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.

Although quantum phase transitions involved with Anderson localization had been investigated for more than a half century, the role of spin polarization in these metal-insulator transitions has not been clearly addressed as a function of both the range of interactions and energy scales. Based on the Anderson-Hartree-Fock study, we reveal that the spin polarization has nothing to do with Anderson metal-insulator transitions in three dimensions as far as effective interactions between electrons are long-ranged Coulomb type. On the other hand, we find that metal-insulator transitions appear with magnetism in the case of Hubbard-type local interactions. One of the most fascinating and rather unexpected results is the appearance of half metals at intermediate energy scales in Anderson insulators of the Fermi energy, that is, only spin ↑ electrons are delocalized while spin ↓ electrons are Anderson localized.

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.

We have synthesized water-soluble polymer, poly[(9,9-bis((6'-(N,N,N-trimethylammonium)hexyl)-2,7-fluorene))-alt-bisphenylfumaronitrile]dibromide (AHF-alt-PFN), the polymer typically obtained by the Suzuki type of polymerization reaction and shows good solubility in methanol. Bulk heterojunction polymer solar cells (BHJ-PSCs) fabricated by using water soluble conjugated polymer and positive (Cs+) and negative (F-, CO2-(3)) charge ions doping as an interfacial layer for poly(3-hexylthiophene):phenyl-C61 butyric acid methyl ester (P3HT:PCBM). We have achieved an enhancement of the short circuit density and power conversion efficiency in solar cell by introducing poly(AHF-alt-PFN) layer between the active layer and the cathode metal. The device with poly(AHF-alt-PRN) layer containing F-, CO2-(3) showed a short circuit current density more 1.3, 2.3 times higher than those of the device without poly(AHF-alt-PFN) + ion layer. We explain the better performance in solar cell with poly(AHF-alt-PFN) + ion layer was due not only to the increase of electron mobility in poly(AHF-alt-PFN) layer but also to the decrease of the electron barrier near cathode by the addition of the negative ions.

Since P.O. Fanger proposed PMV, it has been the most widely used index to estimate thermal comfort. However, in some cases, it is challenging to measure all six parameters within indoor spaces, which are essential for PMV estimation; a couple of parameters, such as Clo or Met, tend to show a large deviation in accuracy. For these reasons, several studies have suggested methods to estimate PMV but their accuracies were significantly compromised. In this vein, this study proposed a way to reduce the dimensions of parameters for PMV prediction utilizing the machine learning method, in order to provide fast PMV calculations without compromising its prediction accuracy. Throughout this study, the most influential features for PMV were pinpointed using PCA, Best Subset, and the Gini Importance, with each model compared to the others. The results showed that PCA and ANN achieved the highest accuracy of 89.70%, and the combination of Best Subset and Random Forest showed the fastest prediction performance among all.