A Deep Learning Based Hybrid Framework for Day-Ahead Electricity Price Forecasting

Zhang, Rongquan; Li, Gangqiang; Ma, Z.Y.

doi:10.1109/access.2020.3014241

Cited by 63 publications

(25 citation statements)

References 52 publications

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“…At the cost of an additional parameter, the elastic net generally yields more accurate predictions than the LASSO. Nevertheless, the latter has become the golden standard in EPF Ziel and Weron, 2018;Janke and Steinke, 2019;Narajewski and Ziel, 2020a;Marcjasz, 2020;Zhang et al, 2020;Özen and Yıldırım, 2021). It was even utilized by Lago et al (2021) 7), of a LEAR-type model considered by Ziel and Weron (2018).…”

Section: Regularization and The Lear Modelmentioning

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: Regularization and The Lear Modelmentioning

confidence: 99%

Forecasting Electricity Prices

Maciejowska¹,

Uniejewski²,

Weron³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In recent years, social network analysis methods have been combined with theoretical approaches in intelligence, mostly for intelligence analysis [ 23 – 25 ], opinion dissemination [ 26 ], and co-authorship network research [ 27 ]. The research contents of bibliometrics and data mining, on the other hand, belong to the more stable core research directions of intelligence, and in recent years, they mainly focus on research hotspot identification [ 28 ], interdisciplinary knowledge flow [ 29 ], topic evolution [ 30 ], knowledge mapping [ 31 ], etc. Declining research hotspots will probably decline in hotness in the next few years, indicating that such topics have accumulated certain research results in the development process and have developed relatively mature, and will undergo topic evolution, more in-depth and detailed research, or shift to similar research fields in the future [ 32 ].…”

Section: Empirical Study: Intelligence As An Examplementioning

confidence: 99%

Section: Empirical Study: Intelligence As An Examplementioning

confidence: 99%

Prediction of Research Hotspots Based on LSTM: Taking Information Science as Example

Xiang

2022

Computational Intelligence and Neuroscience

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Detection and identification become the prediction of future research hotspots in the discipline, which is important to grasp the current status and development trend of the discipline research. In this paper, we use the cumulative topic heat model to calculate the research heat of each research topic in intelligence science from 2000 to 2020 and use the first 70% of the data as the training set, use the LSTM model for prediction, and construct the ECM model for error correction. The actual topic hotness of intelligence science for the latter 30% of data was used as the validation set to verify the effectiveness of the method. It was found that the average deviation rate of the method's prediction results fluctuated between 9.75% and 12.68%, and the average number of error entries was about 0.161, which had high validity. The study also predicts that by 2025, topics such as “crisis warning” and “health information services” in intelligence will continue to rise in popularity and “scientific data” and “data mining” will continue to rise in popularity. The hotness of “data mining” will remain stable, while the hotness of “citation analysis” and “ontology” will gradually decline.

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“…In [12], Wang et al exploited a deep-learning-based ensemble approach for probabilistic wind power forecasting. The experimental results showed [11], [12] superior forecasting performance of deep learning models, compared to statistical models and machine learning models.…”

Section: Introductionmentioning

confidence: 97%

“…Deep learning, perhaps the most powerful forecasting technology, is widely used nowadays, due to the large datasets and computational power available today. In [11], Zhang et al proposed a hybrid deep-learning framework to predict day-ahead electricity prices. In [12], Wang et al exploited a deep-learning-based ensemble approach for probabilistic wind power forecasting.…”

Section: Introductionmentioning

confidence: 99%

Cooperative trading of a price-maker wind power producer: A data-driven approach considering uncertainty

Qureshi¹,

Aziz²,

Bu³

et al. 2021

Preprint

Self Cite

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This paper presents a novel framework for cooperative trading in a price-maker wind power producer, that participates in the short-term electricity balance markets. In this framework, market price uncertainty is first modeled using a price uncertainty predictor, consisting of ridge regression (RR), nonpooling convolutional neural network (NPCNN), and linear quantile regression (LQR). RR is employed to select the correlated features to the corresponding forecast day, NPCNN is employed to extract the nonlinear features, and LQR is employed to estimate the price uncertainty. Then, an improved firefly algorithm (IFA) is proposed to solve the optimization problem. IFA uses the adaptive moment estimation method to improve the convergence speed and search for the global solution. Finally, the Shapley value is employed for the profit distribution of cooperative power producers. Illustrative examples show the effectiveness of the proposed framework and optimization model

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A Deep Learning Based Hybrid Framework for Day-Ahead Electricity Price Forecasting

Cited by 63 publications

References 52 publications

Forecasting Electricity Prices

Forecasting Electricity Prices

Prediction of Research Hotspots Based on LSTM: Taking Information Science as Example

Cooperative trading of a price-maker wind power producer: A data-driven approach considering uncertainty

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