Short term electricity load forecasting for institutional buildings
Yunsun Kim, Heung-gu Son, Sahm Kim
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.
Year of publication: |
2019
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Authors: | Kim, Yunsun ; Son, Heung-gu ; Kim, Sahm |
Published in: |
Energy reports. - Amsterdam [u.a.] : Elsevier, ISSN 2352-4847, ZDB-ID 2814795-9. - Vol. 5.2019, p. 1270-1280
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Subject: | ANN | ARIMA | GARCH | Institutional building | Peak load demand forecasting | Time series | Zeitreihenanalyse | Time series analysis | Theorie | Theory | Prognoseverfahren | Forecasting model | Lastmanagement | Demand-side management | Nachfrage | Demand | Elektrizität | Electricity |
Saved in:
freely available
Type of publication: | Article |
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Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
Language: | English |
Other identifiers: | 10.1016/j.egyr.2019.08.086 [DOI] hdl:10419/243668 [Handle] |
Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10012125400
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