Abstract
Nowadays, modeling and forecasting electricity spot prices are challenging due to their specific features, including multiple seasonalities, calendar effects, and extreme values (also known as jumps, spikes, or outliers). This study aims to provide a comprehensive analysis of electricity price forecasting by comparing several outlier filtering techniques followed by various modeling frameworks. To this end, extreme values are first treated with five different filtering techniques and are then replaced by four different outlier replacement approaches. Next, the spikes-free series is divided into deterministic and stochastic components. The deterministic component includes long-term trend, yearly and weekly seasonalities, and bank holidays and is estimated through parametric and nonparametric approaches. On the other hand, the stochastic component accounts for the short-run dynamics of the price time series and is modeled using different univariate and multivariate models. The one-day-ahead out-of-sample forecast results for the Italian Power Exchange (IPEX), obtained for a whole year, suggest that the outliers pre-filtering give a high accuracy gain. In addition, multivariate modeling for the stochastic component outperforms univariate models.
Highlights
The liberalization of the electricity sector has transformed the structure of this sector by allowing the consumers and investors to make market entry openly
The model allows for dynamic point forecasting and stochastic simulation, the results suggested that the inclusion of exogenous variables does not significantly improve the results. [18] compared the accuracy of time series in both parametric and nonparametric cases using ARIMA, SARIMA, ARCH, AR and Moving Average (MA) models with exogenous variables ARX and ARMAX, vector AR models, threshold AR with exogenous variables TAR and TARX, regime switching model, and mean reverting jump diffusion models
This study revisited the problem of one-day-ahead electricity price forecasting in the liberalized electricity market.“As the price series is highly volatile, this study focused on filtering the series for extreme values
Summary
The liberalization of the electricity sector has transformed the structure of this sector by allowing the consumers and investors to make market entry openly. [28] studied the effect of extreme values treatment on the estimation of the seasonal and stochastic components in electricity price modeling. To deal with the estimation of seasonal components and extreme values in the electricity spot prices, [30] studied different approaches. “This study aims to develop and analyze the modeling framework for forecasting electricity prices after the treatment of price spikes (extreme values). To this end, this study focuses on modeling the behavior of day-ahead electricity prices for the Italian Power Exchange (IPEX) and compared different outliers treatment techniques along with varying frameworks of forecasting to obtain an accurate day-ahead forecast.
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