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• W3200415536 abstract "Due to insufficient records and limited number of weather stations, prediction models must be used to forecast local climate conditions. Accurate prediction is required in the case of emerging cities because rapid growth in urban development causes changes in the attributes of the urban environment, particularly the local microclimate. With the help of the Urban Weather Generator (UWG) and a locally established weather station, this research explores the validity of UWG processed open weather data (i.e., World Weather Online and Open Weather Map datasets). The Marina district in the city of Lusail near Doha, Qatar, saw a 26% increase in temperature prediction accuracy. A more detailed analysis of a representative residential building load prediction reveals that cooling estimate gaps are reduced by 2.7% to 7.3% when compared to the underestimated loads from the rural weather dataset. The impact of urban morphology on urban climate is further studied. The results show that increasing building construction, which results in increased building footprint density in the studied area, increases cooling consumption of the representative residential building by more than 11,000 kWh under certain conditions. Whereas, increase in greenery only results in savings of around 250 kWh. Additionally, a uniform random sensitivity analysis of 10 UWG characteristics showed that cooling consumption can vary between 10,000 kWh and 47,500 kWh compared to the predicted cooling consumption when the baseline weather dataset is used." @default.
• W3200415536 created "2021-09-27" @default.
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• W3200415536 date "2021-12-01" @default.
• W3200415536 modified "2023-10-10" @default.
• W3200415536 title "Impact of urban morphology on urban microclimate and building energy loads" @default.
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• W3200415536 doi "https://doi.org/10.1016/j.enbuild.2021.111499" @default.
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