Investigation of autoregressive forecasting models for market electricity price

Shikhina, Anna V.; Kochengin, Alexei; Chrysostomou, George; Shikhin, Vladimir

doi:10.1109/melecon48756.2020.9140574

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…There are also studies [20][21][22] focusing on the comparison of different time series analysis methods for electricity price forecasting. In [20], the authors compare several prediction models, including SARIMA, SARIMAX, and ARIMA, to predict day-ahead electricity prices in Germany.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The findings enable informed decision making for power generation scheduling in the electricity market. Furthermore, accurate forecasting results have been obtained through various auto-regressive statistical models and their derivatives, as presented in [22]. Detailed computational procedures are provided along with numerical results and performance (MAPE), with some promising results and issues to consider.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture

Cantillo-Luna,

Moreno-Chuquen,

Lopez-Sotelo

et al. 2023

Energies

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This paper describes the development of a deep neural network architecture based on transformer encoder blocks and Time2Vec layers for the prediction of electricity prices several steps ahead (8 h), from a probabilistic approach, to feed future decision-making tools in the context of the widespread use of intra-day DERs and new market perspectives. The proposed model was tested with hourly wholesale electricity price data from Colombia, and the results were compared with different state-of-the-art forecasting baseline-tuned models such as Holt–Winters, XGBoost, Stacked LSTM, and Attention-LSTM. The findings show that the proposed model outperforms these baselines by effectively incorporating nonlinearity and explicitly modeling the underlying data’s behavior, all of this under four operating scenarios and different performance metrics. This allows it to handle high-, medium-, and low-variability scenarios while maintaining the accuracy and reliability of its predictions. The proposed framework shows potential for significantly improving the accuracy of electricity price forecasts, which can have significant benefits for making informed decisions in the energy sector.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture

Cantillo-Luna,

Moreno-Chuquen,

Lopez-Sotelo

et al. 2023

Energies

View full text Add to dashboard Cite

show abstract

Energy Efficiency Improvement of Industrial Enterprise Based on Machine Learning Electricity Tariff Forecasting

Matrenin

Antonenkov

Arestova

2021

2021 XV International Scientific-Technical Conference on Actual Problems of Electronic Instrument Engineering (APEIE)

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The State of the Art Electricity Load and Price Forecasting for the Modern Wholesale Electricity Market

Laitsos,

Vontzos,

Paraschoudis

et al. 2024

Energies

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In a modern and dynamic electricity market, ensuring reliable, sustainable and efficient electricity distribution is a pillar of primary importance for grid operation. The high penetration of renewable energy sources and the formation of competitive prices for utilities play a critical role in the wider economic development. Electricity load and price forecasting have been a key focus of researchers in the last decade due to the substantial economic implications for both producers, aggregators and end consumers. Many forecasting techniques and methods have emerged during this period. This paper conducts a extensive and analytical review of the prevailing load and electricity price forecasting methods in the context of the modern wholesale electricity market. The study is separated into seven main sections. The first section provides the key challenges and the main contributions of this study. The second section delves into the workings of the electricity market, providing a detailed analysis of the three markets that have evolved, their functions and the key factors influencing overall market dynamics. In the third section, the main methodologies of electricity load and price forecasting approaches are analyzed in detail. The fourth section offers a comprehensive review of the existing literature focusing on load forecasting, highlighting various methodologies, models and their applications in this field. This section emphasizes the advances that have been made in all categories of forecasting models and their practical application in different market scenarios. The fifth section focuses on electricity price forecasting studies, summarizing important research papers investigating various modeling approaches. The sixth section constitutes a fundamental discussion and comparison between the load- and price-focused studies that are analyzed. Finally, by examining both traditional and cutting-edge forecasting methods, this review identifies key trends, challenges and future directions in the field. Overall, this paper aims to provide an in-depth analysis leading to the understanding of the state-of-the-art models in load and price forecasting and to be an important resource for researchers and professionals in the energy industry. Based on the research conducted, there is an increasing trend in the use of artificial intelligence models in recent years, due to the flexibility and adaptability they offer for big datasets, compared to traditional models. The combination of models, such as ensemble methods, gives us very promising results.

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Investigation of autoregressive forecasting models for market electricity price

Cited by 3 publications

References 22 publications

An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture

An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture

Energy Efficiency Improvement of Industrial Enterprise Based on Machine Learning Electricity Tariff Forecasting

The State of the Art Electricity Load and Price Forecasting for the Modern Wholesale Electricity Market

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