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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]

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]

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]

The building-integrated coil-type ground-coupled heat exchanger (GCHE) was used to overcome the high installation cost of the heat exchanger. A field test and simulations were conducted to analyze the performance of GCHE when installed vertically or horizontally. The vertical GCHE refers to the coil-type GCHE installed at the center of a PHC (pre-stressed high-strength concrete) pile used for the building foundation. The horizontal GCHE refers to the coil-type GCHE laid horizontally on the bottom of the building. The amount of electric power consumed by the horizontal GCHE system was higher than that consumed by the vertical GCHE system. The coefficient of performance (COP) through a cooling experiment was identified as 3.9–4.3 for the vertical type and 3.3–3.7 for the horizontal type. The simulation model was used to examine the annual performance evaluation for the vertical and horizontal GCHEs. The comparison of the year-round energy consumption values showed that the vertical GCHE has about 15% higher heating/cooling efficiency than the horizontal GCHE. The return period was six years in both the horizontal and vertical GCHEs for the short term, but the cost saving in the vertical GCHE was about 28% higher in the long term. [ABSTRACT FROM AUTHOR]

Highlights: [•] Application of heat control strategy reduces total energy consumption of LED lighting. [•] Convective heat from LED lighting should be emitted outdoors during cooling period. [•] Seasonal optimization of convective heat lowers total energy consumption. [ABSTRACT FROM AUTHOR]

• An improved deep-learning model was proposed to predict heating energy consumption. • Three LSTM (Long-Short Term Memory) models with different input variables compared in detail. • Based on pattern analysis of operation cycle (trend, daily, weekly), additional input variables were considered to reflect change points of energy consumption. • LSTM model with pattern data for operation cycle showed better performance than other cases. The precise prediction of building energy consumption plays an important role in balancing the demand and supply of energy. In general, regular operation patterns occur for non-residential buildings, and the reflection of such operation patterns to models can play a positive role in predicting building energy consumption. In this study, three long short-term memory (LSTM) models were created, and the results were compared to examine the effect of applying the operation pattern data of a non-residential building on the prediction performance of the LSTM model. Each model exhibited different results according to the type of the input data used: (i) case 1 – building environmental data; (ii) case 2 – building environmental data and outdoor environmental data; and (iii) case 3 – building environmental data, outdoor environmental data, and operation pattern data. The building exhibited regular schedules by time and cycle according to the occupancy characteristics. When the prediction model utilized additional variables related to building operation as input data, it exhibited better performance than the other cases. The prediction performances in best case were 17.6% for CVRMSE and 0.6% for MBE. Applying insights into the change points at which the energy consumption pattern of the building changes was reflected to the model resulted in a higher pattern similarity than the other cases. [ABSTRACT FROM AUTHOR]