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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]

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]

• 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]