Price Forecasting in the Day-Ahead Energy Market by an Iterative Method with Separate Normal Price and Price Spike Frameworks

Voronin, Sergey; Partanen, Jarmo

doi:10.3390/en6115897

Cited by 61 publications

(38 citation statements)

References 49 publications

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“…Amjady and Keynia [4] proposed a feature selection algorithm that utilized the mutual information technique. (for later applications, see, e.g., [11,14,15]). In an econometric setup, Gianfreda and Grossi [5] computed p-values of the coefficients of a regression model with autoregressive fractionally integrated moving average disturbances (Reg-ARFIMA) and in one step eliminated all statistically-insignificant variables.…”

Section: Introductionmentioning

confidence: 99%

Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting

2016

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Abstract:In day-ahead electricity price forecasting (EPF) variable selection is a crucial issue. Conducting an empirical study involving state-of-the-art parsimonious expert models as benchmarks, datasets from three major power markets and five classes of automated selection and shrinkage procedures (single-step elimination, stepwise regression, ridge regression, lasso and elastic nets), we show that using the latter two classes can bring significant accuracy gains compared to commonly-used EPF models. In particular, one of the elastic nets, a class that has not been considered in EPF before, stands out as the best performing model overall.

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Section: Introductionmentioning

confidence: 99%

Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting

2016

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“…As there are seven dummies in Equation (6), one of them plays the role of the intercept. Note, that there is no intercept in Equation (5); this is not a major shortcoming since the prices are deseasonalized (i.e., centered) beforehand.…”

Section: Benchmarksmentioning

confidence: 99%

“…In the machine learning literature, Amjady and Keynia [5] proposed a feature selection algorithm based on mutual information that was later utilized in [6,7], among others. On the other hand, Gianfreda and Grossi [8] used a very simple technique-single-step elimination of insignificant predictors in a regression setting.…”

Section: Introductionmentioning

confidence: 99%

Efficient Forecasting of Electricity Spot Prices with Expert and LASSO Models

Uniejewski

Weron

2018

Energies

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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 component approach, we discuss the optimal way of implementing the LASSO. We show that using a complex baseline model with nearly 400 explanatory variables, a well chosen variance stabilizing transformation (asinh or N-PIT), and a procedure that recalibrates the LASSO regularization parameter once or twice a day indeed leads to significant accuracy gains compared to the typically considered EPF models. Moreover, by analyzing the structures of the best LASSO-estimated models, we identify the most important explanatory variables and thus provide guidelines to structuring better performing models.

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“…Some other approaches are based on the namely nontraditional techniques, which include: decision trees and rule-based approaches; probability methods, such as Bayesian classifiers [30]; neural network (NN) methods, such as spiking NN [31]; example-based methods, such as k-nearest neighbors [19,32]; and SVM (support vector machine) [33].…”

Section: Previous Workmentioning

confidence: 99%

Medium-Term Probabilistic Forecasting of Extremely Low Prices in Electricity Markets: Application to the Spanish Case

2016

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Abstract:One of the most relevant challenges that have arisen in electricity markets during the last few years is the emergence of extremely low prices. Trying to predict these events is crucial for market agents in a competitive environment. This paper proposes a novel methodology to simultaneously accomplish punctual and probabilistic hourly predictions about the appearance of extremely low electricity prices in a medium-term scope. The proposed approach for making real ex ante forecasts consists of a nested compounding of different forecasting techniques, which incorporate Monte Carlo simulation, combined with spatial interpolation techniques. The procedure is based on the statistical identification of the process key drivers. Logistic regression for rare events, decision trees, multilayer perceptrons and a hybrid approach, which combines a market equilibrium model with logistic regression, are used. Moreover, this paper assesses whether periodic models in which parameters switch according to the day of the week can be even more accurate. The proposed techniques are compared to a Markov regime switching model and several naive methods. The proposed methodology empirically demonstrates its effectiveness by achieving promising results on a real case study based on the Spanish electricity market. This approach can provide valuable information for market agents when they face decision making and risk-management processes. Our findings support the additional benefit of using a hybrid approach for deriving more accurate predictions.

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Price Forecasting in the Day-Ahead Energy Market by an Iterative Method with Separate Normal Price and Price Spike Frameworks

Cited by 61 publications

References 49 publications

Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting

Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting

Efficient Forecasting of Electricity Spot Prices with Expert and LASSO Models

Medium-Term Probabilistic Forecasting of Extremely Low Prices in Electricity Markets: Application to the Spanish Case

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