Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/28718
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Building Services Engineering | - |
dc.creator | Niu, J | en_US |
dc.creator | Liao, Z | en_US |
dc.date.accessioned | 2015-08-28T04:31:47Z | - |
dc.date.available | 2015-08-28T04:31:47Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/28718 | - |
dc.language.iso | en | en_US |
dc.publisher | Architectural Institute of Japan | en_US |
dc.rights | © 2018 Architectural Institute of Japan | en_US |
dc.rights | This is an Open Access article distributed under the terms of the Creative Commons Attribution-Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. | en_US |
dc.rights | The following publication Jianlei Niu & Zaiyi Liao (2002) Forecasting Residential Energy Demand in China: An approach to technology impacts, Journal of Asian Architecture and Building Engineering, 1(1), 95-103 is available at https://doi.org/10.3130/jaabe.1.95 | en_US |
dc.subject | Residential buildings | en_US |
dc.subject | Energy-demand forecasting | en_US |
dc.subject | Households model | en_US |
dc.subject | Building energy simulation | en_US |
dc.title | Forecasting residential energy demand in China | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 95 | en_US |
dc.identifier.epage | 103 | en_US |
dc.identifier.volume | 1 | en_US |
dc.identifier.issue | 1 | en_US |
dc.identifier.doi | 10.3130/jaabe.1.95 | en_US |
dcterms.abstract | China is undergoing rapid economic development, and experiencing increased energy consumption. An accurate prediction of residential energy demand is beneficial to both energy supply decision-making at the local level and energy policy makers at the national level. It provides the most likely trend of residential energy demand in the specified areas and how the trend may be controlled by technologies and policies. Complexity and difficulty exist regarding the forecasting of energy demand because there are too many variables and uncertainties that may have significant impact, and also because essential historical data regarding residential energy consumption is in most cases inadequate. Unlike most existing models, we have developed a multiple-level forecasting model, with a focus on the impacts of technologies. Essentially, there are four levels in this forecasting system: the household model, community model, city model, and national model. Each level of the model has its own focused variables so that other variables can be isolated to reduce the complexity and difficulty of model implementation. This paper outlines the framework of this forecasting model and details the two lowest levels: household and community level models. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Journal of Asian architecture and building engineering, 2002, v. 1, no. 1, p. 95-103 | en_US |
dcterms.isPartOf | Journal of Asian architecture and building engineering | en_US |
dcterms.issued | 2002 | - |
dc.identifier.rosgroupid | r08510 | - |
dc.description.ros | 2001-2002 > Academic research: refereed > Publication in refereed journal | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Niu_Forecasting_Residential_Energy.pdf | 571.07 kB | Adobe PDF | View/Open |
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