Artikel
Short term electricity load forecasting for institutional buildings
Peak load demand forecasting is important in building unit sectors, as climate change, technological development, and energy policies are causing an increase in peak demand. Thus, accurate peak load forecasting is a critical role in preventing a blackout or loss of energy. This paper presents a study forecasting peak load demand for an institutional building in Seoul. The dataset were collected from campus area consisting of 23 buildings. ARIMA models, ARIMA-GARCH models, multiple seasonal exponential smoothing, and ANN models are used. We find an optimal model with moving window simulations and step-ahead forecasts. Also, including weather and holiday variables is crucial to predict peak load demand. The ANN model with external variables (NARX) worked best for 1-h to 1-d ahead forecasting.
- Language
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Englisch
- Bibliographic citation
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Journal: Energy Reports ; ISSN: 2352-4847 ; Volume: 5 ; Year: 2019 ; Pages: 1270-1280 ; Amsterdam: Elsevier
- Classification
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Wirtschaft
- Subject
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ANN
ARIMA
GARCH
Institutional building
Peak load demand forecasting
Time series
- Event
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Geistige Schöpfung
- (who)
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Kim, Yunsun
Son, Heung-gu
Kim, Sahm
- Event
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Veröffentlichung
- (who)
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Elsevier
- (where)
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Amsterdam
- (when)
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2019
- DOI
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doi:10.1016/j.egyr.2019.08.086
- Handle
- Last update
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10.03.2025, 11:44 AM CET
Data provider
This object is provided by:
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Kim, Yunsun
- Son, Heung-gu
- Kim, Sahm
- Elsevier
Time of origin
- 2019
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