A Novel Method of BP Neural Network Based Green Building Design—The Case of Hotel Buildings in Hot Summer and Cold Winter Region of China

Bin Xu,Xiang Yuan

doi:10.3390/su14042444

Abstract

With the advent of the big data era, architectural design gradually tends to become more quantified and intelligent. This study proposes a novel green design method for energy-saving buildings based on a BP neural network. This study changed the traditional trial–error mode by evaluating energy consumption based on design performance parameters such as building shape, space, and interface. Instead, energy consumption quota values obtained from statistical data, as well as thermal parameters and energy system parameters in energy-saving standards, were taken as input parameters, and then the design scheme of building shape can be obtained through BP neural network technology. Based on data of 61 hotel buildings in a representative city among a hot summer and cold winter climate zone, the BP neural network model is established to control the building design variables, with 41 kgce/m2·a as its energy-saving design target. Through the energy consumption quota, the trained BP network is applied to predict the optimal architectural design parameters, including the building orientation angle, shape coefficient, window–wall ratio, etc., for twelve building typologies in an area range of 5000~60,000 m2. With recommended control thresholds of quantifiable architectural design elements obtained, this research can provide effective design decision-making suggestions for architects.

Highlights

The concept of green ecological architecture design has won increasing popularity.the energy-saving concept is one of the important elements for green ecological architecture
The design objective is to statistically analyze the energy consumption statistical The design objective is to statistically analyze energyenergy consumption statistical inindicators of various local buildings according to thethe building consumption values dicators of data, various local to the building values in the basic obtain thebuildings building according energy consumption quotaenergy throughconsumption the analysis model, in the data, obtain the building energy consumption through the analysis and putbasic forward reasonable design target parameters through quota organically combining with model, put forward reasonable parameters through organically comthe localand energy-saving standards anddesign typicaltarget buildings
This study put forward and set up an energy-saving building design method based on Data mining (DM) technology

Summary

Introduction

The concept of green ecological architecture design has won increasing popularity.the energy-saving concept is one of the important elements for green ecological architecture. As an important factor affecting building energy consumption, the ontological design of a building, such as its spatial form and functional layout, is the most direct and effective link to achieve building energy saving with the lowest cost. It can create a green and healthy space environment for users. In the era of big data, the traditional architectural design methods and design standards can only provide result-oriented static design parameters, which can no longer meet the requirements of scientific, refined, and rapid development of information technology for the discipline of architectural design.

Methods

Results

Conclusion