Next Article in Journal
Seasonal and Diurnal Characteristics of Land Surface Temperature and Major Explanatory Factors in Harris County, Texas
Next Article in Special Issue
Assessing the Effects of Urban Morphology Parameters on Microclimate in Singapore to Control the Urban Heat Island Effect
Previous Article in Journal
Energy Policies and Sustainable Management of Energy Sources
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Sustainability 2017, 9(12), 2288; https://doi.org/10.3390/su9122288

Passive Optimization Design Based on Particle Swarm Optimization in Rural Buildings of the Hot Summer and Warm Winter Zone of China

School of Environment Science and Engineering, Tianjin University, 92 Weijin Road, Tianjin 300072, China
*
Author to whom correspondence should be addressed.
Received: 14 November 2017 / Revised: 4 December 2017 / Accepted: 6 December 2017 / Published: 13 December 2017
(This article belongs to the Collection Sustainable Built Environment)
Full-Text   |   PDF [9716 KB, uploaded 13 December 2017]   |  

Abstract

The development of green building is an important way to solve the environmental problems of China’s construction industry. Energy conservation and energy utilization are important for the green building evaluation criteria (GBEC). The objective of this study is to evaluate the quantitative relationship between building shape parameter, envelope parameters, shading system, courtyard and the energy consumption (EC) as well as the impact on indoor thermal comfort of rural residential buildings in the hot summer and warm winter zone (HWWZ). Taking Quanzhou (Fujian Province of China) as an example, based on the field investigation, EnergyPlus is used to build the building performance model. In addition, the classical particle swarm optimization algorithm in GenOpt software is used to optimize the various factors affecting the EC. Single-objective optimization has provided guidance to the multi-dimensional optimization and regression analysis is used to find the effects of a single input variable on an output variable. Results shows that the energy saving rate of an optimized rural residence is about 26–30% corresponding to the existing rural residence. Moreover, the payback period is about 20 years. A simple case study is used to demonstrate the accuracy of the proposed optimization analysis. The optimization can be used to guide the design of new rural construction in the area and the energy saving transformation of the existing rural houses, which can help to achieve the purpose of energy saving and comfort. View Full-Text
Keywords: rural residence; green building; energy consumption; multidimensional optimization; particle swarm optimization; regression analysis rural residence; green building; energy consumption; multidimensional optimization; particle swarm optimization; regression analysis
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Lu, S.; Wang, R.; Zheng, S. Passive Optimization Design Based on Particle Swarm Optimization in Rural Buildings of the Hot Summer and Warm Winter Zone of China. Sustainability 2017, 9, 2288.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sustainability EISSN 2071-1050 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top