Search

Abstract: Simple and effective building energy models are essentially needed for many applications, such as building performance diagnosis and optimal control, etc. The energy model involves two important parts of building components, i.e., building envelopes and building internal mass. This paper presents a methodology for parameter optimization of 3R2C thermal network model of building envelopes (composed of three resistances and two capacitances) based on frequency domain regression using genetic algorithm (GA). First, the theoretical frequency characteristics of heat transfer through building envelope are calculated using detailed physical description within the frequency range of concern. Second, the frequency characteristics of the simplified 3R2C model are calculated with random values of individual resistances and capacitances which constrain to total thermal resistance and capacitance. Then, the errors between the theoretical frequency characteristics and the frequency characteristics of the simplified model are calculated. Finally, GA estimator is developed to optimize the parameters of the simplified model, allowing the frequency responses of the simplified model match the actual heat transfer through building envelope the best. Various case studies are conducted also to validate the parameter optimization method of the simplified 3R2C model. The accuracy of simplified models for constructions of different weights is studied. [Copyright &y& Elsevier]

Abstract: Building thermal transfer models are essential to predict transient cooling or heating requirements for performance monitoring, diagnosis and control strategy analysis. Detailed physical models are time consuming and often not cost effective. Black box models require a significant amount of training data and may not always reflect the physical behaviors. In this study, a building is described using a simplified thermal network model. For the building envelope, the model parameters can be determined using easily available physical details. For building internal mass having thermal capacitance, including components such as furniture, partitions etc., it is very difficult to obtain detailed physical properties. To overcome this problem, this paper proposes to present the building internal mass with a thermal network structure of lumped thermal mass and estimate the lumped parameters using operation data. A genetic algorithm estimator is developed to estimate the lumped internal thermal parameters of the building thermal network model using the operation data collected from site monitoring. The simplified dynamic model of building internal mass is validated in different weather conditions. [Copyright &y& Elsevier]

The energy efficiency of traditional PCM wall is low in summer since the PCM storing heat in the daytime will become a secondary heat-source and release the heat into the room again at night. In this paper, a novel pipe-encapsulated PCM wall system for self-activated heat removal is proposed. It integrates PCM, nocturnal radiation cooling and gravity heat-pipe technology, and can realize automatic heat insulation and self-activated removal for buildings. Three simplified models for pipe-encapsulated PCM are developed to predict its thermal characteristics. Model Parameters are determined and identified by using genetic algorithm. The pipe-encapsulated PCM is prepared by RT28 and expanded graphite. An experiment setup is established for model validation. Results shows that the 4R2C PCM model are preferable since the identified parameters are relatively less and the accuracy is also acceptable. Furthermore, a simple application about pipe-encapsulated PCM wall is proposed based on the coupled wall model with 4R2C PCM model. Compared with conventional wall, the internal surface accumulated heat transfer of the pipe-encapsulated PCM wall with night active heat removal in 7 days can be reduced by 74.5% in hot climate. It shows great application potential for energy-saving in buildings. [ABSTRACT FROM AUTHOR]