Thermal Environment Optimization and Energy Saving of Residential Buildings under the Demand of Low-Carbon Operation

Abstract

Improving the thermal environment quality of residential buildings under the demand of low-carbon operation is beneficial to residents' physical and mental health. The existing research has not dynamically simulated the indoor thermal environment of residential buildings for longer time, nor has linked the low-carbon demand with the thermal environment optimization method of residential buildings. Therefore, this study relates to the optimization strategies and energy-saving technologies on thermal environment of residential buildings under the demand of low-carbon operation. Firstly, it analyzes the indoor thermal environment of residential buildings based on the external disturbance conditions, internal disturbance conditions and ventilation, developed the heat transfer model for the facade of residential buildings, and calculates the indoor air temperature. Then, it explores the indoor heat distribution mechanism of residential buildings under the heating mode of concentrated heat source, and analyzes the generation process of hot jet above the heat source and the influence of concentrated heat source on temperature distribution. Finally, the study provides the calculation method of carbon emission from common energy sources in residential buildings. Experimental results verify the effectiveness of the proposed method, and the corresponding suggestions on thermal environment optimization and energy saving for residential buildings are provided.

The Role of the Internal Heat Gains for Artificial Lighting on the Energy Performance of Buildings

“…With the transposition of the European Directives on energy efficiency and performance, all new buildings or buildings undergoing to important energy retrofit interventions will have to be nZEB (nearly Zero Energy Building). Among the requirements to achieve this goal [16], artificial lighting can play an important role, with particular reference to the installation of smart systems able to exploit natural light (daylighting) and reduce energy consumption for artificial lighting system.…”

“…The next step will be to overcome and improve the concept of an ultra-highenergy, intelligent performance building. The evolution of building automation and smart building is represented by the cognitive building able to learn in predictive function the amount of information acquired from the data associated with sensors and actuators, and able to adapt to the actual needs of users [16]. For this further area of study, a working group has recently been created: UNI/CT058/GL03 coordinated by ENEA for the definition of the indicators of smart cities and communities and its connections to the scale of local and national networks and platforms to be included within the regulatory framework of the UNI/TS.…”