Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling

Wei, Li; Becker, Denis

doi:10.1016/j.energy.2021.121543

Cited by 92 publications

(33 citation statements)

References 66 publications

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“…These scenarios need to be derived from a suitable price-forecasting model. Existing electricity forecasting methods can be divided into the following three categories, namely, physical methods [28], statistical methods (such as seasonal autoregressive integrated moving average (SARIMA) [29]), and machine learning methods [30], [31], [32]. Recently, with the development of deep learning, new electricity price prediction models have been continuously proposed [22], [30], [33].…”

Section: A Motivationmentioning

confidence: 99%

“…Existing electricity forecasting methods can be divided into the following three categories, namely, physical methods [28], statistical methods (such as seasonal autoregressive integrated moving average (SARIMA) [29]), and machine learning methods [30], [31], [32]. Recently, with the development of deep learning, new electricity price prediction models have been continuously proposed [22], [30], [33]. A long short-term memory (LSTM) network is a special kind of deep learning models that is capable of learning long-term dependencies.…”

Section: A Motivationmentioning

confidence: 99%

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Multimarket Trading Strategy of a Hydropower Producer Considering Active-Time Duration: A Distributional Regression Approach

Khodadadi

Hesamzadeh

Söder

2023

IEEE Systems Journal

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This article presents a new approach for finding the optimal multimarket trading strategy of cascaded hydropower plants (HPPs) in the sequential electricity markets. These markets are day-ahead energy market, the market for frequency containment reserve in normal mode (FCR-N), and manual frequency restoration reserve markets for both energy production and capacity reserve. The active-time duration (ATD) of an mFRR energy offer is an important required parameter and it is uncertain at the time of day-ahead offer-function submission. Hence, we suggest a distributional regression approach for ATD modeling in an optimal multimarket setup. Also, a modified machine learning approach is proposed to generate price scenarios for the mFRR energy market taking into account uncertain ATD parameters. To illustrate our proposed approach, various numerical experiments are performed. Our numerical results show how proper modeling of ATD parameters can lead to a more realistic multimarket offer-function for cascaded HPPs. Furthermore, the results show how the inclusion of FCR-N and mFRR capacity markets change the optimal day-ahead offer-function.

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Section: A Motivationmentioning

confidence: 99%

Section: A Motivationmentioning

confidence: 99%

Multimarket Trading Strategy of a Hydropower Producer Considering Active-Time Duration: A Distributional Regression Approach

Khodadadi

Hesamzadeh

Söder

2023

IEEE Systems Journal

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show abstract

“…These models can be gathered in a main title named as time series analysis. Specifically, ensemble learning methods for Austria [101], deep neural networks analysis for Germany [102] and US (New York) [103], sensitivity analysis for Mexico [104], and deep learning models for US (New York) [105] can be given as country-specific examples. General findings for the studies showed that the proposed method could provide an effective forecast.…”

Section: Electricity Market Price and Load Forecasting Through Wind Energy Productionmentioning

confidence: 99%

Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy

Acaroğlu

Márquez

2021

Energies

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Forecasting the electricity price and load has been a critical area of concern for researchers over the last two decades. There has been a significant economic impact on producers and consumers. Various techniques and methods of forecasting have been developed. The motivation of this paper is to present a comprehensive review on electricity market price and load forecasting, while observing the scientific approaches and techniques based on wind energy. As a methodology, this review follows the historical and structural development of electricity markets, price, and load forecasting methods, and recent trends in wind energy generation, transmission, and consumption. As wind power prediction depends on wind speed, precipitation, temperature, etc., this may have some inauspicious effects on the market operations. The improvements of the forecasting methods in this market are necessary and attract market participants as well as decision makers. To this end, this research shows the main variables of developing electricity markets through wind energy. Findings are discussed and compared with each other via quantitative and qualitative analysis. The results reveal that the complexity of forecasting electricity markets’ price and load depends on the increasing number of employed variables as input for better accuracy, and the trend in methodologies varies between the economic and engineering approach. Findings are specifically gathered and summarized based on researches in the conclusions.

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“…In recent years, emerging artificial intelligence algorithms have been widely used in the prediction of electricity price. For instance, Li et al [15] forecasted electricity price based on a long shortterm memory (LSTM) neural network, using a test period of 4 weeks. Aslam et al [16] focused on the performance of a convolutional network (CNN) in medium-term electricity price forecasting, and showed that the CNN model performs well.…”

Section: Introductionmentioning

confidence: 99%

Multi-Step-Ahead Electricity Price Forecasting Based on Temporal Graph Convolutional Network

Su¹,

Peng²,

Liu³

et al. 2022

Mathematics

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Traditional electricity price forecasting tends to adopt time-domain forecasting methods based on time series, which fail to make full use of the regional information of the electricity market, and ignore the extra-territorial factors affecting electricity price within the region under cross-regional transmission conditions. In order to improve the accuracy of electricity price forecasting, this paper proposes a novel spatio-temporal prediction model, which is combined with the graph convolutional network (GCN) and the temporal convolutional network (TCN). First, the model automatically extracts the relationships between price areas through the graph construction module. Then, the mix-jump GCN is used to capture the spatial dependence, and the dilated splicing TCN is used to capture the temporal dependence and forecast electricity price for all price areas. The results show that the model outperforms other models in both one-step forecasting and multi-step forecasting, indicating that the model has superior performance in electricity price forecasting.

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Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling

Cited by 92 publications

References 66 publications

Multimarket Trading Strategy of a Hydropower Producer Considering Active-Time Duration: A Distributional Regression Approach

Multimarket Trading Strategy of a Hydropower Producer Considering Active-Time Duration: A Distributional Regression Approach

Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy

Multi-Step-Ahead Electricity Price Forecasting Based on Temporal Graph Convolutional Network

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