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A Decision Support Tool for Designin...

Sun, Shilin.

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A Decision Support Tool for Designing Energy-Efficient Residential Buildings at the Early Planning and Design Stage.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A Decision Support Tool for Designing Energy-Efficient Residential Buildings at the Early Planning and Design Stage./
作者:	Sun, Shilin.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	118 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-06, Section: B.
Contained By:	Dissertations Abstracts International82-06B.
標題:	Sustainability. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28255678
ISBN:	9798698575993

A Decision Support Tool for Designing Energy-Efficient Residential Buildings at the Early Planning and Design Stage.
Sun, Shilin.

A Decision Support Tool for Designing Energy-Efficient Residential Buildings at the Early Planning and Design Stage. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 118 p.

Source: Dissertations Abstracts International, Volume: 82-06, Section: B.

Thesis (D.Engr.)--The George Washington University, 2021.

This item must not be sold to any third party vendors.

Predicting building energy consumption is essential at the design stage as it assists in estimating the costs of building operation. Today, most of the current building energy predictions are conducted on a building energy simulation software. Such simulation software is commonly preprogrammed to perform detailed engineering calculations. However, due to the unavailability of detailed building factors at the early design stage, the building energy simulation software tends to yield a large discrepancy between predicted results and actual consumption.Machine learning is a promising technique in the domains of classification and prediction. Algorithms like Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM), have been extensively adopted as predictive tools in various areas such as logistics, retail, financial services, and telecommunication.This research presents a new approach based on machine learning algorithms to reduce buildings' energy costs during operation. This proposed energy predictive model combines four algorithms, including multivariate regression (MVR), sequential minimal optimization (SMO), ANN, and RF. The model built with hybrid algorithm provides an accurate and rapid forecast on energy consumption. With this model, designers can quantify the energy consumption of different design alternatives at the early design stage. Building energy costs can be reduced by selecting the design with the minimum energy consumption, among other options.The predictive model is developed and tested based on a large set of data collected by the Office of Energy Efficiency, Natural Resources of Canada, the Government of Canada. The performance of the proposed model is compared with the models developed in the previous literature, and the application of the model is demonstrated by using the building design examples.

ISBN: 9798698575993Subjects--Topical Terms:

1029978
Sustainability.
Subjects--Index Terms:

Building energy efficiency

A Decision Support Tool for Designing Energy-Efficient Residential Buildings at the Early Planning and Design Stage.
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