Buildings use up to 40% of the global primary energy and 30% of global greenhouse gas emissions, which may significantly impact climate change. Heating, ventilation, and air-conditioning (HVAC) systems are among the most significant contributors to global primary energy consumption and carbon gas emissions. Furthermore, HVAC energy demand is expected to rise in the future. Therefore, advancements in HVAC systems’ performance and design would be critical for mitigating worldwide energy and environmental concerns. To make such advancements, energy modeling and model predictive control (MPC) play an imperative role in designing and operating HVAC systems effectively. Building energy simulations and analysis techniques effectively implement HVAC control schemes in the building system design and operation phases, and thus provide quantitative insights into the behaviors of the HVAC energy flow for architects and engineers. Extensive research and advanced HVAC modeling/control techniques have emerged to provide better solutions in response to the issues. This study reviews building energy modeling techniques and state-of-the-art updates of MPC in HVAC applications based on the most recent research articles (e.g., from MDPI’s and Elsevier’s databases). For the review process, the investigation of relevant keywords and context-based collected data is first carried out to overview their frequency and distribution comprehensively. Then, this review study narrows the topic selection and search scopes to focus on relevant research papers and extract relevant information and outcomes. Finally, a systematic review approach is adopted based on the collected review and research papers to overview the advancements in building system modeling and MPC technologies. This study reveals that advanced building energy modeling is crucial in implementing the MPC-based control and operation design to reduce building energy consumption and cost. This paper presents the details of major modeling techniques, including white-box, grey-box, and black-box modeling approaches. This paper also provides future insights into the advanced HVAC control and operation design for researchers in relevant research and practical fields.

The amount of heat lost through the envelope of a building is one of the most important variables that affects the energy performance evaluation of a house. In addition, it is especially important to estimate and accurately diagnose the amount of heat produced by windows. In Korea, windows’ U-values reflect a building’s initial design values and thermal characteristics that determine the thermal performance of an existing building, and is a factor that can overestimate the energy performance of a building. Therefore, there is a need for a field measurement method that can accurately measure the total U-value of windows in an existing house. This study provides a method of quantitatively measuring the total U-value of windows using the infrared (IR) method in ISO 9869-2. As a result of measuring the U-value using the infrared (IR) method, the Korean Standard (KS F 2278) for window performance test result values and the root mean square error (cvRMSE) for the U-value measurements using the IR method showed a high accuracy of about 3.29%. In addition, we confirmed that the IR method is an effective (cvRMSE about 7% improvement) method that can measure the comparison result faster than the heat flow meter (HFM) method, which is a conventional thermal performance measurement method.

Large construction firms execute management control in preparation for a fast-paced business environment, but small ones are unable to do so. This is because there is no management control model tailored to them. The current study derived Management Performance Evaluation Indicators (MAPEIs) for small construction firms for measuring the management performance of construction firms with 10 or fewer employees, considering the characteristics of small construction firms. MAPEIs consist of BSC (Balanced Scorecard), performance, and the hierarchy and weighted value of KPIs (Key Performance Indicators). After an interview with an expert, based on the management performance indicators of large construction firms, a final hierarchy of small construction firms was constructed through modification and supplementation. The KPIs of the hierarchy were analyzed through a survey using the AHP (Analytic Hierarchy Process) method to finalize MAPEIs for small construction firms in Korea. The final MAPEIs underwent a feasibility evaluation to apply them to real life. It is expected that they can be used as fundamental resources for system development for small construction firm management performance and control. In addition, further studies to resolve the limitations would improve the competitiveness of small construction firms.

PV (photovoltaic) systems are receiving the spotlight in Korea due to the Renewable Energy 3020 Implementation Plan (RE3020), which has the goal of reaching 20% for the proportion of renewable energy generation by 2030. Accordingly, the actual performance evaluation of PV systems to achieve the RE3020 has become more important. PV efficiency is mainly determined by various weather conditions (e.g., solar radiation) that affect the power generation of PV systems. However, the efficiency is also affected by changes in module surface temperature. In particular, the efficiency decreases when the module surface temperature rises. That is, the actual PV efficiency falls short of the rated efficiency. The estimation of module surface temperature is critical for evaluating the actual performance of PV systems. Many studies have been conducted to calculate the surface temperature. However, most of the previous studies focused on calculations of current surface temperatures using current environment data, which means that the previous studies have limitations related to timestep. That is, there is a lack of predictive models that calculate the future surface temperatures by using the current measured data. Therefore, this study developed a predictive model using an ANN (artificial neural network) algorithm to determine the surface temperature of PV modules for a future period of time. Then, this study evaluated the actual performance (i.e., power generation) with the predicted surface temperatures.

The number of domestic apartment houses in South Korea that use district heating is steadily increasing. In addition, most Korean residential buildings use radiant floor heating systems. For such systems, the heating water temperature supplied by a heat exchanger in a mechanical room serves as one of the critical control parameters for providing heat to individual residential apartments. Fixed temperature (FT) and outdoor temperature reset control (OTR) have conventionally been used to adjust the heating supply water temperature. However, both control methods have a major technical weakness; they do not reflect changes in residents’ heating use. To overcome this issue, this study proposes a new method for controlling the heating supply water temperature, called Residential Energy Demand (RED). To verify the proposed method, researchers conducted both simulation- and experiment-based tests. The RED control method achieved about 4% reduction in heating energy consumption compared to the conventional OTR control process. In addition, the RED control method increased the average indoor temperature by 0.17 °C during the heating period. Therefore, this study demonstrates that the proposed control method is capable of achieving energy savings and a warmer thermal indoor environment.

In Korea apartment buildings, most energy is consumed as heating energy. In order to reduce heating energy in apartment buildings, it is required to reduce the amount of energy used in heating systems. Energy saving in heating systems can be achieved through operation and control based on efficient operation plans. The efficient operation plan of the heating system should be based on the predicted heating load. Thus, various methods have been developed for predicting heating loads. Recently, artificial intelligence techniques (e.g., ANN: artificial neural network) have been used to predict heating loads. The process for determination of input data variables is necessary to obtain the accuracy of predicted results using an ANN model. However, there is a lack of studies to evaluate the accuracy level of the predicted results caused by the selection and combination of input variables. There is a need to evaluate the performance of an ANN model for prediction of residential heating loads. Therefore, the purpose of this study is, for a residential building, to evaluate the accuracy levels of predicted heating loads using an ANN model with various combinations of input variables. To achieve the study purpose, each case was classified according to the combination of the input variables and the prediction results were analyzed. Through this, the worst, mean, and best were selected according to the predicted performance. In addition, an actual case was selected consisting of variables that can be measured in an actual building. The derived cv(RMSE) of each case resulted in a percentage value of 38.2% for the worst, 7.3% for the mean, 3.0% for the best, and 5.4% for the actual. The largest difference between the best and worst resulted in 33.2%, and thus the precision of the predicted heating loads was highly affected by the selection and combination of the input variables used for the ANN model.