Academic Journal
Developing a whole building cooling energy forecasting model for on-line operation optimization using proactive system identification.
| Title: | Developing a whole building cooling energy forecasting model for on-line operation optimization using proactive system identification. |
|---|---|
| Authors: | Li, Xiwang1 xl63@drexel.edu, Wen, Jin1 jw325@drexel.edu, Bai, Er-Wei2 erwei@engineering.uiowa.edu |
| Source: | Applied Energy. Feb2016, Vol. 164, p69-88. 20p. |
| Subject Terms: | *Energy consumption of buildings, *Power resources forecasting, *Smart power grids, *Computational fluid dynamics, *Nonlinear systems, *Comparative studies |
| Abstract: | Optimal automatic operation of buildings and their subsystems in responding to signals from a smart grid is essential to reduce energy demand, and to improve the power resilience. In order to achieve such automatic operation, high fidelity and computationally efficiency whole building energy forecasting models are needed. Currently, data-driven (black box) models and hybrid (grey box) models are commonly used in model based building control. However, typical black box models often require long training period and are bounded to building operation conditions during the training period. On the other hand, creating a grey box model often requires (a) long calculation time due to parameter optimization process; and (b) expert knowledge during the model development process. This paper attempts to quantitatively evaluate the impacts of two significant system characteristics: system nonlinearity and response time, on the accuracy of the model developed by a system identification process. A general methodology for building energy forecasting model development is then developed. How to adapt the system identification process based on these two characteristics is also studied. A set of comparison criteria are then proposed to evaluate the energy forecasting models generated from the adapted system identification process against other methods reported in the literature, including Resistance and Capacitance method, Support Vector Regression method, Artificial Neural Networks method, and N4SID subspace algorithm. Two commercial buildings: a small and a medium commercial building, with varying chiller nonlinearity, are simulated using EnergyPlus in lieu of real buildings for model development and evaluation. The results from this study show that the adapted system identification process is capable of significantly improve the performance of the energy forecasting model, which is more accurate and more extendable under both of the noise-free and noisy conditions than those models generated by other methods. [ABSTRACT FROM AUTHOR] |
| Database: | Academic Search Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://www.doi.org/10.1016/j.apenergy.2015.12.002? Name: ScienceDirect (all content) (s7799221) Category: fullText Text: View record from ScienceDirect MouseOverText: View record from ScienceDirect |
|---|---|
| Header | DbId: asx DbLabel: Academic Search Index An: 112264598 RelevancyScore: 1215 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1215.30798339844 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Developing a whole building cooling energy forecasting model for on-line operation optimization using proactive system identification. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Li%2C+Xiwang%22">Li, Xiwang</searchLink><relatesTo>1</relatesTo><i> xl63@drexel.edu</i><br /><searchLink fieldCode="AR" term="%22Wen%2C+Jin%22">Wen, Jin</searchLink><relatesTo>1</relatesTo><i> jw325@drexel.edu</i><br /><searchLink fieldCode="AR" term="%22Bai%2C+Er-Wei%22">Bai, Er-Wei</searchLink><relatesTo>2</relatesTo><i> erwei@engineering.uiowa.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Applied+Energy%22">Applied Energy</searchLink>. Feb2016, Vol. 164, p69-88. 20p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Energy+consumption+of+buildings%22">Energy consumption of buildings</searchLink><br />*<searchLink fieldCode="DE" term="%22Power+resources+forecasting%22">Power resources forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Smart+power+grids%22">Smart power grids</searchLink><br />*<searchLink fieldCode="DE" term="%22Computational+fluid+dynamics%22">Computational fluid dynamics</searchLink><br />*<searchLink fieldCode="DE" term="%22Nonlinear+systems%22">Nonlinear systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Comparative+studies%22">Comparative studies</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Optimal automatic operation of buildings and their subsystems in responding to signals from a smart grid is essential to reduce energy demand, and to improve the power resilience. In order to achieve such automatic operation, high fidelity and computationally efficiency whole building energy forecasting models are needed. Currently, data-driven (black box) models and hybrid (grey box) models are commonly used in model based building control. However, typical black box models often require long training period and are bounded to building operation conditions during the training period. On the other hand, creating a grey box model often requires (a) long calculation time due to parameter optimization process; and (b) expert knowledge during the model development process. This paper attempts to quantitatively evaluate the impacts of two significant system characteristics: system nonlinearity and response time, on the accuracy of the model developed by a system identification process. A general methodology for building energy forecasting model development is then developed. How to adapt the system identification process based on these two characteristics is also studied. A set of comparison criteria are then proposed to evaluate the energy forecasting models generated from the adapted system identification process against other methods reported in the literature, including Resistance and Capacitance method, Support Vector Regression method, Artificial Neural Networks method, and N4SID subspace algorithm. Two commercial buildings: a small and a medium commercial building, with varying chiller nonlinearity, are simulated using EnergyPlus in lieu of real buildings for model development and evaluation. The results from this study show that the adapted system identification process is capable of significantly improve the performance of the energy forecasting model, which is more accurate and more extendable under both of the noise-free and noisy conditions than those models generated by other methods. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asx&AN=112264598 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.apenergy.2015.12.002 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 69 Subjects: – SubjectFull: Energy consumption of buildings Type: general – SubjectFull: Power resources forecasting Type: general – SubjectFull: Smart power grids Type: general – SubjectFull: Computational fluid dynamics Type: general – SubjectFull: Nonlinear systems Type: general – SubjectFull: Comparative studies Type: general Titles: – TitleFull: Developing a whole building cooling energy forecasting model for on-line operation optimization using proactive system identification. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li, Xiwang – PersonEntity: Name: NameFull: Wen, Jin – PersonEntity: Name: NameFull: Bai, Er-Wei IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 02 Text: Feb2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 03062619 Numbering: – Type: volume Value: 164 Titles: – TitleFull: Applied Energy Type: main |
| ResultId | 1 |