Search

This study proposes a transfer learning (TL)-based inverse modelling to identify unknown building properties. This study examines the transfer from virtual buildings to existing buildings, especially for identifying wall U-value, HVAC efficiency and lighting power density (LPD). For this purpose, synthetic data were generated from simulation results of sampled EnergyPlus models, and then we developed artificial neural network (ANN) models using this data. By adopting TL, the ANN models were transferred to the domain of existing buildings and evaluated on 61 existing buildings. As a result, the relative improvements in CVRMSE achieved by the transferred models against the models trained only with existing buildings' data were 8.85%, 10.34% and 15.73% for nominal cooling COP, wall U-value and LPD, respectively. Moreover, it is expected that the use of TL enables the developed model to be reusable for another group of buildings with improved performance and reduced training time. [ABSTRACT FROM AUTHOR]

The purpose of the present study was to investigate the relevance of building thermal performance and characteristics to building energy consumption. This paper reports an energy analysis of 4625 office buildings in Seoul, South Korea, using data from the Korean national building energy database and architectural database. The following four research questions were investigated: (1) Do old buildings consume more energy than new ones? (2) Have strict prescriptive building energy codes contributed to the reduction in energy use intensity (EUI, kWh/m2·year) over the past several decades? (3) What are the characteristics of building energy consumption in terms of season, age, and cooling system (electric chiller vs absorption chiller)? (4) Which factors in the Korean building energy database are relevant to building energy consumption? The analyses revealed that, contrary to common assumptions, new buildings did not always consume less energy than old buildings, and it may be wrong to attribute intensification of prescriptive building energy codes directly to building energy efficiency improvements. In addition, the building characteristics (i.e., district, year built, number of floors, number of elevators, and total floor area) available in the Korean building energy database do not adequately explain building energy consumption, and the existing data collection method needs further improvement. [ABSTRACT FROM AUTHOR]

This paper presents the results of model predictive control (MPC) using multiple deep neural network (DNN) models for the cooling system of a factory building. The target building accommodates a large space (80 m × 60 m × 9.7 m) and serves cooling via 45 diffusers connected to a direct expansion air handling unit that comprises two air supply fans and four condensing units. The authors developed 10 simulation models using a DNN: One predicts the supply air temperature of the HVAC system, while the others predict the indoor temperature of nine zones. The models can sufficiently predict the thermal behavior of the HVAC system (normalized mean bias error (NMBE): −1.2 %, coefficient of variation of the root mean square error (CVRMSE): 3.8 %) and indoor environment (NMBE: 1.1 %, CVRMSE: 1.3 %). The purpose of the MPC is to minimize the energy consumption of the condensing units while maintaining the cooling set-point temperature. The MPC was applied to the target building for a sampling time of 10 min, for four weeks, from August 23 to September 17, 2021. It was found that energy consumption decreased by 35.1 % compared with the baseline period, while satisfying the cooling set-point temperature maintenance condition. Finally, the authors highlight that the DNN-based MPC is a practical approach sufficient for predicting indoor thermal behavior and has significant potential for energy saving. [ABSTRACT FROM AUTHOR]

Uncertainty analysis in building energy assessment has become an active research field because a number of factors influencing energy use in buildings are inherently uncertain. This paper provides a systematic review on the latest research progress of uncertainty analysis in building energy assessment from four perspectives: uncertainty data sources, forward and inverse methods, application of uncertainty analysis, and available software. First, this paper describes the data sources of uncertainty in building performance analysis to provide a firm foundation for specifying variations of uncertainty factors affecting building energy. The next two sections focus on the forward and inverse methods. Forward uncertainty analysis propagates input uncertainty through building energy models to obtain variations of energy use, whereas inverse uncertainty analysis infers unknown input factors through building energy models based on energy data and prior information. For forward analysis, three types of approaches (Monte Carlo, non-sampling, and non-probabilistic) are discussed to provide sufficient choices of uncertainty methods depending on the purpose and specific application of a building project. For inverse analysis, recent research has concentrated more on Bayesian computation because Bayesian inverse methods can make full use of prior information on unknown variables. Fourth, several applications of uncertainty analysis in building energy assessment are discussed, including building stock analysis, HVAC system sizing, variations of sensitivity indicators, and optimization under uncertainty. Moreover, the software for uncertainty analysis is described to provide flexible computational environments for implementing uncertainty methods described in this review. This paper concludes with the trends and recommendations for further research to provide more convenient and robust uncertainty analysis of building energy. Uncertainty analysis has been ready to become the mainstream approach in building energy assessment although a number of issues still need to be addressed. [ABSTRACT FROM AUTHOR]