Short-term residential load forecasting: Impact of calendar effects and forecast granularity

Lusis, P; Khalilpour, KR; Andrew, L; Liebman, A

Short-term residential load forecasting: Impact of calendar effects and forecast granularity

Lusis, P Khalilpour, KR Andrew, L Liebman, A

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Applied Energy, 2017, 205, (C), pp. 654-669
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Lusis, P
dc.contributor.author	Khalilpour, KR
dc.contributor.author	Andrew, L
dc.contributor.author	Liebman, A
dc.date.accessioned	2022-08-29T02:55:52Z
dc.date.available	2022-08-29T02:55:52Z
dc.date.issued	2017-01-01
dc.identifier.citation	Applied Energy, 2017, 205, (C), pp. 654-669
dc.identifier.issn	0306-2619
dc.identifier.issn	1872-9118
dc.identifier.uri	http://hdl.handle.net/10453/161008
dc.description.abstract	Literature is rich in methodologies for “aggregated” load forecasting which has helped electricity network operators and retailers in optimal planning and scheduling. The recent increase in the uptake of distributed generation and storage systems has generated new demand for “disaggregated” load forecasting for a single-customer or even down at an appliance level. Access to high resolution data from smart meters has enabled the research community to assess conventional load forecasting techniques and develop new forecasting strategies suitable for demand-side disaggregated loads. This paper studies how calendar effects, forecasting granularity and the length of the training set affect the accuracy of a day-ahead load forecast for residential customers. Root mean square error (RMSE) and normalized RMSE were used as forecast error metrics. Regression trees, neural networks, and support vector regression yielded similar average RMSE results, but statistical analysis showed that regression trees technique is significantly better. The use of historical load profiles with daily and weekly seasonality, combined with weather data, leaves the explicit calendar effects a very low predictive power. In the setting studied here, it was shown that forecast errors can be reduced by using a coarser forecast granularity. It was also found that one year of historical data is sufficient to develop a load forecast model for residential customers as a further increase in training dataset has a marginal benefit.
dc.language	English
dc.publisher	Elsevier
dc.relation.ispartof	Applied Energy
dc.relation.isbasedon	10.1016/j.apenergy.2017.07.114
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	09 Engineering, 14 Economics
dc.subject.classification	Energy
dc.title	Short-term residential load forecasting: Impact of calendar effects and forecast granularity
dc.type	Journal Article
utslib.citation.volume	205
utslib.for	09 Engineering
utslib.for	14 Economics
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Professional Practice and Leadership
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-08-29T02:55:50Z
pubs.issue	C
pubs.publication-status	Published
pubs.volume	205
utslib.citation.issue	C

Abstract:

Literature is rich in methodologies for “aggregated” load forecasting which has helped electricity network operators and retailers in optimal planning and scheduling. The recent increase in the uptake of distributed generation and storage systems has generated new demand for “disaggregated” load forecasting for a single-customer or even down at an appliance level. Access to high resolution data from smart meters has enabled the research community to assess conventional load forecasting techniques and develop new forecasting strategies suitable for demand-side disaggregated loads. This paper studies how calendar effects, forecasting granularity and the length of the training set affect the accuracy of a day-ahead load forecast for residential customers. Root mean square error (RMSE) and normalized RMSE were used as forecast error metrics. Regression trees, neural networks, and support vector regression yielded similar average RMSE results, but statistical analysis showed that regression trees technique is significantly better. The use of historical load profiles with daily and weekly seasonality, combined with weather data, leaves the explicit calendar effects a very low predictive power. In the setting studied here, it was shown that forecast errors can be reduced by using a coarser forecast granularity. It was also found that one year of historical data is sufficient to develop a load forecast model for residential customers as a further increase in training dataset has a marginal benefit.

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http://hdl.handle.net/10453/161008