Hourly Electricity Prices in Day-Ahead Markets

This paper focuses on the characteristics of hourly electricity prices in day-ahead markets. In these markets, quotes for day-ahead delivery of electricity are submitted simultaneously for all hours in the next day. The same information set is used for quoting all hours of the day. The dynamics of hourly electricity prices does not behave as a time series process. Instead, these prices should be treated as a panel in which the prices of 24 cross-sectional hours vary from day to day. This paper introduces a panel model for hourly electricity prices in day-ahead markets and examines their characteristics. The results show that hourly electricity prices exhibit hourly specific mean-reversion and that they oscillate around an hourly specific mean price level. Furthermore, a block structured cross-sectional correlation pattern between the hours is apparent.Day-ahead electricity;Electricity prices;Energy markets;Panel models

Robust Forecasting of Non-Stationary Time Series

This paper proposes a robust forecasting method for non-stationary time series. The time series is modelled using non-parametric heteroscedastic regression, and fitted by a localized MM-estimator, combining high robustness and large efficiency. The proposed method is shown to produce reliable forecasts in the presence of outliers, non-linearity, and heteroscedasticity. In the absence of outliers, the forecasts are only slightly less precise than those based on a localized Least Squares estimator. An additional advantage of the MM-estimator is that it provides a robust estimate of the local variability of the time series.Heteroscedasticity;Non-parametric regression;Prediction;Outliers;Robustness

Robust Control Charts for Time Series Data

This article presents a control chart for time series data, based on the one-step- ahead forecast errors of the Holt-Winters forecasting method. We use robust techniques to prevent that outliers affect the estimation of the control limits of the chart. Moreover, robustness is important to maintain the reliability of the control chart after the occurrence of alarm observations. The properties of the new control chart are examined in a simulation study and on a real data example.

Hourly Electricity Prices in Day-Ahead Markets

This paper focuses on the characteristics of hourly electricity prices in day-ahead markets. In these markets, quotes for day-ahead delivery of electricity are submitted simultaneously for all hours in the next day. The same information set is used for quoting all hours of the day. The dynamics of hourly electricity prices does not behave as a time series process. Instead, these prices should be treated as a panel in which the prices of 24 cross-sectional hours vary from day to day. This paper introduces a panel model for hourly electricity prices in day-ahead markets and examines their characteristics. The results show that hourly electricity prices exhibit hourly specific mean-reversion and that they oscillate around an hourly specific mean price level. Furthermore, a block structured cross-sectional correlation pattern between the hours is apparent

Robust Forecasting of Non-Stationary Time Series

This paper proposes a robust forecasting method for non-stationary time series. The time series is modelled using non-parametric heteroscedastic regression, and fitted by a localized MM-estimator, combining high robustness and large efficiency. The proposed method is shown to produce reliable forecasts in the presence of outliers, non-linearity, and heteroscedasticity. In the absence of outliers, the forecasts are only slightly less precise than those based on a localized Least Squares estimator. An additional advantage of the MM-estimator is that it provides a robust estimate of the local variability of the time series.