Ontology-Based Semantic Modeling of HVAC Systems in the Office Building: A Foundation for Scalable Control and Digital Twin Integration

2026-04-06

Yujin Kim, Juwan Ha, Seungmin Lee, and Soolyeon Cho

Model Predictive Coordinated Control for Grid-Interactive Residential Building with Energy Recovery Ventilator, PV, and Battery Energy Storage System

2026-04-06

Control Architecture for an Energy Flexible Building Dual Mechanical System with Novel Storage

2026-04-06

Improving Energy and Data Efficiency in Factory Building: A Physics-Informed Neural Networks-Based Model Predictive Control (PINNs-Based MPC) Approach

2025-08-04

Unfolding 3D Space into Binary Images for Daylight Simulation via Neural Network

Journal of Daylighting · 2023-12-30 · 3 citations

Daylighting plays a crucial role in building science, impacting both occupants’ well-being and energy consumption in buildings. Balancing the size of openings with energy efficiency has long been a challenge. To address this, various daylight metrics have been developed to assess interior spaces’ daylight quality. Additionally, architects have been using simulation algorithms to predict postconstruction light conditions. In recent years, machine learning (ML) has revolutionized daylight simulations, offering a way to predict daylight conditions without cumbersome 3D modeling or heavy computational resources. However, accommodating architects’ creativity remains a challenge for current machine learning-based models. Specifically, the diversity of window shapes and their locations on facades poses difficulties for prediction accuracy. To overcome this limitation, this paper proposes a novel method that transforms wall information into matrices and uses them as input to train an artificial neural network-based model; this model can well predict the annual daylight simulation result generated by the Climate-Based Daylight Modeling tools. This method allows the model to adapt to various real-world design scenarios in real time, and its robust reliability has been demonstrated through evaluations of prediction accuracy concerning different annual daylight metrics. This approach caters to specific cases and opens possibilities for application in other machine learning and deep learning-based methods.

Potential Cooling Energy Savings of Economizer Control and Artificial-Neural-Network-Based Air-Handling Unit Discharge Air Temperature Control for Commercial Building

Buildings · 2023-04-28 · 14 citations

Heating, ventilation, and air-conditioning (HVAC) systems play a significant role in building energy consumption, accounting for around 50% of total energy usage. As a result, it is essential to explore ways to conserve energy and improve HVAC system efficiency. One such solution is the use of economizer controls, which can reduce cooling energy consumption by using the free-cooling effect. However, there are various types of economizer controls available, and their effectiveness may vary depending on the specific climate conditions. To investigate the cooling energy-saving potential of economizer controls, this study employs a dry-bulb temperature-based economizer control approach. The dry-bulb temperature-based control strategy uses the outdoor air temperature as an indicator of whether free cooling can be used instead of mechanical cooling. This study also introduces an artificial neural network (ANN) prediction model to optimize the control of the HVAC system, which can lead to additional cooling energy savings. To develop the ANN prediction model, the EnergyPlus program is used for simulation modeling, and the Python programming language is employed for model development. The results show that implementing a temperature-based economizer control strategy can lead to a reduction of 7.6% in annual cooling energy consumption. Moreover, by employing an ANN-based optimal control of discharge air temperature in air-handling units, an additional 22.1% of cooling energy savings can be achieved. In conclusion, the findings of this study demonstrate that the implementation of economizer controls, especially the dry-bulb temperature-based approach, can be an effective strategy for reducing cooling energy consumption in HVAC systems. Additionally, using ANN prediction models to optimize HVAC system controls can further increase energy savings, resulting in improved energy efficiency and reduced operating costs.

Energy savings and life cycle cost analysis of advanced double skin facade system applied to old apartments in South Korea

Journal of Building Engineering · 2023-04-11 · 5 citations

Sensitivity Analysis and Multi-Objective Optimization of Skylight Design in the Early Design Stage

Energies · 2023-12-31 · 5 citations

Building geometry design decisions are important for energy efficiency and daylight performance. Sensitivity analysis, coupled with optimization, is an important approach to investigate and optimize building geometry in the early design stage. Incorporating skylights is an important daylighting strategy in commercial buildings; however, skylight-to-floor ratio (SFR) is often the only design variable evaluated in precedent studies. More design variables related to skylight geometry, clerestory geometry, skylight material, and building geometry need to be evaluated. This study investigates the skylight design of a 2000-square-meter commercial building. Eighteen design variables are evaluated according to their influence on building energy and daylight performance. One-at-a-time (OAT), linear regression, and Morris sensitivity analysis approaches are utilized to identify the most influential variables. Seven of the twelve building geometry variables and two of the six building material variables are considered as important. Then, a multi-objective optimization with genetic algorithms is processed to find out the optimal design solution. The three objectives are energy use intensity (EUI), daylight autonomy (DA), and daylight uniformity (DU). After the optimization, five candidate design options are picked from the Pareto front. Discussions are made on the features of these designs, and one design is selected as the optimal solution.

Comparative Analysis of ANN and LSTM Prediction Accuracy and Cooling Energy Savings through AHU-DAT Control in an Office Building

Buildings · 2023-05-31 · 22 citations

This paper proposes the optimal algorithm for controlling the HVAC system in the target building. Previous studies have analyzed pre-selected algorithms without considering the unique data characteristics of the target building, such as location, climate conditions, and HVAC system type. To address this, we compare the accuracy of cooling load prediction using ANN and LSTM algorithms, widely used in building energy research, to determine the optimal algorithm for HVAC control in the target building. We develop a simulation model calibrated with actual data to ensure data reliability and compare the energy consumption of the existing HVAC control method and the two algorithms-based methods. Results show that the ANN algorithm, with a CV(RMSE) of 12.7%, has a higher prediction accuracy than the LSTM algorithm, CV(RMSE) of 17.3%, making it a more suitable algorithm for HVAC control. Furthermore, implementing the ANN-based approach results in a 3.2% cooling energy reduction from the optimal control of Air Handling Unit (AHU) Discharge Air Temperature (DAT) compared to the fixed DAT at 12.8 °C in a representative day. This study demonstrates that ML-based HVAC system control can effectively reduce cooling energy consumption in HVAC systems, providing an effective strategy for energy conservation and improved HVAC system efficiency.

Hybrid Solar Geothermal Heat Pump System Model Demonstration Study

Frontiers in Energy Research · 2022-01-03 · 17 citations

In this paper, the development and demonstration of a hybrid solar geothermal heat pump polygeneration system is presented. The poly-generation system has been designed, modeled, and simulated in TRNSYS software environment. Its performance was assessed followed by installation and demonstration at a demo site in Cheongju, Korea. The space heating and cooling load of the building is 13.8 kW in heating mode at an ambient temperature of −10.3°C and 10.6 kW in cooling mode at an ambient temperature of 32.3°C. The simulation data were compared with the field demo data using ISO 13256. The results showed that the model data compare well with the demo data both in heating and cooling modes of operation. At a source temperature of 16.7°C, the heat pump lab performance data-based COPc shows 9.9, while demonstration COPc shows 10.3, thus, representing 4.3% relative error. The heat pump source temperature decreased by 4.0°C from 20.9°C to 16.9°C due to ground heat exchanger coupling and resulted in a COPc increase by 13.3% from 8.5 to 9.8. When compared at the design conditions (outside temperature of 32.3°C), the TRSNYS model overestimated the demonstration site data by 12%, 9.3 vs. 8.1 kW with power consumption of 3.1 vs. 2.2 kW. The hybrid polygeneration system power consumption decreased by 1.2 kW when ambient temperature decreased from 35°C to 25°C.