2018
DOI: 10.3390/en11082039
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Efficient Forecasting of Electricity Spot Prices with Expert and LASSO Models

Abstract: Recent electricity price forecasting (EPF) studies suggest that the least absolute shrinkage and selection operator (LASSO) leads to well performing models that are generally better than those obtained from other variable selection schemes. By conducting an empirical study involving datasets from two major power markets (Nord Pool and PJM Interconnection), three expert models, two multi-parameter regression (called baseline) models and four variance stabilizing transformations combined with the seasonal compon… Show more

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Cited by 36 publications
(28 citation statements)
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“…An advantage of using automated variable selection is an almost unlimited number of initially considered explanatory variables [19]. In this study, we define a baseline model with 76 potential regressors and its three extensions; the largest one takes into account 200+ explanatory variables.…”
Section: Lasso-estimated Modelsmentioning
confidence: 99%
See 1 more Smart Citation
“…An advantage of using automated variable selection is an almost unlimited number of initially considered explanatory variables [19]. In this study, we define a baseline model with 76 potential regressors and its three extensions; the largest one takes into account 200+ explanatory variables.…”
Section: Lasso-estimated Modelsmentioning
confidence: 99%
“…Selecting a 'good' value for λ is critical. It is, however, a complex problem [9,12,19]. Because of a relatively short dataset, we are not able to reselect λ based on model performance in a validation period.…”
Section: Lasso Estimationmentioning
confidence: 99%
“…where X d−1 contains elements from the information set on day d − 1, i.e., a constant and lags of ∆ X,Y,d . In Figure 7 we illustrate the obtained p-values using 'chessboards', analogously as in [20,21,30,34] for the Diebold-Mariano test, i.e., we use a heat map to indicate the range of the p-values-the closer they are to zero (→ dark green) the more significant is the difference between the forecasts of a window set on the X-axis (better) and the forecasts of a window set on the Y-axis (worse). Evidently, the CPA test results confirm and emphasize the observations made in Section 4.2.…”
Section: The Cpa Test and Statistical Significancementioning
confidence: 99%
“…Besides the forecasting engine choice, another delicate issue surrounding forecasting accuracy is the proper feature selection (selected price explanatory variables) [46]. By this means, first a pre-selection stage is carried out where Correlation Analysis (CA) is performed over the available price data, i.e., we take the current day D (last available information) and measure the partial correlation between the prior lags with a span up to a total of 168 h (enabling the capture of intra-day and intra-week relationships), and by selecting the most significant lags the recent days set is formed.…”
Section: Short-term Price Forecastingmentioning
confidence: 99%