Prediction Interval Estimation of Demand Curve in Electric Power Distribution System

Yamazaki, Tomohide; Wakao, Shinji; Ito, Hirokazu; Sano, Tsuneyo

doi:10.1002/eej.23036

Cited by 1 publication

(2 citation statements)

References 8 publications

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“…Clustering Historic energy demand [21,48,49,81,82,[91][92][93]109,114,118,155,163,232,[234][235][236]263,268,279,322,323,326,328,342,353,355,360,369,372,395,403,428,476] Weather data [21,82,[91][92][93]114,118,163,263,275,348,353,476] Calendar data [48,92,93,109,…”

Section: Methods Input Referencesmentioning

confidence: 99%

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Modeling Energy Demand—A Systematic Literature Review

et al. 2021

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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.

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Section: Methods Input Referencesmentioning

confidence: 99%

“…Historic energy demand [33,34,37,39,68,71,82,89,90,101,107,108,111,131,147,163,175,178,229,236,263,285,331,340,346,349,356,361,365,396,398,425,442,449,478] Weather data [37,39,68,82,89,101,107,147,163,175,183,229,263,340,349,356,396,...…”

Section: Instance Basedmentioning

confidence: 99%