Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/243668 
Year of Publication: 
2019
Citation: 
[Journal:] Energy Reports [ISSN:] 2352-4847 [Volume:] 5 [Publisher:] Elsevier [Place:] Amsterdam [Year:] 2019 [Pages:] 1270-1280
Publisher: 
Elsevier, Amsterdam
Abstract: 
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.
Subjects: 
ANN
ARIMA
GARCH
Institutional building
Peak load demand forecasting
Time series
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
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