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There is growing concern that airtight and well-insulated buildings designed to limit heat loss in temperate and cold climates could unintentionally elevate the risk of overheating in summers. Existing literature primarily uses dynamic simulation to investigate this problem due to the difficulty of obtaining large-scale in-performance data. To address this gap, we undertake a meta-analysis of large-scale indoor air temperature data for 195 UK dwellings, as a study of performance in a temperate climate. Of these, 113 are baseline (i.e., typical existing dwellings) and the rest designed to the high-performance Passivhaus standard. Using both Passivhaus and the well-known CIBSE TM59 overheating standards, this study found that there were few overheated cases for any building type. However, the average summer nighttime temperature of Passivhaus bedrooms was 1.6 °C higher than baseline, with 20 out of 31 measured bedrooms exceeding the overheating criterion, and the average overheating hours constituting approximately 19% of the total summertime observation period. These findings suggest that bedrooms in highly insulated dwellings may pose an overheating risk although whole-dwelling overheating risk is low. [ABSTRACT FROM AUTHOR]

It has been identified that improving building energy efficiency is an effective method to reduce greenhouse gas (GHG) emissions. Although standards have been established to satisfy a building's minimum energy demand while ensuring the comfort of its residents, they are difficult to implement in mixed-humid regions. This study proposes a hybrid ventilation strategy that can comprehensively reduce cooling, heating, and ventilation energy in mixed-humid climate regions to significantly decrease the primary energy demand and reduce the impact of buildings on the environment. This study evaluated the changes in energy saving potential and thermal comfort according to the extension of the natural ventilation period and passive strategies, such as decentralized ventilation. Changes in indoor air temperature, operative temperature, and PMV for each strategy were analyzed. As a result, extending the natural ventilation and the decentralized ventilation strategies can save 32% and 34% of the building's energy, respectively. Considering that electricity is the main energy source for cooling in Korea, the extension of the natural ventilation period was judged to be the best approach from the perspective of primary energy demand. The results can be used to predict changes in building energy demand and thermal comfort and select an appropriate ventilation strategy based on occupant information obtained using Internet of Things. [ABSTRACT FROM AUTHOR]

This paper aims to derive the operational modes of a parallel double-window system that reduces cooling energy consumption and satisfies indoor comfort through natural ventilation. The parallel double-window system examined in this paper is a window system that could control indoor draft distribution and adjust the size of the opening depending on indoor and outdoor conditions. The system can be used in five ways (all close, out-open + in close, out-open+in open (tilt), out-open+in open (turn) and all open). This work verified the energy savings and indoor comfort of the existing mode experimentally, which were originally derived based on simple calculations at the time when the parallel double-window system was developed. A new operation mode, Alt 1, was derived, which addressed problems of the existing mode. In addition, in this work, the operation mode Alt 2 was derived, which simplified Alt 1 so that the actual occupant can use the system easily. By measuring these three operation modes and comparing the results with those of energy plus simulations, the work derived the amount of cooling energy savings and the level of indoor comfort through the use of an appropriate operation mode during inter-seasonal periods. Compared to when the natural ventilation operation mode was not used, cooling energy consumption was reduced by 60% when the operation mode was in use. The cooling temperature set point could have a significant impact on cooling energy consumption. [ABSTRACT FROM AUTHOR]

An adequate window system is one of the most important retrofit strategies for effective energy conservation of a building, because the U-value and solar heat gain coefficient of windows have enormous impact on the heating and cooling loads of buildings. Therefore, this paper presents methods for improving the energy efficiency of existing residences that have various window sizes and envelope insulations, through a window retrofit using optimal U-value and solar heat gain coefficient values. Furthermore, the window retrofit strategy has been standardized using analysis of the correlation between the properties of the retrofitted window and energy saving rates. The results show that the annual heating and cooling energy demand decreases by 7.9–16.7% when changing the U-value of the windows in a poorly insulated house, and that the relationship between the lower U-value and energy saving is strong for poorly insulated houses regardless of window size. However, for houses with better insulation and larger window sizes, the total energy usage decreases by 18.4–29.7% when the solar heat gain coefficient is lower, and the energy saving effect of the U-value decreases while that of the solar heat gain coefficient increases.Practical application: This study was focused on improving the energy efficiency of existing residences by applying retrofitting technology. By exploration of the contribution of the specific qualities of windows and the thermal envelope (insulation) system of buildings via simulation, it was determined that it is necessary to adjust the U-value and SHGC of retrofitted windows, in relation to the thermal performance and window-wall ratio of an existing residence, to achieve high energy efficiency. [ABSTRACT FROM AUTHOR]

Phase change materials (PCMs) absorb or release a large amount of heat when changing phase from solid to liquid or vice versa. When this characteristic of PCM is utilized, it can be employed effectively for energy conversation in buildings. To apply PCM to buildings, the thermal characteristics of PCM must be considered, such as the melting temperature and temperature range of the phase changes. In addition, the characteristics of the heating and cooling system, which influence the cycle of PCM absorbing and releasing heat, should also be taken into account. The purpose of this study was to provide guidelines for a method to apply PCM suitable for Korean residences, which use packaged air conditioners and floor radiant heating systems for cooling and heating, respectively. To accomplish this, the characteristics of cooling and heating energy reduction were analysed according to the application of PCM through simulation, taking into consideration various heating and cooling operating conditions. In addition, thermal conditions for PCM suitable for summer and winter were analysed through case studies, with the melting temperature and temperature range of phase changes as variables. [ABSTRACT FROM PUBLISHER]

Incorporating Internet of Things (IoT) technology into the operation of buildings is expected to generate immense synergy, thereby saving energy and improving occupant comfort by overcoming the limitations of the existing system. Preventing operations in the absence of occupants can save energy, and the occupants' preferred operating temperature should be used as the control set-point rather than the nominal temperature. In this study, IoT technology and image sensors are used to rapidly detect indoor environment changes, and a method is proposed to utilize air quality and thermal comfort as the control set-points. A real-time ventilation control algorithm is proposed based on the CO2 concentration calculated according to the number of occupants. To check the thermal comfort level, the real-time operating temperature estimated from the surface temperature data of the infrared array sensor is reflected in the comfort zone defined by the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE). The deficiencies in indoor environment conditions caused by the temporal and spatial lag of sensors in the old system are minimized using IoT technology, which also facilitates wireless communications. The image sensors can be used for multiple purposes based on various interpretations of the image information obtained. [ABSTRACT FROM AUTHOR]

To effectively analyze building energy, it is important to utilize the environmental data that influence building energy consumption. This study analyzed outdoor and indoor data collected from buildings to find out the conditions of rooms that had a significant effect on heating and cooling energy consumption. To examine the conditions of the rooms in each building, the energy consumption importance priority was derived using the Gini importance of the random forest algorithm on external and internal environmental data. The conditions that had a significant effect on energy consumption were analyzed to be: (i) conditions related to the building design—wall, floor, and window area ratio, the window-to-wall ratio (WWR), the window-to-floor area ratio (WFR), and the azimuth, and (ii) the internal conditions of the building—the illuminance, occupancy density, plug load, and frequency of room utilization. The room conditions derived through analysis were considered in each sample, and the final influential building energy consumption factors were derived by using them in a decision tree as being the WFR, window area ratio, floor area ratio, wall area ratio, and frequency of use. Furthermore, four room types were classified by combining the room conditions obtained from the key factor classifications derived in this study. [ABSTRACT FROM AUTHOR]

Humans spend approximately 90% of the daytime in buildings, and greenhouse gases (GHGs) emitted by buildings account for approximately 20% of total GHG emissions. As the energy consumed during building operation from a building life-cycle perspective amounts to approximately 70–90% of the total energy, it is essential to accurately predict the energy consumption of buildings for their efficient operation. This study aims to optimize a model for predicting the thermal energy consumption of buildings by (i) first extracting major variables through feature selection and deriving significant variables in addition to the collected data and (ii) predicting the thermal energy consumption using a machine learning model. Feature selection using random forest was performed, and 11 out of 17 available data were selected. The accuracy of the prediction model was significantly improved when the hour of day variable was added. The prediction model was constructed using an artificial neural network (ANN), and the improvement in the prediction accuracy was analyzed by comparing different cases of variable combinations. The ANN prediction accuracy was improved by 15% using the feature selection process compared to when all data were used as input data, and 25% coefficient of variation of the root mean square error (CVRMSE) accuracy was achieved. [ABSTRACT FROM AUTHOR]

Although the latest energy-efficient buildings use a large number of sensors and measuring instruments to predict consumption more accurately, it is generally not possible to identify which data are the most valuable or key for analysis among the tens of thousands of data points. This study selected the electric energy as a subset of total building energy consumption because it accounts for more than 65% of the total building energy consumption, and identified the variables that contribute to electric energy use. However, this study aimed to confirm data from a building using clustering in machine learning, instead of a calculation method from engineering simulation, to examine the variables that were identified and determine whether these variables had a strong correlation with energy consumption. Three different methods confirmed that the major variables related to electric energy consumption were significant. This research has significance because it was able to identify the factors in electric energy, accounting for more than half of the total building energy consumption, that had a major effect on energy consumption and revealed that these key variables alone, not the default values of many different items in simulation analysis, can ensure the reliable prediction of energy consumption. [ABSTRACT FROM AUTHOR]

Heating, ventilation, and air-conditioning (HVAC) systems usually have a set-point temperature control feature that uses the indoor dry-bulb temperature to control the indoor environment. However, an incorrect set-point temperature can reduce thermal comfort and result in unnecessary energy consumption. This study focuses on a derivation method for the optimal cooling set-point temperature of an HVAC system used in office buildings, considering the thermal characteristics and daily changes in the weather conditions, to establish a comfortable indoor environment and minimize unnecessary energy consumption. The operative temperature is used in the HVAC system control, and the mean radiant temperature is predicted with 94% accuracy through a multiple regression analysis by applying the indoor thermal environment data and weather information. The regression equation was utilized to create an additional equation to calculate the optimal set-point temperature. The simulation results indicate that the HVAC system control with the new set-point temperatures calculated from the derived equation improves thermal comfort by 38.5% (26%p). This study confirmed that a cooling set-point temperature that considers both the thermal characteristics of a building and weather conditions is effective in enhancing the indoor thermal comfort during summer. [ABSTRACT FROM AUTHOR]