Titlebook: Data-driven Analytics for Sustainable Buildings and Cities; From Theory to Appli Xingxing Zhang Book 2021 The Editor(s) (if applicable) and

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https://doi.org/10.1007/978-3-030-42224-0al energy consumption and simulation results. This chapter aims to extract occupant-behaviour related electricity load patterns using classical K-means clustering approach at the initial investigation stage. Smart-metering data from a case study in Shanghai, China, was used for the load pattern anal

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Physical Storage and Distributionhistorical weather data (e.g. typical meteorological year-TMY). Nevertheless, due to climate change, the actual weather data during a NZEB’s lifecycle may differ considerably from the historical weather data. Consequently, the designed NZEBs using the historical weather data may not achieve the desi

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Undergraduate Topics in Computer Sciencenties for the emerging solar photovoltaic/thermal (PV/T) technologies. This chapter therefore aims to conduct a techno-economic evaluation of a reference solar PV/T concentrator in Sweden for building application. An analytical model is developed based on the combinations of Monte Carlo simulation t

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https://doi.org/10.1007/978-1-4471-5601-7ver, complex to predict and control conventionally. This chapter, therefore, proposes a novel reinforcement learning (RL) method for the advanced control of window opening and closing. The RL control aims at optimising the time point for window opening/closing through observing and learning from the

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Concise Guide to Formal Methodsin predicting Thermal Sensation (TS). The implicit assumption is that PMV can be applied for predicting TS of a large population. Our statistical analysis of a subset of ASHRAE global database of thermal comfort field study shows that occupants’ expectations towards TS are affected by factors that a

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