Forecasting Electricity Spot Prices Using Lasso: On Capturing the Autoregressive Intraday Structure

By Florian Ziel. IEEE Transactions on Power Systems, 2015

129 citations recorded by the source corpus.

In this paper we present a regression based model for day-ahead electricity spot prices. We estimate the considered linear regression model by the lasso estimation method. The lasso approach allows for many possible parameters in the model, but also shrinks and sparsifies the parameters automatically to avoid overfitting. Thus, it is able to capture the autoregressive intraday dependency structure of the electricity price well. We discuss in detail the estimation results which provide insights to the intraday behavior of electricity prices. We perform an out-of-sample forecasting study for several European electricity markets. The results illustrate well that the efficient lasso based estimation technique can exhibit advantages from two popular model approaches.

Subfields: Economics and Econometrics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/0f8475eddfaa4876ed6364cbd27df6f174c915a4

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Citations in the corpus

Probabilistic forecasting in day-ahead electricity markets: Simulating peak and off-peak prices (2018, background): “As mentioned above, electricity prices show special characteristics which are usually classified in the relevant literature ( see Weron (2014) and Ziel (2016)).”
Electricity price forecasting using sale and purchase curves: The X-Model (2015, methodology): “As in Ziel (2016a), we will allow for much more ﬂexibility in the model, as the structure of the data is far more complex.”
Variance Stabilizing Transformations for Electricity Spot Price Forecasting (2018, methodology): “Recently, Ziel and Weron [13] utilized it in an extensive empirical study on multi- and univariate EPF models, motivated by the transformation’s ability to preserve unimodality of the sample density.”
On the importance of the long-term seasonal component in day-ahead electricity price forecasting (2016, methodology): “…proposed by Misiorek et al. (2006) and later used in a number of EPF studies (Gaillard et al., 2016; Kristiansen, 2012; Maciejowska et al., 2016; Nowotarski and Weron, 2016; Nowotarski et al., 2014; Serinaldi, 2011; Uniejewski et al., 2016; Weron, 2006; Weron and Misiorek, 2008; Ziel, 2016).”
Econometric modelling and forecasting of intraday electricity prices (2018, methodology): “As the measure of the importance of a standardized parameter (cid:101) β s,i we consider the fraction of the absolute standardized parameter of a model to the sum of all absolute standardized parameters as in Ziel (2016) Naturally, 0 ≤ ι s,i ≤ 1 and =1 ι s,i = 1.”
Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark (2020, methodology): “The LEAR is based on the so-called full ARX or fARX model, a parameter-rich autoregressive specification with exogenous variables, which in turn is inspired by the general autoregressive model defined by Equation (2) in [68], with some important differences.”
Recent advances in electricity price forecasting: A review of probabilistic forecasting (2016, methodology): “In the first two autoregressive structures (ARX and mARX) the modeling is implemented separately across the hours, leading to 24 sets of parameters for each day, an approach commonly taken in EPF studies [29, 35, 42, 43, 72, 117, 118].”