Search

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]

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]

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]

Highlights: [•] Improved performance in a vertical PV module via a PCM was assessed. [•] The optimal PCM melting temperature was determined according to weather conditions. [•] The PCM thickness was also determined to maximize the performance of the PV module. [•] The electrical power out of the PV/PCM module was increased by 1.5%. [Copyright &y& Elsevier]

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]

Abstract: Geothermal energy has been provided to improve the energy performance of buildings with great support from the government in Korea. However, despite the many advantages of using a ground-coupled heat pump (GCHP) with geothermal energy, the high construction cost of the ground-coupled heat exchanger (GCHE) is the primary obstacle to prevent the supply and spread of GCHPs. In this study, in order to overcome the problems of the conventional GCHP, a GCHP integrated with a PHC (prestressed high-strength concrete) pile, which is used in the foundation of buildings, was introduced and its thermal performance was analyzed through experiments conducted in summer. To increase the thermal performance, a coil-type pipe was used. The PHC-pile-integrated GCHP was installed at a depth of 15m. However, because it was installed in the beneath of the building, it was not largely affected by the outdoor temperature. The measured effective thermal conductivity was 3.69W/m°C, which is similar to that of a conventional vertical GCHP. Also, the COP was determined to be 3.9–4.3, which is slightly lower than the conventional vertical GCHP. However, considering the fact that the expensive drilling cost could be mitigated by 83.7%, the thermal performances were satisfactory. [Copyright &y& Elsevier]

Abstract: Many Korean apartment units use balconies as semi-indoor spaces by installing external windows. These balconies form environmental buffer spaces to improve comfort and reduce cooling and heating costs. However, the balcony spaces of some units have been removed and have been placed in living rooms. The removal of these environmental buffer spaces has caused problems. Significant heat loss through windows is one of the most serious problems; window surface condensation caused by low surface temperatures in the winter makes residents uncomfortable and damages construction materials. Therefore, it is evident that window design requires improvements. This study introduces a double window system with improved thermal performance and ventilation slits to solve the window surface condensation problem in apartment units without balconies. To verify the thermal performance of the proposed system, temperature factors were analyzed by field measurements. The condensation frequencies were also evaluated by temperature factors at an indoor temperature of 25°C and 55% humidity. Ultimately, the double window system was able to reduce condensation frequency greatly. An examination of the air volume by ventilation slit operational mode found that the case with an open outer window bottom vent and an inner window top ventilation slit, discharged the indoor vapor effectively and increased the dew point of the window''s surface. [Copyright &y& Elsevier]

The light-emitting diode (LED) market is expected to account for half of lamps worldwide in 2020. Despite the numerous benefits of LED lighting, the heat from LED lights affects indoor thermal conditions in buildings and increases the indoor cooling load in summer. A new method for integrating LED lighting with heating, ventilation, and air conditioning (HVAC) systems are proposed to prevent this heating effect in buildings by exchanging the lighting heat to outdoor and indoor spaces according to a seasonal operating strategy. Field measurements of a mock-up model were used to determine the lighting heat removal and reuse rates of the seasonal strategy. The ratio of lighting heat removal and reuse to LED input power was 70.3% in the summer test and 49.5% in the winter test. An indoor thermal effect analysis was performed using a simulation program with lighting heat input fractions obtained from the measured data. The sensible thermal loads in the mock-up chamber were reduced by 7.5% for cooling and 1.4% for heating using the proposed method, and the annual site energy reductions of reference office buildings were 8.5% for Houston, 8.1% for Atlanta, and 5.9% for Boulder, compared to the base case. [ABSTRACT FROM AUTHOR]

In this study, a model that is capable of evaluating the environmental benefits of a new elementary school facility was developed. The model is composed of three steps: (i) retrieval of elementary school facilities having similar characteristics as the new elementary school facility using case-based reasoning; (ii) creation of energy consumption and material data for the benchmark elementary school facility using the retrieved similar elementary school facilities; and (iii) evaluation of the environmental benefits of the new elementary school facility by assessing and comparing the environmental impact of the new and created benchmark elementary school facility using life cycle assessment. The developed model can present the environmental benefits of a new elementary school facility in terms of monetary values using Environmental Priority Strategy 2000, a damage-oriented life cycle impact assessment method. The developed model can be used for the following: (i) as criteria for a green-building rating system; (ii) as criteria for setting the support plan and size, such as the government's incentives for promoting green-building projects; and (iii) as criteria for determining the feasibility of green building projects in key business sectors. [Copyright &y& Elsevier]

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]