Load forecasting for smart grid using non-linear model in Hadoop distributed file system

Jees, S Arun; Venugopal, G.

doi:10.1007/s10586-018-1997-2

Cited by 9 publications

(2 citation statements)

References 20 publications

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“…Using Spark MLlib, Galicia et al developed a forecasting method that has been tested on a series of real-time electricity demands in Spain, with a 10-minute interval between each measurement [12]. Jees et al use the k-means algorithm, grey relational degree, decision tree algorithm, and support vector machine to predict an accurate prediction result in the Hadoop framework [19]. Duenas et al describe an online failure prediction system using a random forest predictor on Apache Spark [11].…”

Section: Related Workmentioning

confidence: 99%

A RS-BOLGBM Algorithm Based on LightGBM for Supercomputing Center Job Running Status Prediction

Cao,

Tang,

Shi

et al. 2024

Preprint

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In recent years, more and more large-scale high-performance applications have been submitted to the Supercomputing Center for processing. These jobs often require intensive computations, and any running failure will lead to a lot of wasted costs. Therefore, the effective prediction of job running status will help to improve the resource management efficiency of the Supercomputing Center. In this paper, we first propose a combinatorial feature selection method combining ReliefF and SelectKBest, which can effectively obtain job running state features. Then, an improved LightGBM algorithm based on Bayesian hyperparameter optimization is proposed. This approach can further enhance the prediction accuracy and tuning efficiency. Thirdly, we achieve a RS-BOLGBM algorithm based on LightGBM for the Supercomputing Center job running status prediction on Spark. The experimental results show that our proposed RS-BOLGBM algorithm has good prediction accuracy on the common SWF (standard workloads format) dataset of the Supercomputing Center. Our proposed RS-BOLGBM also has good running efficiency on the Spark cluster.

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Section: Related Workmentioning

confidence: 99%

A RS-BOLGBM Algorithm Based on LightGBM for Supercomputing Center Job Running Status Prediction

Cao,

Tang,

Shi

et al. 2024

Preprint

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“…The landscape has changed, and users have now become an active part of the generation phase, reflecting a more decentralized and participatory approach to energy management. This new structure has led to the creation of energy hubs, allowing consumers to function as power plants through renewable distributed generation [87], [88]. Still, they have to face several issues related to both load-side and source-side.…”

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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LPM2DA: a lattice-based privacy-preserving multi-functional and multi-dimensional data aggregation scheme for smart grid

2021

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Load forecasting for smart grid using non-linear model in Hadoop distributed file system

Cited by 9 publications

References 20 publications

A RS-BOLGBM Algorithm Based on LightGBM for Supercomputing Center Job Running Status Prediction

A RS-BOLGBM Algorithm Based on LightGBM for Supercomputing Center Job Running Status Prediction

K-Means and Alternative Clustering Methods in Modern Power Systems

LPM2DA: a lattice-based privacy-preserving multi-functional and multi-dimensional data aggregation scheme for smart grid

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