Short-term power load forecasting based on gray relational analysis and support vector machine optimized by artificial bee colony algorithm

Pang, Xinfu; Sun, Wei; Li, Haibo; Wang, Yibao; Luan, Changfeng

doi:10.7717/peerj-cs.1108

Cited by 5 publications

(3 citation statements)

References 23 publications

(20 reference statements)

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“…To face this issue, several publications work to connect energy consumption to energy production [25], [89], [90]. Therefore, short-term predictions became a relevant topic of interest in the electric power plant loads field [14], [91], [92], [93], [94]. Finally, the inclusion of EVs in household loads can also be considered in the case studies performed, even though they add another degree of uncertainty to the load due to their potential integration into energy hubs [95], [96].…”

Section: ) Solar Energymentioning

confidence: 99%

K-Means and Alternative Clustering Methods in Modern Power Systems

Miraftabzadeh,

Colombo,

Longo

et al. 2023

IEEE Access

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As power systems evolve by integrating renewable energy sources, distributed generation, and electric vehicles, the complexity of managing these systems increases. With the increase in data accessibility and advancements in computational capabilities, clustering algorithms, including K-means, are becoming essential tools for researchers in analyzing, optimizing, and modernizing power systems. This paper presents a comprehensive review of over 440 articles published through 2022, emphasizing the application of K-means clustering, a widely recognized and frequently used algorithm, along with its alternative clustering methods within modern power systems. The main contributions of this study include a bibliometric analysis to understand the historical development and wide-ranging applications of K-means clustering in power systems. This research also thoroughly examines K-means, its various variants, potential limitations, and advantages. Furthermore, the study explores alternative clustering algorithms that can complete or substitute K-means. Some prominent examples include K-medoids, Time-series K-means, BIRCH, Bayesian clustering, HDBSCAN, CLIQUE, SPECTRAL, SOMs, TICC, and swarm-based methods, broadening the understanding and applications of clustering methodologies in modern power systems. The paper highlights the wide-ranging applications of these techniques, from load forecasting and fault detection to power quality analysis and system security assessment. Throughout the examination, it has been observed that the number of publications employing clustering algorithms within modern power systems is following an exponential upward trend. This emphasizes the necessity for professionals to understand various clustering methods, including their benefits and potential challenges, to incorporate the most suitable ones into their studies.INDEX TERMS Clustering algorithms, K-means clustering, power systems. FIGURE 3. K-means clustering and power system bibliometric trend per country.

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Section: ) Solar Energymentioning

confidence: 99%

K-Means and Alternative Clustering Methods in Modern Power Systems

Miraftabzadeh,

Colombo,

Longo

et al. 2023

IEEE Access

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“…Therefore, scholars have proposed artificial intelligence methods. Artificial intelligence (AI) techniques have recently seen increased use in STLF, which include Support Vector Machine (SVM) [7], Long Short-Term Memory (LSTM) network [8], Bi-directional Long Short-Term Memory (BiLSTM) [9], Gated Recurrent Unit (GRU) [10] network, and the improved models of various scholars, etc., which can capture the non-linear characteristics of power loads better and significantly enhance the precision of load forecasting. BiGRU [11] can consider past and future known data and learn more feature information effectively.…”

Section: Introductionmentioning

confidence: 99%

An Improved WOA (Whale Optimization Algorithm)-Based CNN-BIGRU-CBAM Model and Its Application to Short-Term Power Load Forecasting

Dai,

Wang

2024

Energies

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The accuracy requirements for short-term power load forecasting have been increasing due to the rapid development of the electric power industry. Nevertheless, the short-term load exhibits both elasticity and instability characteristics, posing challenges for accurate load forecasting. Meanwhile, the traditional prediction model suffers from the issues of inadequate precision and inefficient training. In this work, a proposed model called IWOA-CNN-BIGRU-CBAM is introduced. To solve the problem of the Squeeze-and-Excitation (SE) attention mechanism’s inability to collect information in the spatial dimension effectively, the Convolutional Block Attention Module (CBAM) is firstly introduced as a replacement. This change aims to enhance the ability to capture location attributes. Subsequently, we propose an improved Whale Optimization Algorithm (IWOA) that addresses its limitations, such as heavy reliance on the initial solution and susceptibility to local optimum solutions. The proposed IWOA is also applied for the hyperparameter optimization of the Convolutional Neural Network–Bidirectional Gated Recurrent Unit–Convolutional Block Attention Module (CNN-BiGRU-CBAM) to improve the precision of predictions. Ultimately, applying the proposed model to forecast short-term power demand yields results that show that the CBAM effectively addresses the problem of the SE attention mechanism’s inability to capture spatial characteristics fully. The proposed IWOA exhibits a homogeneous dispersion of the initial population and an effective capability to identify the optimal solution. Compared to other models, the proposed model improves R2 by 0.00224, reduces the RMSE by 18.5781, and reduces MAE by 25.8940, and the model’s applicability and superiority are validated.

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“…In another work, the GRA is used to analyse the amount of energy consumption, emission and growth patterns from insufficient data [18]. Grey Relatıon Analysıs Modellıng Normally the GRA analysis involves five major steps [19][20][21][22] as shown in fig. 2.…”

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confidence: 99%

Optimal Energy Extraction of Non- Buoyant Type Wave Energy Converter Using Grey Relation Analysis

Nagulan

2023

tjjpt

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The wave energy converters involved in ocean wave energy harvesting are subjected to various natural calamities and thus obtaining the optimal energy extraction is a challenging task. It is normally noted that the energy extraction is maximum when the frequency of the ocean waves matches the natural frequency of the device. The natural frequency of the converter can be modified by tuning its dynamics, but it is intricate to perform in the ocean environment, so an alternative method should be adopted. In this work, the optimal electrical damping is performed to modify the frequency of the system to match the natural frequency of the incident wave. The grey relation analysis is carried out to find out the optimal damping value. Further, the obtained results are subjected to experimental verification and it is observed that the energy extraction is significantly improved. This approach is less complex and effective when compared with the other methods.

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Short-term power load forecasting based on gray relational analysis and support vector machine optimized by artificial bee colony algorithm

Cited by 5 publications

References 23 publications

K-Means and Alternative Clustering Methods in Modern Power Systems

K-Means and Alternative Clustering Methods in Modern Power Systems

An Improved WOA (Whale Optimization Algorithm)-Based CNN-BIGRU-CBAM Model and Its Application to Short-Term Power Load Forecasting

Optimal Energy Extraction of Non- Buoyant Type Wave Energy Converter Using Grey Relation Analysis

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