• Energy Research

Abstract Several conflicting criteria exist in building design optimization, especially energy consumption and indoor environment thermal performance. This paper presents a novel multi-objective optimization model that can assist designers in green building design. The Pareto solution was used to obtain a set of optimal solutions for building design optimization, and uses an improved multi-objective genetic algorithm (NSGA-II) as a theoretical basis for building design multi-objective optimization model. Based on the simulation data on energy consumption and indoor thermal comfort, the study also used a simulation-based improved back-propagation (BP) network which is optimized by a genetic algorithm (GA) to characterize building behavior, and then establishes a GA–BP network model for rapidly predicting the energy consumption and indoor thermal comfort status of residential buildings; Third, the building design multi-objective optimization model was established by using the GA–BP network as a fitness function of the multi-objective Genetic Algorithm (NSGA-II); Finally, a case study is presented with the aid of the multi-objective approach in which dozens of potential designs are revealed for a typical building design in China, with a wide range of trade-offs between thermal comfort and energy consumption.

Abstract Pipe-embedded radiant systems have the potential to reduce building energy consumption and provide better thermal comfort. Understanding and quantifying their dynamic thermal performance (cooling capacity, surface temperature) are important when designing and controlling such systems. Compared to the existing studies, this paper developed a comprehensive response factor method to calculate the dynamic performance for pipe-embedded radiant systems. Firstly, we used the state-space method to calculate the response factors. Secondly, the calculated response factors were verified to follow the heat balance principle. Afterwards, we found the response factor method has good accuracy when compared with the ISO or CFD method. Moreover, we also improved the response factor method to quantify the impact of additional heat on the dynamic performance. In addition, a parametric simulation considering six impact factors was conducted, from which we arrived at a response factor database for pipe-embedded radiant systems. The main advantage of the response factor method is that it does not require complicated iteration process. Therefore, it could possibly be used for the dynamic simulation or cooling load calculation for pipe-embedded radiant systems.

Abstract Compared with all-air system, the cooling load dynamics for integration operation of radiant ceiling panel (RCP) and dedicated outdoor air system (DOAS) is more complex, and there is a lack of practical cooling load calculation method in current standards and guidebooks. This study aims to explore their cooling load dynamics and develop a simplified calculation method for such integrated system. A heat balance based model to calculate the cooling load for the integrated system was established. The results showed that RCP with DOAS removed more room heat gain than all-air system at the same indoor air temperature set-point, and the peak cooling load of RCP with DOAS is 16% larger. It was also found that ignoring DOAS in cooling load calculation could make the results 6% larger than DOAS integrated. In addition, there is a condensation risk if RCP alone was used to maintain air set-point temperature. To help the application of the integrated system at design stage, we proposed cooling load correction coefficient rrad as the cooling load difference between radiant and all-air system, and the cooling load split fraction rDOAS for DOAS. Through orthogonal screening, 6 significant factors on rrad and rDOAS were selected. Simplified models for rrad and rDOAS calculation in both continuous and intermittent operation modes were established using simulation data, and a revised design process for the integrated system was proposed.

Abstract Radiant heating and cooling systems have significant energy-saving potential and are gaining popularity in commercial buildings. The main aim of the experimental study reported here was to characterize the behavior of radiant cooling systems in a typical office environment, including the effect of ceiling fans on stratification, the variation in comfort conditions from perimeter to core, control on operative temperature vs. air temperature and the effect of carpet on cooling capacity. The goal was to limit both the first cost and the perceived risk associated with such systems. Two types of radiant systems, the radiant ceiling panel (RCP) system and the radiant slab (RS) system, were investigated. The experiments were carried out in one of the test cells that constitute the FLEXLAB test facility at the Lawrence Berkeley National Laboratory in March and April 2016. In total, ten test cases (five for RCP and five for RS) were performed, covering a range of operational conditions. In cooling mode, the air temperature stratification is relatively small in the RCP, with a maximum value of 1.6 K. The observed stratification effect was significantly greater in the RS, twice as much as that in the RCP. The maximum increase in dry bulb temperature in the perimeter zone due to solar radiation was 1.2 K for RCP and 0.9 K for RS – too small to have a significant impact on thermal comfort. The use of ceiling fans was able to reduce any excess stratification and provide better indoor comfort, if required. The use of thin carpet requires a 1 K lower supply chilled water temperature to compensate for the added thermal resistance, somewhat reducing the opportunities for water-side free cooling and increasing the risk of condensation. In both systems, the difference between the room operative temperature and the room air temperature is small when the cooling loads are met by the radiant systems. This makes it possible to use the air temperature to control the radiant systems in lieu of the operative temperature, reducing both first cost and maintenance costs.

Abstract Building energy simulation (BES) plays a significant role in buildings with applications such as architectural design, retrofit analysis, and optimizing building operation and controls. There is a recognized need for model calibration to improve the simulations’ credibility, especially with building data becoming increasingly available and the promises that a digital twin brings. However, BES calibration remains challenging due to the lack of clear guidelines and best practices. This study aims to provide the foundation for future research through a detailed systematic review of the vital aspects of BES calibration. Specifically, we conducted a meta-analysis and categorization of the simulation inputs and outputs, data type and resolution, key calibration methods, and calibration performance evaluation. This study also identified reproducible simulations as a critical issue and proposes an incremental approach to encourage future research’s reproducibility.

Abstract Abstract Window operation is not only an important method for improving the indoor thermal environment and air quality, but also a significant way to reduce energy consumption of air-conditioned rooms during off-running periods in transition seasons. The occupants' window-operation behavior is influenced by both objective factors, such as thermal comfort and indoor air quality; and objective sensation, such as psychology and physiology, introducing considerable randomness and uncertainty. A two-month field observation of occupant window-opening behaviors for natural ventilation in an office building during the transition seasons was carried out in Chongqing, China. Multi-factor analysis of variance was conducted in data analysis using SPSS statistical software. The results showed that outdoor air temperature significantly affected window opening among other factors such as outdoor relative humidity, indoor air temperature, indoor relative humidity, and indoor CO2 concentration, which have much less effect. The main trigger point for opening windows in the transition seasons is from occupants' desire to improve the indoor thermal and air quality environment. A probability model of occupants' window operation was proposed based on logistic regression analysis. Meanwhile, the Monte Carlo simulation results indicate that during transition seasons (when outdoor temperature varied from 15 to 30 °C), the probability of window opening in office buildings follows a normal distribution and increases linearly along with the outdoor temperature growth.

Abstract Building energy simulation (BES) has been widely adopted for the investigation of building environmental and energy performance for different design and retrofit alternatives. Data-driven analytics is vital for interpreting and analyzing BES results to reveal trends and provide useful insights. However, seamless integration between BES and data-driven analytics current does not exist. This paper presents eplusr, an R package for conducting data-driven analytics with EnergyPlus. The R package is cross-platform and distributed using CRAN (The Comprehensive R Archive Network). With a data-centric design philosophy, the proposed framework focuses on better and more seamless integration between BES and data-driven analytics. It provides structured inputs/outputs format that can be easily piped into data analytics workflows. The R package also provides an infrastructure to bring portable and reusable computational environment for building energy modeling to facilitate reproducibility research.