Electricity price forecasting

Weron, Rafał; Ziel, Florian

doi:10.4324/9781315459653-36

Cited by 7 publications

(12 citation statements)

References 13 publications

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“…An ensemble is a collection of simulated price paths, also called trajectories or scenarios. For a large number of paths the ensemble approximates the underlying distribution F P arbitrarily well (Weron and Ziel, 2020). In practice 'large' means thousands or millions of paths, which may be a computational challenge (Narajewski and Ziel, 2020b).…”

Section: Paths and Ensemblesmentioning

confidence: 99%

Forecasting Electricity Prices

Maciejowska¹,

Uniejewski²,

Weron³

2022

Preprint

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Forecasting electricity prices is a challenging task and an active area of research since the 1990s and the deregulation of the traditionally monopolistic and government-controlled power sectors. It is interdisciplinary by nature. It requires expertise in econometrics, statistics or machine learning for developing well-performing predictive models, in finance for understanding market mechanics, and in electrical engineering for comprehension of the fundamentals driving electricity prices.Although electricity price forecasting aims at predicting both spot and forward prices, the vast majority of research is focused on short-term horizons which exhibit dynamics unlike in any other market. The reason is that power system stability calls for a constant balance between production and consumption, while being weather (both demand and supply) and business activity (demand only) dependent. The recent market innovations do not help in this respect. The rapid expansion of intermittent renewable energy sources is not offset by the costly increase of electricity storage capacities and modernization of the grid infrastructure.On the methodological side, this leads to three visible trends in electricity price forecasting research as of 2022. Firstly, there is a slow, but more noticeable with every year, tendency to consider not only point but also probabilistic (interval, density) or even path (also called ensemble) forecasts. Secondly, there is a clear shift from the relatively parsimonious econometric (or statistical) models towards more complex and harder to comprehend, but more versatile and eventually more accurate statistical/machine learning approaches. Thirdly, statistical error measures are nowadays regarded as only the first evaluation step. Since they may not necessarily reflect the economic value of reducing prediction errors, more and more often, they are complemented by case studies comparing profits from scheduling or trading strategies based on price forecasts obtained from different models.

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Section: Paths and Ensemblesmentioning

confidence: 99%

Forecasting Electricity Prices

Maciejowska¹,

Uniejewski²,

Weron³

2022

Preprint

Self Cite

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“…The most important is the spot market, particularly the day-ahead auction. It takes place once a day at noon where all S products of the following day are traded in a uniform price auction [54]. In the majority of countries S = 24, however in some cases like the UK it is S = 48.…”

Section: The Datamentioning

confidence: 99%

“…The study uses a rolling window what mimics the daily business in practice and is a standard procedure in the EPF literature [1,54]. The initial in-sample period spans the date range from 01.01.2015 to 26.12.2018 which consists of D = 4 • 364 = 1456 observations.…”

Section: The Datamentioning

confidence: 99%

Distributional neural networks for electricity price forecasting

Marcjasz¹,

Narajewski²,

Weron³

et al. 2022

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We present a novel approach to probabilistic electricity price forecasting (EPF) which utilizes distributional artificial neural networks. The novel network structure for EPF is based on a regularized distributional multilayer perceptron (DMLP) which contains a probability layer. Using the TensorFlow Probability framework, the neural network's output is defined to be a distribution, either normal or potentially skewed and heavy-tailed Johnson's SU (JSU). The method is compared against state-of-the-art benchmarks in a forecasting study. The study comprises forecasting involving day-ahead electricity prices in the German market. The results show evidence of the importance of higher moments when modeling electricity prices.

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“…Out of the numerous approaches to EPF developed over the last two decades (Weron & Ziel, 2020), two classes of models are of particular importance when predicting dayahead prices -statistical (also called econometric or technical analysis) and computational intelligence (also referred to as artificial intelligence, non-linear or machine learning); to provide a comprehensive test ground we will use two well-performing benchmarks, one from each class. Moreover, many -if not most -of the proposed methods are hybrid solutions, that typically comprise data decomposition, feature selection, clustering, forecast averaging and/or heuristic optimization to estimate the (hyper)parameters (Lago et al, 2021).…”

Section: Electricity Price Forecastingmentioning

confidence: 99%

Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx

Olivares,

Challu,

Marcjasz

et al. 2021

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We extend the neural basis expansion analysis (NBEATS) to incorporate exogenous factors. The resulting method, called NBEATSx, improves on a well performing deep learning model, extending its capabilities by including exogenous variables and allowing it to integrate multiple sources of useful information. To showcase the utility of the NBEATSx model, we conduct a comprehensive study of its application to electricity price forecasting (EPF) tasks across a broad range of years and markets. We observe state-of-the-art performance, significantly improving the forecast accuracy by nearly 20% over the original NBEATS model, and by up to 5% over other well established statistical and machine learning methods specialized for these tasks. Additionally, the proposed neural network has an interpretable configuration that can structurally decompose time series, visualizing the relative impact of trend and seasonal components and revealing the modeled processes' interactions with exogenous factors. To assist related work we made the code available in this repository.

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Electricity price forecasting

Cited by 7 publications

References 13 publications

Forecasting Electricity Prices

Forecasting Electricity Prices

Distributional neural networks for electricity price forecasting

Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx

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