A Building Information Model (BIM) and Artificial Neural Network (ANN) Based System for Personal Thermal Comfort Evaluation and Energy Efficient Design of Interior Space
1
School of Economics and Management, Tongji University, Shanghai 20092, China
2
International Business Administration Academic, Shanghai International Studies University, Shanghai 201620, China
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(18), 4972; https://doi.org/10.3390/su11184972
Received: 3 August 2019 / Revised: 3 September 2019 / Accepted: 3 September 2019 / Published: 11 September 2019
(This article belongs to the Special Issue Sustainable by Design: Towards a New Paradigm for the Built Environment)
It is crucial to evaluate indoor personal thermal comfort for a comfortable and green thermal environment. At present, the research on individual thermal comfort does not consider its implementation mode. Moreover, the improvement of energy saving efficiency under the premise of increasing human comfort is an urgent problem that needs to be solved. In this paper, we proposed a Building Information Model (BIM) and Artificial Neural Network (ANN) based system to solve this problem. The system consists of two parts including an ANN predictive model considering the Predicted Mean Vote (PMV) index, the persons’ position, and an innovative plugin of BIM to realize dynamic evaluation and energy efficient design. The ANN model has three layers, considering three environment parameters (air temperature, air humidity, and wind speed around the person), three human state parameters (human metabolism rate, clothing thermal resistance, and the body position) and four body parameters (gender, age, height, and weight) as inputs. The plugin provides two functions. One is to provide corresponding personal thermal comfort evaluation results with dynamic changes of parameters returned by Wireless Sensor Networks (WSN). The other one is to provide energy saving optimization suggestions for interior space design by simulating the energy consumption index of different design schemes. In the data test, the Mean Squared Error (MSE) of the established ANN model was about 0.39, while the MSE of traditional PMV model was about 2.1. The system realized the integration of thermal information and a building model, thereby providing guidance for the creation of a comfortable and green indoor environment.
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MDPI and ACS Style
Ma, G.; Liu, Y.; Shang, S. A Building Information Model (BIM) and Artificial Neural Network (ANN) Based System for Personal Thermal Comfort Evaluation and Energy Efficient Design of Interior Space. Sustainability 2019, 11, 4972.
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