Simulations of building energy end use by artificial neural networks

Pao Yue-kong Library Electronic Theses Database

Simulations of building energy end use by artificial neural networks

 

Author: Wong, Hon-cheung
Title: Simulations of building energy end use by artificial neural networks
Year: 2001
Subject: Buildings -- Energy consumption
Neural networks (Computer science)
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Dept. of Building Services Engineering
Pages: 1 v. (various pagings) : ill. ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1597800
URI: http://theses.lib.polyu.edu.hk/handle/200/3639
Abstract: Simulation is always an attractive challenge for researchers. Precise simulation of electricity consumption is crucial for planning and implementing energy policies, and energy saving measures. Artificial neural network technique has a great potential for energy consumption prediction. The aim of this study is to identify the feasibility of using artificial neural networks to simulate energy end-use in local buildings. Developing and executing neural network models in a convenient computer spreadsheet environment of 'Microsoft Excel' together with a 'Microsoft Excel VBA' programme is demonstrated and encouraged. The computer spreadsheet software is mainly used as a data management tool, and the computer programme is used to build up, train and test neural network models. This approach facilitates manipulation and modification of developed models. Several potential preliminary models are developed for simulation of the electricity end uses in public and private residential buildings. Data sets released from the Electrical and Mechanical Services Department in the period from 1984 to 1995 are used for training of neural network models, and the trained model is tested with the data set of the year of 1996. All aspects of neural networks including training method, key parameters, and configurations are explored. Through tests, two optimal models emerge. Despite the small data sets for calibration, the performances of optimal models are comparatively acceptable. The developed models have to be updated upon more data are ready. The same approach is also utilized to develop a neural network for predicting electricity consumption of a chiller plant. The performance is considered acceptable. The approach for development of neural networks presented in this dissertation can be applied in other energy predictions.

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