ALBERT

Beschreibung: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 50〈/p〉 〈p〉Author(s): Zhengrong Li, Haowei Xing, Godfried Augenbroe〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Although there have been numerous studies on the evaluation of models that estimate sky diffuse radiation on inclined surfaces, it is still difficult for investigators to select from available sky diffuse radiation models for urban microclimate and building performance simulation. This is due to the fact that results from different studies are not consistent, or even contradictive, which indicates the fact that the evaluation criterion itself has a great effect on the performance of the model.〈/p〉 〈p〉To explore the effect of different evaluation criteria on the performance rating of the models, four evaluation methods are applied in this paper: diffuse irradiance on facades with respect to sky condition, diffuse irradiance on facades with respect to orientation, diffuse irradiance distribution among sky dome with respect to sky condition and diffuse irradiance on buildings in obstructed environment. Based on a statistical test on available data, Igawa model is considered to be the most accurate and appropriate model for urban and building energy simulation. Besides, an evaluation criterion appropriate for screening sky diffuse models for urban and building energy simulation is proposed. Furthermore, potential errors that may occur in the measurement and the corresponding quality control is presented.〈/p〉 〈/div〉

Beschreibung: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 50〈/p〉 〈p〉Author(s): D. Koteswara Rao, D. Chandrasekharam〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Sustainable conservation of natural resources has become a primary concern for urban cities, globally as they are centers of consumption and economy. Due to population growth, cities depend more on imports of food, energy, water, and services from all over the globe, and consume more virtual water than direct water, because of their food habits and lifestyle. Most of the imported goods are water intensive and pose challenges in tracing the source of virtual water. The goal of this research is to develop a general framework to assess the water footprint (WF) of a typical city in India using existing databases. A consumer-centric approach has been adopted for assessing WF in Hyderabad Metro Development Area (HMDA). The variation of the WF across economic classes of consumers is also analyzed. The WF is estimated based on four broad categories: 1) food consumption, 2) fossil fuels based energy, 3) electric power, and 4) direct water. Average WF of HMDA region is 1041 m〈sup〉3〈/sup〉/cap/year (2852 LPCD), in which 70% (1986 LPCD) of WF was consumed by food, 25% (744 LPCD) by electric power, only 4% (121 LPCD) is from direct water consumption and surprisingly the contribution from fossil fuel WF to total per capita WF of HMDA area is less than 1%.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: Available online 5 July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society〈/p〉 〈p〉Author(s): Shuqin Chen, Xiyong Zhang, Shen Wei, Tong Yang, Jun Guan, Wenxiao Yang, Lijuan Qu, Yunqing Xu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Accurate grasp of district power demand is of great significance to both sizing of district power supply and its operation optimization. In this study, an index system has been established and visualized through a Geographic Information System, for revealing both temporal and spatial characteristics of district power loads caused by heating/cooling systems, including load level and fluctuation characteristics, spatial distribution of electric loads, and load coupling relationships between individual buildings and the district. Principal component analysis was applied to identify the buildings with significant impact on district load management. Using this method, the spatial-temporal characteristics of electric loads caused by heating in one university campus in China were analyzed. The results showed that building type and the operation modes had great effects on the level and volatility of the district electric load caused by heating. Buildings with high load levels and strong coupling with the peak district electric load, such as academic buildings, often had a major impact on the power demand of the district. Therefore, they were considered as key targets for energy-saving renovation and operation optimization. Buildings with large load fluctuation, such as teaching buildings, could contribute to the peak load shaving by adjusting the heating systems’ operation.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 50〈/p〉 〈p〉Author(s): Shuo-Jun Mei, Zhiwen Luo, Fu-Yun Zhao, Han-Qing Wang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Urban ventilation is important for building a healthy urban living environment. 2-D CFD simulation has been used widely for street canyon ventilation due to its high computational efficiency, but its applicability for a 3-D simulation has never been studied. This paper tried to answer the question: if and under what conditions, the widely-adopted 2-D CFD simulations on street canyon ventilation can represent a 3-D scenarios? 3-D simulations on street canyons with various street lengths and corresponding 2-D simulations are carried out with RNG 〈em〉k〈/em〉-〈em〉ε〈/em〉 model. Our study identified two important ventilation mechanism for controlling ventilation and dispersion in a 3-D street canyon, i.e., canyon vortex on the canyon top and the corner vortices at the street ends. The relative importance of these two driving forces will change with the street length/street width ratio (〈em〉B/W〈/em〉). For isolated street canyon, when 〈em〉B/W〈/em〉 is higher than 20 (for 〈em〉H/W〈/em〉 = 1) and 70 (〈em〉H/W〈/em〉 = 2), the street canyon ventilation will be dominated by canyon vortex, and 3-D street canyon ventilation could be simplified as a 2-D case. For multiple street canyon, the threshold of 〈em〉B/W〈/em〉 will become 20 when 〈em〉H/W〈/em〉 = 1, and 50 when 〈em〉H/W〈/em〉 = 2. The findings in this study could improve our approaches for simulating urban ventilation.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 50〈/p〉 〈p〉Author(s): Gaofeng Gu, Dujuan Yang, Tao Feng, Harry Timmermans〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The increasing shift of individuals to use new electric mobility tools like electric cars (EV) and electric bikes has changed household energy expenditure. It may also affect households’ investments in renewable energy equipment, i.e. solar panels, heat pumps. Relatively little research has been conducted on how the decision to purchase electric vehicles affects the decision to invest in home renewable energy equipment. This paper, therefore, aims to examine the effects of mobility tools decisions on the intention to invest in solar panels and heat pumps, based on the data collected through a stated choice experiment. A mixed logit model is estimated to capture unobserved heterogeneity among individuals. Results show that mobility tools significantly influence the choice of home renewable energy equipment. Households who prefer to purchase electric vehicles have a higher probability to invest in solar panels and heat pumps than households who prefer other mobility tools. In addition, EV adopters’ intention to invest in solar panels are stronger than the intention to invest in heat pumps. This suggests that electric vehicle users are likely the early adopters of solar panels.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 50〈/p〉 〈p〉Author(s): Heictor Correia Maioli, Raíssa Corrêa de Carvalho, Denise Dumke de Medeiros〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The increase in population concentration in large cities is a trend in the world, which brings several problems. In the context of urban mobility, bicycle sharing systems deserve special mention due to the impact and growth worldwide. The dissemination and success of these systems are also linked to aspects related to quality in their provision. Thus, this paper aims to conduct a study in the context of bicycle sharing to help managers to stimulate the use of this service and contribute to the development of sustainable cities. The SERVPERF tool was adapted and used to assess the quality of bicycle sharing service, in addition to identify which aspects impact on users’ satisfaction. This approach is innovative since there is a gap in the literature about customer satisfaction analyzes and aspects related to the quality of bicycle sharing service. In this way, this study can contribute to the dissemination of this service and to the solution of urban mobility problems by identifying the aspects considered as most important for the customers and thus improving these aspects. Therefore, the bicycle sharing system may have the increasingly use and will help to reduce urban mobility problems.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 50〈/p〉 〈p〉Author(s): Zhijian Liu, Di Wu, Yuanwei Liu, Guangya Jin, Qiaomei Wang, Zhonghe Han, Hancheng Yu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Low energy buildings are an efficient approach to meet space heating requirements and saving energy. Studies of low energy buildings mainly focus on cold climate regions. However, there is less research on the feasibility of low energy buildings on Qinghai-Tibetan Plateau located in a severely cold climatic region. Therefore, a low energy building equipped with an integrated heating system was built on Qinghai-Tibetan Plateau. The performance of the low energy building was quantitatively evaluated by field measurement and dynamic TRNSYS simulation. To be specific, the indoor temperature and relative humidity were measured, and further served as the data to verify the accuracy of the simulated results. The results show that the temperature and humidity were above 20 °C and 40% in the heating season, in accordance with the regulated low energy building standard (DB63/T1682-2018). According to the TRNSYS simulation, the hourly indoor temperature and relative humidity were generally in the range of 20–24 °C and 34–56%, respectively, in the heating season, with an energy demand of 16.1 kW h/(m〈sup〉2〈/sup〉 year). Overall, the building performance meets the local low energy building standard (DB63/T1682-2018). Therefore, a passive building with integrated heating system assistance is feasible for pleasant indoor comfort on Qinghai-Tibetan Plateau. These findings could explore the application potential of low energy buildings in severe cold climate areas.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 50〈/p〉 〈p〉Author(s): Abdelkader Dairi, Tuoyuan Cheng, Fouzi Harrou, Ying Sun, TorOve Leiknes〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Wastewater treatment plants (WWTPs) are sustainable solutions to water scarcity. As initial conditions offered to WWTPs, influent conditions (ICs) affect treatment units states, ongoing processes mechanisms, and product qualities. Anomalies in ICs, often raised by abnormal events, need to be monitored and detected promptly to improve system resilience and provide smart environments. This paper proposed and verified data-driven anomaly detection approaches based on deep learning methods and clustering algorithms. Combining both the ability to capture temporal auto-correlation features among multivariate time series from recurrent neural networks (RNNs), and the function to delineate complex distributions from restricted Boltzmann machines (RBM), RNN-RBM models were employed and connected with various classifiers for anomaly detection. The effectiveness of RNN based, RBM based, RNN-RBM based, or standalone individual detectors, including expectation maximization clustering, K-means clustering, mean-shift clustering, one-class support vector machine (OCSVM), spectral clustering, and agglomerative clustering algorithms were evaluated by importing seven years ICs data from a coastal municipal WWTP where more than 150 abnormal events occurred. Results demonstrated that RNN-RBM-based OCSVM approach outperformed all other scenarios with an area under the curve value up to 0.98, which validated the superiority in feature extraction by RNN-RBM, and the robustness in multivariate nonlinear kernels by OCSVM. The model was flexible for not requiring assumptions on data distribution, and could be shared and transferred among environmental data scientists.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Neng Zhu, Daokun Chong〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Hyperthermal environments in public or industrial buildings impose substantial heat stress on the human body. To ensure human health in built environments, research has focused on the interactions between the heat tolerance and environments. This paper summarizes the progress in the literature concerning the evaluation and improvement of heat tolerance in three areas: environmental and physiological parameters, heat tolerance evaluation indexes, and heat acclimation (HA). This review leads to four conclusions: (1) Future indexes should give top priority to subjective perceptions to diminish individual differences; (2) machine learning techniques based on big data should be adopted to deal with complex correlations between hot environments and heat tolerance; (3) an optimal HA training regimen should be determined for workers to improve their heat tolerance and HA should be considered in the design and control strategies of indoor thermal environments for energy conservation; and (4) more field investigations should be conducted to modify laboratory-based findings. This review provides a comprehensive understanding of built environments and health and guidelines for future research.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Sophie A. Nitoslawski, Nadine J. Galle, Cecil Konijnendijk van den Bosc, James W.N. Steenberg〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Smart cities are increasingly part of urban sustainability discourses. There is a growing interest in understanding how citizen engagement, connected technology, and data analytics can support sustainable development. Evidence has also repeatedly shown that green infrastructure such as urban forests address diverse urban challenges and are critical components of urban sustainability and resilience. Nevertheless, it is unclear whether green space and urban forest management are gaining significant traction in smart city planning. It is thus timely to consider whether and to what extent urban forests and other green spaces can be effectively integrated into smart city planning, to maximize green benefits for all city dwellers.〈/p〉 〈p〉We address this gap by exploring current and emerging smart city trends and technologies, and highlight practical applications for urban forest and green space management. Current “smart urban forest” projects reveal a focus on novel monitoring techniques using sensors and Internet of Things (IoT) technologies, as well as open data and citizen engagement, particularly through the use of mobile devices, applications (“apps”), and open-source mapping platforms. We propose a definition and promising approach to “smart urban forest management”, emphasizing both the potential of digital infrastructure to enhance forest benefits and the facilitation of citizen stewardship and empowerment in green space planning. Cities are getting faster and smarter – can (and should) the trees, and their managers, do the same?〈/p〉 〈/div〉