In the context of global efforts to enhance building energy efficiency, variable refrigerant flow (VRF) systems are recognized for their high performance and flexible control, achieving widespread adoption, particularly in East Asia. This proliferation necessitates accurate thermal load prediction, which is essential for right-sizing systems and meeting performance guarantees. However, conventional whole-building forecasting fails at the outdoor unit (ODU) zone level because it overlooks the distinct zonal characteristics and the granular, user-driven operational dynamics that primarily govern the load. This paper addresses this critical gap by introducing a physics-guided transfer learning framework centered on a novel methodology for creating a high-fidelity, physics-based source domain. The methodology first empirically establishes the cooling-capacity-weighted indoor unit (IDU) activation ratio as a key determinant, and then develops a dynamic psychrometric blending method to integrate this metric into EnergyPlus. This physics-guided simulation approach enables the creation of a large-scale simulation database. Building on this foundation, a Long short-term memory (LSTM) network is pre-trained to learn general thermal principles, and a transfer learning strategy is then used to adapt this knowledge to data-scarce, real-world scenarios. The framework's efficacy was demonstrated through three distinct transfer strategies that systematically evaluated its performance using non-target data (for zero-shot prediction), limited target-specific data, and a hybrid of both. All strategies markedly outperformed a model pre-trained solely on simulation data, with the optimal hybrid strategy achieving a final R² of 0.866 and reducing the mean absolute error (MAE) by 18.9%. This approach demonstrates a promising pathway toward reliable prediction for ODU zones, offering valuable support for more efficient VRF system design and operation.

Building heating, ventilation, and air conditioning (HVAC) systems consume large amounts of energy, and precise energy prediction is necessary for developing various energy-efficiency strategies. Energy prediction using data-driven models has received increasing attention in recent years. Typically, two types of driven models are used for building energy prediction: sequential and parallel predictive models. The latter uses the historical energy of the target building as training data to predict future energy consumption. However, for newly built buildings or buildings without historical data records, the energy can be estimated using the parallel model, which employs the energy data of similar buildings as training data. The second predictive model is seldom studied because the model input feature is difficult to identify and collect. Herein, we propose a novel key-variable-based parallel HVAC energy predictive model. This model has informative input features (including meteorological data, occupancy activity, and key variables representing building and system characteristics) and a simple architecture. A general key-variable screening toolkit which was more versatile and flexible than present parametric analysis tools was developed to facilitate the selection of key variables for the parallel HVAC energy predictive model. A case study is conducted to screen the key variables of hotel buildings in eastern China, based on which a parallel chiller energy predictive model is trained and tested. The average cross-test error measured in terms of the coefficient of variation of the root mean square error (CV-RMSE) and normalized mean bias error (NMBE) of the parallel chiller energy predictive model is approximately 16% and 8.3%, which is acceptable for energy prediction without using historical energy data of the target building.

Although computer technologies have greatly advanced in recent years and help engineers improve work efficiency, the heating, ventilation, and air conditioning (HVAC) design process is still very time-consuming. In this paper, we propose a conceptual framework for automating the entire design process to replace current human-based HVAC design procedures. This framework includes the following automated processes: building information modeling (BIM) simplification, building energy modeling (BEM) generation & load calculation, HVAC system topology generation & equipment sizing, and system diagram generation. In this study, we analyze the importance of each process and possible ways to implement them using software. Then, we use a case study to test the automated design procedure and illustrate the feasibility of the new automated design approach. The purpose of this study is to simplify the steps in the traditional rule-based HVAC system design process by introducing artificial intelligence (AI) technology based on the traditional computer-aided design (CAD) process. Experimental results show that the automatic processes are feasible, compared with the traditional design process can effectively shorten the design time from 23.37 working hours to nearly 1 hour, and improve the efficiency.

Heating, Ventilation, and Air-Conditioning (HVAC) control strategies are set arbitrarily in many commercial buildings by operators, who sometimes lack relevant skills and professional training. It is acknowledged that improving the control strategy of HVAC is feasible and valid, which as a consequence can improve the overall HVAC performance of existing buildings. However, it is quite difficult for an outsiders or a commissioning agent to tell what the HVAC control strategies are and whether they are implemented appropriately in existing buildings. This paper is intended to carry out analysis on the data about Building Automation System (BAS), as well as the data about building energy, for the purpose of identifying the control strategies of HVAC in a given building by using data mining algorithm. Then the results can be adopted by us to determine whether the building is under faulty operation or is running under suboptimal conditions. In this paper, what are proposed are algorithms of data mining identification for some specific HVAC control strategies, including DR on/off strategy, DR reset strategy and temperature reset strategy of chilled water. On the basis of data mining algorithms, a framework is then developed so as to identify these strategies, and the main scenario of this identification framework is known as analyzing many commercial buildings on an energy monitoring platform of a public building. This framework takes the sensor data obtained from HVAC, including temperature, flowrate, and electricity usage, as input, which is followed by the application of Image Segmentation and PCA algorithm for preprocessing. Then, based on these input variables, XGBoost algorithm is employed to determine whether these strategies have been implemented in buildings or not. In order to get the data for training and testing the framework, EnergyPlus Runtime Language is adopted for the application of different strategies. t is finally shown by the result that the identification algorithm can achieve the accuracy rate of 92.5% in the case studies by using one-day operation data, and the identification algorithm can arrive at the accuracy rate of 100% by using three-day operation data.