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• W3086799188 abstract "To reduce greenhouse gas emissions and energy consumption in urban areas, understanding buildings' energy performance and consumption patterns is essential for implanting effective energy management and efficiency strategies at a city scale. Such plans' implementation at large scale requires information on how the energy demands may change under specific interventions. Urban Building Energy Models (UBEM) are proposed tools to estimate current and future building's energy demand. These models rely on a bottom-up approach, combining both statistical techniques and physics-based methods. This study aims at providing an enhanced modeling approach that simulates buildings' energy demand at high spatial and temporal resolution, which can help in evaluating energy management strategies and decision-making energy policies. The methodology is applied for the city of Beirut, representative of the Mediterranean region where the similarity of buildings technologies and climatic concerns among its cities is pronounced. The main objectives of the thesis are to develop, investigate and adjust a bottom-up energy modeling tool at urban scale; to provide evidence of the tool's suitability to support guidelines for future interventions; and finally to investigate the impact of the city's compactness on daylight availability and thus citizens' well-being. In this case study based on two different districts within the city, a near-city-scale building energy model, BEirut Energy Model BEEM, is generated to estimate the building's stock electricity consumption. To reduce the modeling and computation time, an archetypal classification of the buildings based on their types and periods of construction is adopted. The additional information required to generate the 3D model of the buildings are the number of floors, buildings' areas and a topographic map of the study areas. By coupling the models to the hourly weather conditions, the thermodynamic model of 3,630 buildings is simulated in EnergyPlus. Adapting the model to Beirut's occupancy and users' behaviors is crucial to enhance the reliability of BEEM. The availability of metered electricity data allows the model calibration, which is based on buildings' clustering and finding the clusters' coefficients representative of specific energy patterns. After the training phase, the model's accuracy in predicting electricity consumption is improved. Comparing the actual consumption and the calibrated results, the averaged absolute percentage error of the electricity consumption was reduced from 310% to 41% in district A and from 326% to 39% in district B. The calibrated model is combined with Geographic Information System (GIS) for a spatiotemporal distribution of energy demand patterns, which can help in assessing the most suitable intervention technologies." @default.
• W3086799188 created "2020-09-21" @default.
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• W3086799188 date "2019-09-17" @default.
• W3086799188 modified "2023-09-26" @default.
• W3086799188 title "Revealing the spatio-temporal energy consumption of a mediterranean city : the case of beirut" @default.
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