Mean shift densification of scarce data sets in short-term electric power load forecasting for special days

Rego, Liviane; Sumaili, Jean; Miranda, Vladimiro; Francês, Carlos Renato Lisboa; Silva, Marcelino; Santana, Ádamo Lima de

doi:10.1007/s00202-016-0424-z

Cited by 9 publications

(4 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multi Layer Perceptron (MLP) ANN (23) ( [? ], [14], [25], [42], [45], [78], [101], [110], [122], [124], [132], [156], [164], [171], [178], [187], [210], [211], [213], [228], [237], [242], [256]), Support Vector Machines (SVM) (21) ( [? ], [36], [53], [57], [65], [78], [79], [106], [115], [117], [122], [157], [159], [166], [187], [193], [203], [227], [240], [253], [256]), autoregressive integrated moving average (ARIMA) (13) ( [6], [19], [32], [42], [53], [78], …”

Section: Sms Resultsmentioning

confidence: 99%

Smart Grids Data Analysis: A Systematic Mapping Study

Rossi

Chren

2020

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

Article under review. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.The article is structured as follows. In section II, we provide an overview of Smart Grids concepts: mainly architecture and components of the SG infrastructure. In section III, we define the goals, needs, process and research questions for the Systematic Mapping Study on data analysis in the SG context. In section IV, we present the results from the overall SMS process, divided into answers to the main research questions set. In Section V, we discuss the main threats to validity of the current study. Section VI provides the final conclusions.

show abstract

Section: Sms Resultsmentioning

confidence: 99%

Smart Grids Data Analysis: A Systematic Mapping Study

Rossi

Chren

2020

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

show abstract

“…These candidates are then shifted towards regions of the highest density, identified using a kernel density estimate. In power system applications, Mean Shift could be beneficial for detecting areas of high energy consumption or demand hotspots [321], [322], [323], providing valuable insights for power distribution and load management strategies [324], [325], [326].…”

Section: ) Mean-shift Clusteringmentioning

confidence: 99%

K-Means and Alternative Clustering Methods in Modern Power Systems

Miraftabzadeh,

Colombo,

Longo

et al. 2023

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

“…Sometimes model performance suffers from the lack of data. In the case of ML approaches this can be compensated by the creation of virtual data through densification or latent information functions [184,185]. In engineering-based techniques insufficient data can be tackled by prioritization as well as the right choice of representative samples as done in [19,54,[186][187][188].…”

Section: Measures For Improvement Of Accuracymentioning

confidence: 99%

Modeling Energy Demand—A Systematic Literature Review

et al. 2021

View full text Add to dashboard Cite

In this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing literature reviews, in this comprehensive study all of the following aspects of energy demand models are analyzed: techniques, prediction accuracy, inputs, energy carrier, sector, temporal horizon, and spatial granularity. Readers benefit from easy access to a broad literature base and find decision support when choosing suitable data-model combinations for their projects. Results have been compiled in comprehensive figures and tables, providing a structured summary of the literature, and containing direct references to the analyzed articles. Drawbacks of techniques are discussed as well as countermeasures. The results show that among the articles, machine learning (ML) techniques are used the most, are mainly applied to short-term electricity forecasting on a regional level and rely on historic load as their main data source. Engineering-based models are less dependent on historic load data and cover appliance consumption on long temporal horizons. Metaheuristic and uncertainty techniques are often used in hybrid models. Statistical techniques are frequently used for energy demand modeling as well and often serve as benchmarks for other techniques. Among the articles, the accuracy measured by mean average percentage error (MAPE) proved to be on similar levels for all techniques. This review eases the reader into the subject matter by presenting the emphases that have been made in the current literature, suggesting future research directions, and providing the basis for quantitative testing of hypotheses regarding applicability and dominance of specific methods for sub-categories of demand modeling.

show abstract

Mean shift densification of scarce data sets in short-term electric power load forecasting for special days

Cited by 9 publications

References 37 publications

Smart Grids Data Analysis: A Systematic Mapping Study

Smart Grids Data Analysis: A Systematic Mapping Study

K-Means and Alternative Clustering Methods in Modern Power Systems

Modeling Energy Demand—A Systematic Literature Review

Contact Info

Product

Resources

About