Estimation of Energy Savings for Building Retrofits Using Neural Networks-Reference-Cited by-同舟云学术

Estimation of Energy Savings for Building Retrofits Using Neural Networks

Published:1998-08-01 Issue:3 Volume:120 Page:211-216
ISSN:0199-6231
Container-title:Journal of Solar Energy Engineering
language:en
Short-container-title:

Author:

Krarti M.¹,Kreider J. F.¹,Cohen D.²,Curtiss P.²

Affiliation:

1. Joint Center for Energy Management, University of Colorado, Boulder, CO 90309-0428

2. Architectural Energy Corporation, 2540 Frontier Avenue, Boulder, CO 80301

Abstract

This paper overviews some applications of neural networks (NNs) to estimate energy and demand savings from retrofits of commercial buildings. First, a brief background information on NNs is provided. Then, three specific case studies are described to illustrate how and when NNs can be used successfully to determine energy savings due to the implementation of various energy conservation measures in existing commercial buildings.

Publisher

ASME International

Subject

Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment

Link

http://asmedigitalcollection.asme.org/solarenergyengineering/article-pdf/120/3/211/5652152/211_1.pdf

Reference18 articles.

1. Anstett M. , and KreiderJ. F., 1993, “Application of Artificial Neural Networks to Commercial Energy Use Prediction,” ASHRAE Transactions, Vol. 99, Pt. 1, pp. 505–517.

2. Bloem, H., deGraaf, M., Kreider, J. F., and Norle´n, U., 1998, “The First System Identification Competition,” Proceedings of the ASME ISEC, Albuquerque, NM.

3. Cohen, D., and Krarti, M., 1995, “A Neural Network Modeling Approach Applied to Energy Conservation Retrofits,” Building Simulation Fourth International Conference Proceedings, Madison, WI, pp. 423–430.

4. Cohen, D., and Krarti, M., 1997, “Neural Network Modeling of Measured Data to Predict Building Energy System Retrofit Savings,” Proceedings of the ASME ISEC, Washington, D.C., pp. 27–38.

5. Curtiss P. S. , 1997, “Examples of Neural Networks Used for Building System Control and Energy Management,” ASHRAE Transactions, Vol. 103, Pt. 2, pp. 909–913.

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