PCA Forecast Averaging—Predicting Day-Ahead and Intraday Electricity Prices

Maciejowska, Katarzyna; Uniejewski, Bartosz; Serafin, Tomasz

doi:10.3390/en13143530

Cited by 31 publications

(26 citation statements)

References 52 publications

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“…Notably, the PCA method was sensitive to outliers in the data [ 63 ]. Therefore, research suggests it could be a promising alternative for other weighting schemes [ 64 ]. However, the PCA method was not developed to identify a subset of variables among many variables that are most predictive of an outcome [ 34 ].For the comparative analysis to Case Study 2, Mean averages are 9.09% as eleven items (dimensions) were involved.…”

Section: Comparative Analysis Of the Pca And Entropy Methodsmentioning

confidence: 99%

A comparative analysis of the principal component analysis and entropy weight methods to establish the indexing measurement

Zhang

Yan

et al. 2022

PLoS ONE

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Background As the world’s largest coal producer, China was accounted for about 46% of global coal production. Among present coal mining risks, methane gas (called gas in this paper) explosion or ignition in an underground mine remains ever-present. Although many techniques have been used, gas accidents associated with the complex elements of underground gassy mines need more robust monitoring or warning systems to identify risks. This paper aimed to determine which single method between the PCA and Entropy methods better establishes a responsive weighted indexing measurement to improve coal mining safety. Methods Qualitative and quantitative mixed research methodologies were adopted for this research, including analysis of two case studies, correlation analysis, and comparative analysis. The literature reviewed the most-used multi-criteria decision making (MCDM) methods, including subjective methods and objective methods. The advantages and disadvantages of each MCDM method were briefly discussed. One more round literature review was conducted to search publications between 2017 and 2019 in CNKI. Followed two case studies, correlation analysis and comparative analysis were then conducted. Research ethics was approved by the Shanxi Coking Coal Group Research Committee. Results The literature searched a total of 25,831publications and found that the PCA method was the predominant method adopted, and the Entropy method was the second most widely adopted method. Two weighting methods were compared using two case studies. For the comparative analysis of Case Study 1, the PCA method appeared to be more responsive than the Entropy. For Case Study 2, the Entropy method is more responsive than the PCA. As a result, both methods were adopted for different cases in the case study mine and finally deployed for user acceptance testing on 5 November 2020. Conclusions The findings and suggestions were provided as further scopes for further research. This research indicated that no single method could be adopted as the better option for establishing indexing measurement in all cases. The practical implication suggests that comparative analysis should always be conducted on each case and determine the appropriate weighting method to the relevant case. This research recommended that the PCA method was a dimension reduction technique that could be handy for identifying the critical variables or factors and effectively used in hazard, risk, and emergency assessment. The PCA method might also be well-applied for developing predicting and forecasting systems as it was sensitive to outliers. The Entropy method might be suitable for all the cases requiring the MCDM. There is also a need to conduct further research to probe the causal reasons why the PCA and Entropy methods were applied to each case and not the other way round. This research found that the Entropy method provides higher accuracy than the PCA method. This research also found that the Entropy method demonstrated to assess the weights of the higher dimension dataset was higher sensitivity than the lower dimensions. Finally, the comprehensive analysis indicates a need to explore a more responsive method for establishing a weighted indexing measurement for warning applications in hazard, risk, and emergency assessments.

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Section: Comparative Analysis Of the Pca And Entropy Methodsmentioning

confidence: 99%

A comparative analysis of the principal component analysis and entropy weight methods to establish the indexing measurement

Zhang

Yan

et al. 2022

PLoS ONE

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“…We use a heatmap to indicate the span of p-values. The closer they are to zero (dark green), the more significant is the difference between forecasts obtained with the approach from X-axis (superior) and predictions from the method in the Y-axis (inferior) [7,16,15]. The "chessboard" in Figure 5 corresponds to the results of the multivariate approach, considering 24-dimensional error vectors (see Equation 3).…”

Section: Resultsmentioning

confidence: 99%

“…As shown by numerous studies in the EPF [7,14], the selection of the calibration sample impacts the overall forecasting accuracy of the autoregressive model. While the majority of authors consider the longest possible portion of data for the model calibration, averaging predictions obtained from calibration samples of different lengths [16] or utilizing more sophisticated statistical methods [15] allows for the significant reduction of forecasting errors. In this paper, we propose a new method for the selection of the calibration sample, based on the k-nearest neighbors algorithm.…”

Section: Methods and Algorithmsmentioning

confidence: 99%

Forecasting Electricity Prices: Autoregressive Hybrid Nearest Neighbors (ARHNN) Method

Nitka

Serafin

Sotiros

2021

Lecture Notes in Computer Science

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The ongoing reshape of electricity markets has significantly stimulated electricity trading. Limitations in storing electricity as well as on-the-fly changes in demand and supply dynamics, have led price forecasts to be a fundamental aspect of traders' economic stability and growth. In this perspective, there is a broad literature that focuses on developing methods and techniques to forecast electricity prices. In this paper, we develop a new hybrid method, called ARHNN, for electricity price forecasting (EPF) in day-ahead markets. A well performing autoregressive model, with exogenous variables, is the main forecasting instrument in our method. Contrarily to the traditional statistical approaches, in which the calibration sample consists of the most recent and successive observations, we employ the k-nearest neighbors (k-NN) instance-based learning algorithm and we select the calibration sample based on a similarity (distance) measure over a subset of the autoregressive model's variables. The optimal levels of the k-NN parameter are identified during the validation period in a way that the forecasting error is minimized. We apply our method in the EPEX SPOT market in Germany. The results reveal a significant improvement in accuracy compared to commonly used approaches.

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“…However, what role will optimal execution play in the future, and how could further research promote its importance? We encourage trading-oriented forecasting approaches like that of Maciejowska et al [30] or Maciejowska et al [29] to discuss execution as well. Benchmarks like ID3 are a suitable first approximation but neglect the trading character and the fact that a trader often has to pay bid-ask spreads.…”

Section: Discussionmentioning

confidence: 99%

Optimal Order Execution in Intraday Markets: Minimizing Costs in Trade Trajectories

Kath,

Ziel

2020

Preprint

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Optimal execution, i.e., the determination of the most cost-effective way to trade volumes in continuous trading sessions, has been a topic of interest in the equity trading world for years.Electricity intraday trading slowly follows this trend but is far from being well-researched.The underlying problem is a very complex one. Energy traders, producers, and electricity wholesale companies receive various position updates from customer businesses, renewable energy production, or plant outages and need to trade these positions in intraday markets.They have a variety of options when it comes to position sizing or timing. Is it better to trade all amounts at once? Should they split orders into smaller pieces? Taking the German continuous hourly intraday market as an example, this paper derives an appropriate model for electricity trading. We present our results from an out-of-sample study and differentiate between simple benchmark models and our more refined optimization approach that takes into account order book depth, time to delivery, and different trading regimes like XBID (Cross-Border Intraday Project) trading. Our paper is highly relevant as it contributes further insight into the academic discussion of algorithmic execution in continuous intraday markets and serves as an orientation for practitioners. Our initial results suggest that optimal execution strategies have a considerable monetary impact.

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PCA Forecast Averaging—Predicting Day-Ahead and Intraday Electricity Prices

Cited by 31 publications

References 52 publications

A comparative analysis of the principal component analysis and entropy weight methods to establish the indexing measurement

A comparative analysis of the principal component analysis and entropy weight methods to establish the indexing measurement

Forecasting Electricity Prices: Autoregressive Hybrid Nearest Neighbors (ARHNN) Method

Optimal Order Execution in Intraday Markets: Minimizing Costs in Trade Trajectories

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