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Urban heat island (UHi), a phenomenon involving increased air temperature of a city compared to the surrounding rural area, results in increased energy use and escalated health problems. to understand the magnitude and characteristics of UHi in Seoul and to accommodate for the high temporal variability and spatial heterogeneity of the UHi which make it inherently challenging to analyze using conventional statistical methods, we developed two deep learning models, a temporal UHi-model and a spatial UHi model, using a feed-forward deep neural network (Dnn) architecture. Data related to meteorological elements (e.g. air temperature) and urban texture (e.g. surface albedo) were used to train and test the temporal UHi-model and the Spatial UHi-model respectively. Also, we develop and propose a new metric, UHI-hours, that quantifies the total number of hours that UHI exists in a given area. our results show that UHi-hours is a better indicator of seasonal UHi than the commonly used index, UHi-intensity. consequently, UHi-hours is likely to provide a better measure of the cumulative effects of UHI over time than UHI-intensity. UHI-hours will help us to better quantify the effect of UHI on, for example, the overall daily productivity of outdoor workers or heat-related mortality rates.The world's population is increasing dramatically; new cities are being built, while existing cities are getting overpopulated. Currently, more than 50% of the world population resides in cities 1 . This increased development in urban areas has resulted in changes in urban morphology and surfaces, as well as an increase in the amount of anthropogenic heat released to the atmosphere 2 . The combined effect of such changes leads to higher air temperature in urban areas than in the surrounding rural or suburban areas, hence the formation of heat islands 3 . These heat islands, especially in summers, considerably decrease the outdoor air quality and trigger heat-related diseases and deaths in urban areas 4,5 . Moreover, extreme temperatures have been reported to be the leading cause of weather-related deaths 6 . This is particularly prevalent among the elderly (people of 65 years of age and above) and those with cardiovascular and respiratory health issues. For example, Paravantis et al. 7 analyzed the impact of temperature and heat waves on the number of deaths caused by cardiovascular and respiratory health issues in people above 65 years of age in Athens, Greece. They reported a U-shaped exposure-response curve, indicating reduced mortality rates at moderate temperatures and 20% and 35% increase in mortality rates at very low and very high temperatures, respectively. Furthermore, due to the increase in urban temperatures, there has been an increase in air-conditioning system usage, which in turn has led to high electricity demands and overall increased building energy consumption 8 . At the same time, passive cooling methods, such as natural and night ventilation, have become ineffective for the thermal comfort of building occupants 9 .Due to...

Improper refrigerant charge amount (RCA) is a recurring fault in electric heat pump (EHP) systems. Because EHP systems show their best performance at optimum charge, predicting the RCA is important. There has been considerable development of data-driven techniques for predicting RCA; however, the current data-driven approaches for estimating RCA suffer from poor generalization and overfitting. This study presents a hybrid deep neural network (DNN) model that combines both a basic DNN model and a thermodynamic model to counter the abovementioned challenges of existing data-driven approaches. The data for designing models were collected from two EHP systems with different specifications, which were used for the training and testing of models. In addition to the data obtained using the basic DNN model, the hybrid DNN model uses the thermodynamic properties as a thermodynamic model. The testing results show that the hybrid DNN model has a prediction performance of 93%, which is 21% higher than that of the basic DNN model. Furthermore, for model training and model testing, the hybrid DNN model has a 6% prediction performance difference, indicating its reliable generalization capabilities. To summarize, the hybrid DNN model improves data-driven approaches and can be used for designing efficient and energy-saving EHP systems.Sustainability 2020, 12, 2914 2 of 23 and decreased the capacity by~20%; moreover, improper RCA can further reduce the efficiency of on-site ACs by 10-20% [9].In a field study, the refrigerant was improperly charged for~50% of the on-site heating, ventilating, and air-conditioning (HVAC) systems [8,10]. Furthermore, more than half of the residential cooling systems showed improper RCA problems [8]. For the long-term operation of systems, mechanical wear or improper maintenance could lead to refrigerant leakage or overcharge, which, in turn, resulted in reduced system operation efficacy and increased energy consumption [11]. Moreover, improper RCA can result in decreased system performance, increased energy consumption, and reduced life span of the system [12]. In the long run, from an economic point of view, improper RCA can lead to an increase in the operational cost of a building system; thus, if the RCA of a running system in a building can be effectively estimated, it can be positively used to solve the abovementioned problems.The topic of RCA detection is not widely discussed in the literature and only a few studies have been conducted for detecting RCA [13][14][15][16][17]. For example, a polynomial expression-based RCA detection algorithm was developed using only subcooling. The results showed relatively good predictions within a relative deviation of 8.0% [13]; however, although approaches based on mathematical expression models allow accurate predictions of RCA, they are often designed for individual systems, thus making their applications on other on-site EHP systems difficult [2,14]. Moreover, they may require several sensors to implement, thus leading to increase in cost. Other studies r...

The use of photovoltaic (PV) systems has drawn attention as a solution to reduce the dependence on fossil fuel for building energy needs. Moreover, incorporating energy storage systems (ESSs) in PV systems can optimise electric energy costs by increasing dependency on PV-generated energy during electric peak load times. However, current ESSs have limited capacities making it difficult to fully maximise PV-generated energy. We propose a novel integrated energy-efficient system for PV, ESS and electric heat pump (EHP) that maximises the usage of PV energy, optimises ESS usage and reduces EHP energy consumption costs. The components of the proposed integrated system are linked with a deep learning (DL)-based algorithm that forecasts PV energy generation and energy demand of the EHP. The proposed system schedules the charging/discharging time of ESSs depending on peak load times, the forecasted EHP electric demand, and PV-generated energy. The data used were collected for 10 months from a retail shop equipped with an EHP and ESS. We found that the developed DL-based forecasting models for PV and EHP are accurate and reliable (ie, R 2 above 0.95). Also, the results show that the proposed integrated energy-efficient PV-ESS-EHP system saves 12% of the total annual electric costs, which corresponds to 1 285 291 Won. The proposed system ensures an efficient method to maximise PV-generated energy resulting in reduced dependency on fossil fuels for building energy needs.

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