Residential demand forecasting with solar-battery systems: A survey-less approach

Percy, SD; Aldeen, M; Berry, A

Residential demand forecasting with solar-battery systems: A survey-less approach

Percy, SD Aldeen, M Berry, A

Permalink

Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Sustainable Energy, 2018, 9, (4), pp. 1499-1507
Issue Date:: 2018-10-01

Closed Access

	Filename	Description	Size
	Residential_Demand_Forecasting_With_Solar-Battery_Systems_A_Survey-Less_Approach.pdf	Published version	650.46 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Percy, SD
dc.contributor.author	Aldeen, M
dc.contributor.author	Berry, A https://orcid.org/0000-0002-2499-9491
dc.date.accessioned	2022-09-01T07:21:46Z
dc.date.available	2022-09-01T07:21:46Z
dc.date.issued	2018-10-01
dc.identifier.citation	IEEE Transactions on Sustainable Energy, 2018, 9, (4), pp. 1499-1507
dc.identifier.issn	1949-3029
dc.identifier.issn	1949-3037
dc.identifier.uri	http://hdl.handle.net/10453/161202
dc.description.abstract	Due to the push to reduce greenhouse gas emissions in the residential sector, new precincts are likely to include a higher uptake of solar and battery systems. One fundamental tool required to assess the impact and cost of solar and battery systems in residential precincts is a reliable model that can simulate electricity demand for individual homes. That model should capture the time-series complexity and the unique demographic composition of a precinct. In response, this research contributes to state-of-the-art demand forecasting by presenting a time-series, survey-less, demographically focused model formulation. The model achieves the survey-less feature by substituting household surveys for geodemographic profiling data, avoiding comprehensive surveying used in other research. This increases the usability for developers and network operators. The research applies the adaptive boost regression tree algorithm as an alternative to traditional methods, such as feed forward neural network (FFANN). The results investigate three case study precincts and show that this algorithm improves over an FFANN in aspects such as peak event estimation and daily variability, achieving an R2 value of 0.86. Finally, the research implements an MILP battery model and demonstrates the demand model is robust to provide real value battery system design, cost, and impact estimations.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Sustainable Energy
dc.relation.isbasedon	10.1109/TSTE.2018.2791982
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0906 Electrical and Electronic Engineering, 0915 Interdisciplinary Engineering
dc.title	Residential demand forecasting with solar-battery systems: A survey-less approach
dc.type	Journal Article
utslib.citation.volume	9
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0915 Interdisciplinary Engineering
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/A/DRsch The Data Science Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2022-09-01T07:21:45Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	9
utslib.citation.issue	4

Abstract:

Due to the push to reduce greenhouse gas emissions in the residential sector, new precincts are likely to include a higher uptake of solar and battery systems. One fundamental tool required to assess the impact and cost of solar and battery systems in residential precincts is a reliable model that can simulate electricity demand for individual homes. That model should capture the time-series complexity and the unique demographic composition of a precinct. In response, this research contributes to state-of-the-art demand forecasting by presenting a time-series, survey-less, demographically focused model formulation. The model achieves the survey-less feature by substituting household surveys for geodemographic profiling data, avoiding comprehensive surveying used in other research. This increases the usability for developers and network operators. The research applies the adaptive boost regression tree algorithm as an alternative to traditional methods, such as feed forward neural network (FFANN). The results investigate three case study precincts and show that this algorithm improves over an FFANN in aspects such as peak event estimation and daily variability, achieving an R2 value of 0.86. Finally, the research implements an MILP battery model and demonstrates the demand model is robust to provide real value battery system design, cost, and impact estimations.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/161202