Importance of the Long-Term Seasonal Component in Day-Ahead Electricity Price Forecasting Revisited: Parameter-Rich Models Estimated via the LASSO

Jędrzejewski, Arkadiusz; Marcjasz, Grzegorz; Weron, Rafał

doi:10.3390/en14113249

Cited by 10 publications

(5 citation statements)

References 45 publications

(115 reference statements)

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“…It abstracts the data layer by layer by constructing a multilevel neural network structure to predict the future price. Future research can explore these problems in depth and propose more effective solutions to provide more power for the development of electricity price prediction (Jdrzejewski et al, 2021;Yakoub et al, 2023). With the continuous development of the energy market and the continuous innovation of data technology, we believe that future research on electricity price prediction will achieve more significant results.…”

Section: ) Deep Learning-based Forecasting Methodsmentioning

confidence: 99%

Research on a price prediction model for a multi-layer spot electricity market based on an intelligent learning algorithm

Lin,

Chen,

Zhao

et al. 2024

Front. Energy Res.

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With the continuous promotion of the unified electricity spot market in the southern region, the formation mechanism of spot market price and its forecast will become one of the core elements for the healthy development of the market. Effective spot market price prediction, on one hand, can respond to the spot power market supply and demand relationship; on the other hand, market players can develop reasonable trading strategies based on the results of the power market price prediction. The methods adopted in this paper include: Analyzing the principle and mechanism of spot market price formation. Identifying relevant factors for electricity price prediction in the spot market. Utilizing a clustering model and Spearman’s correlation to classify diverse information on electricity prices and extracting data that aligns with the demand for electricity price prediction. Leveraging complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to disassemble the electricity price curve, forming a multilevel electricity price sequence. Using an XGT model to match information across different levels of the electricity price sequence. Employing the ocean trapping algorithm-optimized Bidirectional Long Short-Term Memory (MPA-CNN-BiLSTM) to forecast spot market electricity prices. Through a comparative analysis of different models, this study validates the effectiveness of the proposed MPA-CNN-BiLSTM model. The model provides valuable insights for market players, aiding in the formulation of reasonable strategies based on the market's supply and demand dynamics. The findings underscore the importance of accurate spot market price prediction in navigating the complexities of the electricity market. This research contributes to the discourse on intelligent forecasting models in electricity markets, supporting the sustainable development of the unified spot market in the southern region.

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Section: ) Deep Learning-based Forecasting Methodsmentioning

confidence: 99%

Research on a price prediction model for a multi-layer spot electricity market based on an intelligent learning algorithm

Lin,

Chen,

Zhao

et al. 2024

Front. Energy Res.

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“…( 5) have been added to expert models. Interestingly, the performance of LEAR-type models can be further improved by deseazonalizing the data with respect to the long-term seasonal component (LTSC) before estimation (Jȩdrzejewski et al, 2021), just like in the case of parsimonious regression (Nowotarski and Weron, 2016) and neural network models (Marcjasz et al, 2020).…”

Section: P D24mentioning

confidence: 99%

Forecasting Electricity Prices

Maciejowska¹,

Uniejewski²,

Weron³

2022

Preprint

Self Cite

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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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“…The choice of the EPF model and its results can support the decision-making process in day-ahead trading, the transition towards new preferred business technologies [22], renewable energy sources [31,32], electromobility [33,34], and regional investment directions [35]. Although the forecasting methods are numerous, they have encouraged researchers to intensify their studies to develop more accurate forecasting techniques [36]. Finally, there is a research gap related to the comprehensive comparison of the EPF models' bibliometric relations, their development, and their accuracy [37].…”

Section: Introductionmentioning

confidence: 99%

Models of Electricity Price Forecasting: Bibliometric Research

Zema

Sulich

2022

Energies

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Electricity Price Forecasting (EPF) influences the sale conditions in the energy sector. Proper models of electricity price prognosis can be decisive for choice between energy sources as a start point of transformation toward renewable energy sources. This article aims to present and compare various EPF models scientific publications. Adopted in this study procedure, the EPF publications models are compared into two main categories: the most popular and the most accurate. The adopted method is a bibliometric study as a variation of Systematic Literature Review (SLR) with specified automated queries supported by the VOSviewer bibliometric maps exploration. The subject of this research is the exploration of EPF models in two databases, Web of Science and Scopus, and their content comparison. As a result, the SLR research queries were classified into two groups, the most cited and most accurate models. Queries characteristics were explained, along with the graphical presentation of the results. Future promising research avenues can be dedicated to the most accurate EPF model formulation proved by statistical testing of its significance and accuracy.

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Importance of the Long-Term Seasonal Component in Day-Ahead Electricity Price Forecasting Revisited: Parameter-Rich Models Estimated via the LASSO

Cited by 10 publications

References 45 publications

Research on a price prediction model for a multi-layer spot electricity market based on an intelligent learning algorithm

Research on a price prediction model for a multi-layer spot electricity market based on an intelligent learning algorithm

Forecasting Electricity Prices

Models of Electricity Price Forecasting: Bibliometric Research

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