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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]

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]

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]

• 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]

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]