Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine

Khan, Sajjad; Aslam, Shahzad; Mustafa, İqra; Aslam, Sheraz

doi:10.3390/forecast3030028

Cited by 20 publications

(13 citation statements)

References 38 publications

(41 reference statements)

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“…Machine learning (ML)-based techniques are implemented in smart grids for providing mitigation and detection against cybersecurity attacks. The authors of [148] also implemented ML techniques to forecast electricity prices; however, we analyzed the ML techniques that are applied to detect the cyberattacks on smart meters that causes huge electricity cost. In Figure 7, we present the general framework adopted in smart grid.…”

Section: Deep Learning Based Cybersecurity Techniques In Smart Gridsmentioning

confidence: 99%

Cyber Threats to Smart Grids: Review, Taxonomy, Potential Solutions, and Future Directions

et al. 2022

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Smart Grids (SGs) are governed by advanced computing, control technologies, and networking infrastructure. However, compromised cybersecurity of the smart grid not only affects the security of existing energy systems but also directly impacts national security. The increasing number of cyberattacks against the smart grid urgently necessitates more robust security protection technologies to maintain the security of the grid system and its operations. The purpose of this review paper is to provide a thorough understanding of the incumbent cyberattacks’ influence on the entire smart grid ecosystem. In this paper, we review the various threats in the smart grid, which have two core domains: the intrinsic vulnerability of the system and the external cyberattacks. Similarly, we analyze the vulnerabilities of all components of the smart grid (hardware, software, and data communication), data management, services and applications, running environment, and evolving and complex smart grids. A structured smart grid architecture and global smart grid cyberattacks with their impact from 2010 to July 2022 are presented. Then, we investigated the the thematic taxonomy of cyberattacks on smart grids to highlight the attack strategies, consequences, and related studies analyzed. In addition, potential cybersecurity solutions to smart grids are explained in the context of the implementation of blockchain and Artificial Intelligence (AI) techniques. Finally, technical future directions based on the analysis are provided against cyberattacks on SGs.

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Section: Deep Learning Based Cybersecurity Techniques In Smart Gridsmentioning

confidence: 99%

Cyber Threats to Smart Grids: Review, Taxonomy, Potential Solutions, and Future Directions

et al. 2022

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“…This paper summarizes the formation of the electricity price in the spot market as follows [39][40][41][42][43]:…”

Section: Analysis On the Formation Mechanism Of Electricity Price In ...mentioning

confidence: 99%

Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China

et al. 2022

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With the deepening of China’s electricity spot market construction, spot market price prediction is the basis for making reasonable quotation strategies. This paper proposes a day-ahead spot market price forecast based on a hybrid extreme learning machine technology. Firstly, the trading center’s information is examined using the Spearman correlation coefficient to eliminate characteristics that have a weak link with the price of power. Secondly, a similar day-screening model with weighted grey correlation degree is constructed based on the grey correlation theory (GRA) to exclude superfluous samples. Thirdly, the regularized limit learning machine (RELM) is tuned using the Marine Predators Algorithm (MPA) to increase RELM parameter accuracy. Finally, the proposed forecasting model is applied to the Shanxi spot market, and other forecasting models and error computation methodologies are compared. The results demonstrate that the model suggested in this paper has a specific forecasting effect for power price forecasting technology.

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“…Although this proposed method improved accuracy and efficiency compared to traditional methods, it failed to produce satisfactory mean absolute percentage error (MAPE) values. In another study [21], Ensemble Empirical Mode Decomposition was used in conjunction with various regression models such as Recurrent Neural Network (RNN), Multi-Layer Perceptron (MLP), SVR and ELM to obtain the actual predicted price of electricity based on data from the power systems of New South Wales (NSW), Queensland (QLD) and Victoria (VIC). According to the findings of this study, computational intelligence models produce a large MAPE; however, the suggested ELM model paired with the Ensemble Empirical Mode Decomposition manages to reduce the MAPE to less than 10%.…”

Section: Introductionmentioning

confidence: 99%

Optimized Data-Driven Models for Short-Term Electricity Price Forecasting Based on Signal Decomposition and Clustering Techniques

et al. 2022

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In recent decades, the traditional monopolistic energy exchange market has been replaced by deregulated, competitive marketplaces in which electricity may be purchased and sold at market prices like any other commodity. As a result, the deregulation of the electricity industry has produced a demand for wholesale organized marketplaces. Price predictions, which are primarily meant to establish the market clearing price, have become a significant factor to an energy company’s decision making and strategic development. Recently, the fast development of deep learning algorithms, as well as the deployment of front-end metaheuristic optimization approaches, have resulted in the efficient development of enhanced prediction models that are used for electricity price forecasting. In this paper, the development of six highly accurate, robust and optimized data-driven forecasting models in conjunction with an optimized Variational Mode Decomposition method and the K-Means clustering algorithm for short-term electricity price forecasting is proposed. In this work, we also establish an Inverted and Discrete Particle Swarm Optimization approach that is implemented for the optimization of the Variational Mode Decomposition method. The prediction of the day-ahead electricity prices is based on historical weather and price data of the deregulated Greek electricity market. The resulting forecasting outcomes are thoroughly compared in order to address which of the two proposed divide-and-conquer preprocessing approaches results in more accuracy concerning the issue of short-term electricity price forecasting. Finally, the proposed technique that produces the smallest error in the electricity price forecasting is based on Variational Mode Decomposition, which is optimized through the proposed variation of Particle Swarm Optimization, with a mean absolute percentage error value of 6.15%.

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Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine

Cited by 20 publications

References 38 publications

Cyber Threats to Smart Grids: Review, Taxonomy, Potential Solutions, and Future Directions

Cyber Threats to Smart Grids: Review, Taxonomy, Potential Solutions, and Future Directions

Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China

Optimized Data-Driven Models for Short-Term Electricity Price Forecasting Based on Signal Decomposition and Clustering Techniques

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