Recent advances in electricity price forecasting: A review of probabilistic forecasting

Nowotarski, Jakub; Weron, Rafał

doi:10.1016/j.rser.2017.05.234

Cited by 424 publications

(311 citation statements)

References 121 publications

(334 reference statements)

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“…for an equidistant grid of probabilities r between 0 and 1 of size R, see e.g. Nowotarski and Weron (2018 To draw significant conclusions on the outperformance of the forecasts of the considered models, we also calculate the Diebold and Mariano (1995) test, which tests forecasts of model A against forecasts of model B. In the following paper, we compute the multivariate version of the DM test as in Ziel and Weron (2018…”

Section: German Intraday Continuous Marketmentioning

confidence: 99%

Estimation and Simulation of the Transaction Arrival Process in Intraday Electricity Markets

Narajewski

Ziel

2019

Energies

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We examine the novel problem of the estimation of transaction arrival processes in the intraday electricity markets. We model the inter-arrivals using multiple timevarying parametric densities based on the generalized F distribution estimated by maximum likelihood. We analyse both the in-sample characteristics and the probabilistic forecasting performance. In a rolling window forecasting study, we simulate many trajectories to evaluate the forecasts and gain significant insights into the model fit. The prediction accuracy is evaluated by a functional version of the MAE (mean absolute error), RMSE (root mean squared error) and CRPS (continuous ranked probability score) for the simulated count processes. This paper fills the gap in the literature regarding the intensity estimation of transaction arrivals and is a major contribution to the topic, yet leaves much of the field for further development. The study presented in this paper is conducted based on the German Intraday Continuous electricity market data, but this method can be easily applied to any other continuous intraday electricity market. For the German market, a specific generalized gamma distribution setup explains the overall behaviour significantly best, especially as the tail behaviour of the process is well covered.

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Section: German Intraday Continuous Marketmentioning

confidence: 99%

Estimation and Simulation of the Transaction Arrival Process in Intraday Electricity Markets

Narajewski

Ziel

2019

Energies

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“…More recently there has been an interest in density forecasts for the hourly prices, motivated by considerations of risk management. See [1,2] for extensive reviews. In this paper, we provide a new formulation with a focus upon price spreads, and specifically, we forecast the density functions for the intraday spreads in the day-ahead prices.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Intra-Day Spread Densities of Electricity Prices

Abramova

Bunn

2020

Energies

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Intra-day price spreads are of interest to electricity traders, storage and electric vehicle operators. This paper formulates dynamic density functions, based upon skewed-t and similar representations, to model and forecast the German electricity price spreads between different hours of the day, as revealed in the day-ahead auctions. The four specifications of the density functions are dynamic and conditional upon exogenous drivers, thereby permitting the location, scale and shape parameters of the densities to respond hourly to such factors as weather and demand forecasts. The best fitting and forecasting specifications for each spread are selected based on the Pinball Loss function, following the closed-form analytical solutions of the cumulative distribution functions.Energies 2020, 13, 687 2 of 31 opportunities for storage or load shifting, will depend upon the wind and solar forecasts, as well as demand and supply considerations. Furthermore, the price density functions are non-normal with skewness switching between positive and negative depending upon the dynamics of production of renewable energy [9]. Because of this non-Normality and the non-independence of each hourly price, spread densities cannot be easily derived as the difference of the price densities, and so we estimate and forecast the daily matrix of intraday spreads directly.We apply our spread densities formulation to the German market. This is the largest and the main daily reference market for wholesale power in Europe. It is also strongly influenced by wind and solar production, as well as providing a context where batteries and demand-side management are active innovations. The day ahead auction has been actively researched and closes at noon each day, with the vector of 24 hourly prices for the next day being released an hour later. For modeling the spread densities, we adapt the Generalised Additive Model for Location, Scale and Shape (GAMLSS) semi-parametric regression model [10], which has already been used effectively to form day-ahead densities of price levels in the German context [9]. Within this framework, the hourly electricity price spreads form a response variable, whose distribution function varies according to multiple exogenous factors. The GAMLSS framework allows choice from a wide range of distributions, whose distribution parameters change according to the exogenous variables specified using (non)linear relationships. The dynamic location, scale and shape parameters (related to the mean, volatility, skewness and kurtosis of price spreads) are therefore explicitly incorporated into the forecasting model. The paper proceeds by first describing the data and the density estimation process. In Section 2, we use the Pinball Loss function to select the best fitting density model with four distribution parameters. Then in Section 3, we undertake a rolling window forecasting evaluation and demonstrate the value of the dynamic, conditional latent parameter. Section 4 concludes.

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“…Weron explained the complexity of available solutions, their strengths and weaknesses, and the opportunities and threats that the forecasting tools offer or that may be encountered in the process of electricity price forecasting [1]. A very useful tutorial review of probabilistic electricity price forecasting that presented much-needed guidelines for the rigorous use of methods, measures and tests, in line with the paradigm of 'maximizing sharpness subject to reliability,' was created by Nowotarski and Weron [2]. With the introduction of deregulation to the power industry, the price of electricity has been the key to all activities in the power market.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Electricity Price Forecasting Model Using Interval-Valued Autoregressive Process

et al. 2018

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Abstract:The uncertainty that dominates in the functioning of the electricity market is of great significance and arises, generally, because of the time imbalance in electricity consumption rates and power plants' production capacity, as well as the influence of many other factors (weather conditions, fuel costs, power plant operating costs, regulations, etc.). In this paper we try to incorporate this uncertainty in the electricity price forecasting model by applying interval numbers to express the price of electricity, with no intention of exploring influencing factors. This paper represents a hybrid model based on fuzzy C-mean clustering and the interval-valued autoregressive process for forecasting the short-term electricity price. A fuzzy C-mean algorithm was used to create interval time series to be forecasted by the interval autoregressive process. In this way, the efficiency of forecasting is improved because we predict the interval, not the crisp value where the price will be. This approach increases the flexibility of the forecasting model.

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Recent advances in electricity price forecasting: A review of probabilistic forecasting

Cited by 424 publications

References 121 publications

Estimation and Simulation of the Transaction Arrival Process in Intraday Electricity Markets

Estimation and Simulation of the Transaction Arrival Process in Intraday Electricity Markets

Forecasting the Intra-Day Spread Densities of Electricity Prices

Short-Term Electricity Price Forecasting Model Using Interval-Valued Autoregressive Process

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