. The comparisons are furthermore carefully simulated to consider model selection uncertainty in order to realistically test the value of combining as an ex ante policy. Overall, our results support combining for both accurate operational planning and risk managemen

Forecasting next-day electricity prices: from different models to combination

As a result of deregulation of most power markets around the world electricity price modeling and forecasting have obtained increasing importance in recent years. Large number of models has been studied on a wide range of power markets, from linear time series and multivariate regression models to more complex non linear models with jumps, but results are mixing and there is no single model that provides convincing superior performance in forecasting spot prices. This study considers whether combination forecasts of spot electricity prices are statistically superior to a wide range of single model based forecasts. To this end we focus on one-day ahead forecasting of half-hourly spot data from the British UK Power Exchange electricity market. In this work we focus on modeling data corresponding to some load periods of the day in order to evaluate the forecasting performance of prices representative of different moment of the day. Several forecasting models for power spot prices are estimated on the basis of expanding and/or rolling estimation windows of different sizes. Included are linear ARMAX models, different specifications of multiple regression models, non linear Markov switching regression models and time-varying parameter regression models. One-day ahead forecasts are obtained for each model and evaluated according to different statistical criteria as prediction error statistics and the Diebold and Mariano test for equal predictive accuracy. Forecasting results highlight that no model globally outperforms the others: differences in forecasting accuracy depend on several factors, such as model specification, sample realization and forecasting period. Since different forecasting models seem to capture different features of spot price dynamics, we propose a forecasting approach based on the combination of forecasts. This approach has been useful to improve forecasting accuracy in several empirical situations, but it is novel in the spot electricity price forecasting context. In this work different strategies have been employed to construct combination forecasts. The simplest approach is an equally weighted combination of the forecasts. An alternative is the use of adaptive forecast combination procedures, which allows for time-varying combination coefficients. Methods from Bates & Granger (1969) are considered. Models entering the combination are chosen for each forecasting season using the model confidence set method (MCS) described in Hansen et al. (2003, 2005) and then screened with the forecasts encompassing method of Fair & Shiller (1990). For each load period, our findings underline that models behave differently in each season. For this reason we propose a combination applied at a seasonal level. In this thesis some promising results in this direction are presented. The combination results are compared with the best results obtained from the single models in each forecasting period and for different prediction error statistics. Our findings illustrate the usefulness of the procedure, showing that combining forecasts at a seasonal level have the potential to produce predictions of superior or equal accuracy relative to the individual forecasts.Con la liberalizzazione dei mercati dell’elettricità, il problema della modellazione e previsione dei prezzi elettrici è diventato di fondamentale importanza. In letteratura sono stati studiati e applicati ad un gran numero di mercati molti tipi di modelli, come modelli per serie storiche, regressione lineare e modelli non lineari a salti molto più complessi. I risultati però sono contrastanti e finora nessun modello ha mostrato una capacità previsiva dei prezzi elettrici superiore rispetto agli altri. L’obiettivo di questa tesi è capire se i modelli di combinazione di previsioni possano dare risultati statisticamente superiori rispetto alle previsioni ottenute da singoli modelli. In particolare, viene affrontato il problema della previsione dei prezzi elettrici del giorno dopo applicato al mercato elettrico britannico UK Power Exchange. In questo mercato, i prezzi hanno frequenza semioraria: al fine di valutare il comportamento previsivo dei modelli, relativamente all’andamento dei prezzi nei diversi momenti della giornata, sono state scelte specifiche fasce orarie. I modelli usati per la previsione dei prezzi sono stati stimati sulla base di finestre di dati espandibili e/o mobili di diverse misure fissate. I modelli considerati includono modelli lineari di tipo ARMAX e diverse specificazioni di modelli di regressione multipla. Inotre sono stati considerati modelli di regressione non lineare a regimi Markov switching e modelli di regressione a parametri non costanti. Le previsioni a un passo ottenute dai modelli specificati sono state confrontate secondo diversi criteri statistici come le statistiche basate sull’errore di previsione e il test di Diebold e Mariano. Dai risultati emerge che, globalmente, nessun modello considerato supera gli altri per abilità previsiva: vari fattori, tra cui specificazione del modello, realizzazione campionaria e periodo di previsione, influenzano l’accuratezza previsiva. Dal momento che modelli di previsione diversi sembrano evidenziare caratteristiche diverse della dinamica dei prezzi elettrici, viene proposto un approccio basato sulla combinazione di previsioni. Questo metodo, finalizzato a migliorare l’accuratezza previsiva, si è dimostrato utile in molti studi empirici, ma finora non è stato usato nel contesto della previsione dei prezzi elettrici. In questa tesi sono state usate diverse tecniche di combinazione. L’approccio più semplice consiste nel dare lo stesso peso a tutte le previsioni ottenute dai singoli modelli. Altre procedure di combinazione di previsioni sono di tipo adattivo, poichè utilizzano coefficienti non costanti. In questo contesto, sono stati considerati i metodi di Bates & Granger (1969). I modelli usati nella combinazione sono stati scelti, per ciascuna stagione di previsione, con il metodo model confidence set (MCS) descritto in Hansen et al. (2003, 2005) e successivamente ridotti con il metodo forecasts encompassing di Fair & Shiller (1990). Per ciascuna ora considerata, i risultati sottolineano che i modelli si comportano in modo diverso a seconda della stagione di previsione. Questa caratteristica giustifica l’applicazione dei modelli di combinazione di previsioni ad un livello stagionale. In questa tesi vengono presentati risultati promettenti in questa direzione. Considerando le statistiche basate sull’errore di previsione, i risultati delle combinazioni sono stati confrontati con i migliori risultati ottenuti dai singoli modelli in ciascun periodo previsivo. Il vantaggio della procedura proposta deriva dal fatto che combinando le previsioni ad un livello stagionale, si ottengono previsioni di accuratezza superiore o uguale rispetto alle previsioni individuali

Robust forecasting of electricity prices: simulations, models and the impact of renewable sources

In this paper a robust approach to modelling electricity spot prices is introduced. Differently from what has been recently done in the literature on electricity price forecasting, where the attention has been mainly drawn by the prediction of spikes, the focus of this contribution is on the robust estimation of nonlinear SETARX models (Self-Exciting Threshold Auto Regressive models with eXogenous regressors). In this way, parameters estimates are not, or very lightly, influenced by the presence of extreme observations and the large majority of prices, which are not spikes, could be better forecasted. A Monte Carlo study is carried out in order to select the best weighting function for Generalized M-estimators of SETAR processes. A robust procedure to select and estimate nonlinear processes for electricity prices is introduced, including robust tests for stationarity and nonlinearity and robust information criteria. The application of the procedure to the Italian electricity market reveals the forecasting superiority of the robust GM-estimator based on the polynomial weighting function respect to the non-robust Least Squares estimator. Finally, the introduction of generation from renewable sources in the robust estimation of SETARX processes contributes to the improvement of the forecasting ability of the model.JRC.B.1-Finance and Econom

Forecasting electricity prices through robust nonlinear models

In this paper a robust approach to modelling electricity spot prices is introduced. Differently from what has been recently done in the literature on electricity price forecasting, where the attention has been mainly drawn by the prediction of spikes, the focus of this contribution is on the robust estimation of nonlinear SETARX models. In this way, parameters estimates are not, or very lightly, influenced by the presence of extreme observations and the large majority of prices, which are not spikes, could be better forecasted. A Monte Carlo study is carried out in order to select the best weighting function for GM-estimators of SETAR processes. A robust procedure to select and estimate nonlinear processes for electricity prices is introduced, including robust tests for stationarity and nonlinearity and robust information criteria. The application of the procedure to the Italian electricity market reveals the forecasting superiority of the robust GM-estimator based on the polynomial weighting function on the non-robust Least Squares estimator. Finally, the introduction of external regressors in the robust estimation of SETARX processes contributes to the improvement of the forecasting ability of the model.JRC.B.1-Finance and Econom

Robust forecasting of electricity prices: simulations, models and the impact of renewable sources

In this paper a robust approach to modelling electricity spot prices is introduced. Dierently from what has been recently done in the literature on electricity price forecasting, where the attention has been mainly drawn by the prediction of spikes, the focus of this contribution is on the robust estimation of nonlinear SETARX models (Self-Exciting Threshold Auto Regressive models with eXogenous regressors). In this way, parameters estimates are not, or very lightly, in uenced by the presence of extreme observations and the large majority of prices, which are not spikes, could be better forecasted. A Monte Carlo study is carried out in order to select the best weighting function for Generalized M-estimators of SETAR processes. A robust procedure to select and estimate nonlinear processes for electricity prices is introduced, including robust tests for stationarity and nonlinearity and robust information criteria. The application of the procedure to the Italian electricity market reveals the forecasting superiority of the robust GM-estimator based on the polynomial weighting function respect to the non-robust Least Squares estimator. Finally, the introduction of generation from renewable sources in the robust estimation of SETARX processes contributes to the improvement of the forecasting ability of the model