Urban Building Energy Model is a new bottom-up engineering model introduced by Reinhart and Cerezo [18], to combining the capabilities of statistical and engineering models to provide hourly energy assumptions, estimate the impacts of new technologies interventions, and incorporate occupant behaviors [14]. Another feature of UBEMs is the possibility to combine them with GIS platform. The resulting energy maps are then used for results analysis and comparison with measured data or surveys to help designers and policy makers.
UBEM apply heat transfer equations in and around the buildings that are represented each one as an individual 3D dynamic thermal model. UBEM are hence able to support complex
scenario development. Furthermore, they can be combined with energy simulation
programs. The energy modeling workflow of an UBEM requires high effort and time
resources given the massive amount of data for potentially thousands of buildings.
Assembling, managing, and automating the workflow is essential. For this purpose, the
building stock is divided into archetypes to reduce complexity and computation requirements [14].
An illustrative case of UBEM is the Boston model developed by the MIT Sustainable Lab [55].The model was accomplished using a set of tools comprising GIS [64] for buildings’ footprints importation, Rhinoceros 3D [65] as the CAD environment, and EnergyPlus as the thermal simulation engine. The workflow consists of generating the archetypes based on the year of construction and buildings’ types, extruding the building’s footprint to create the three-dimensional form, dividing it into floors, generating windows and assigning the
specific thermal properties based on the building’s archetype. Shading surfaces were
determined and each building was then represented by a thermal model and its energy performance was simulated in EnergyPlus. A following study, where the same workflow was applied for a neighborhood of Boston, explored different ECM that can be applied to reduce
the energy consumption [15]. Another example is the CityBES in the US, an open
interactive web-based platform to automatically generate UBEMs based on city GIS dataset [16]. It provides results of energy end-uses on annual, monthly and hourly timescales with a
3D visualization of the city and its urban modules.
3DStock is another 3D model for the British building stock, which breaks buildings to floors with different activities, and floors to zones with different sub-activities. Geometrical data,
electricity and gas consumption are attached to each Self-Contained Unit. 3DStock is
capable of making projections of future consumption, or testing the impact of possible abatement measures and new technologies [17].
In the previous paragraphs, we discussed the recent trends of cities, the urbanization issues and challenges, and the climate change threats. Urgent interventions and feasible actions are required. In this context, energy management for urban policies rises with a particular importance, to reduce the energy consumption, improve its quality, increase its availability
and reduce the GHG emissions resulting from its production. Urban models have been
introduced to assist these objectives. An overview of traditional and recent modeling
techniques was presented to explore the limitations and the strengths of each technique.
When managing the integration of renewable energy or application of retrofit measures
at city level, scaling down to hourly energy consumption patterns is crucial. This high
resolution temporal energy demand is determined by occupant activities, lifestyles and
economic status. Hence, UBEM calibration concentrates on integrating the significant
weight of occupant behaviors into the urban model. To encompass these aspects,
probabilistic approaches are applied. In this context, Cerezo et al. [66] proposed a Monte Carlo simulation with probabilistic distributions method to characterize uncertain parameters related to building occupancy. The method was then validated by comparing it to two others deterministic methods for a district in Kuwait City. The probabilistic method showed less error in terms of average Energy Use Intensity (EUI) and standard deviation.
Richardson et al. [67] developed a Markov-Chain Monte Carlo technique for stochastic
occupancy model generation based on a time-series data. The data set consisted of 24-hour diaries, completed at ten-minutes time step by thousands of participants. It was used to derive transition probabilities matrices to predict the probability of the current state
(resident is active or not) to change in the next time step. The model showed similar
profiles as the data set, revealing its accuracy. As the model is freely available, He et al. [68] applied it to generate heating patterns of English houses. In order to validate their findings, they coupled the stochastic model to EnergyPlus and compared the results to
another set of simulations with a deterministic occupancy model. It has been obvious that the hourly thermal demands with the stochastic model are more realistic and representative for the dwellings. UBEM approach is the most useful and reliable one to estimate hourly energy consumption at urban level, and then explore the impacts of ECM and/or renewable energy technologies, it will be used in this study. However, since we have selected Beirut, it is important to point out that there is substantial work done for modeling energy consumption of buildings in the city. For example, Annan et al. [69] simulated the impact of natural ventilation on energy use in buildings by simulating one typical residential building in Beirut. Ghaddar et al. [70] have simulated the impact of air conditioning use on UHI and energy use in buildings in Beirut by adopting a top-down model approach. The
authors found that the temperature in urban areas could increase by 0.8◦C during the day
and 4.7◦C during the night due to the extensive use of air conditioning systems. A
bottom-up approach was used to assess Lebanon’s energy budget from 2010 to 2015 [71]. For the building sector, the calculations were based on the constructed area per building type, the climatic zone, the occupancy rates and the energy demands per end-uses per building type defined by a previous study A roadmap for developing energy indicators for
buildings in Lebanon [72]. The latter study results were obtained after simplified
calculations conducted for a business as usual case (BAU) under coastal climate and with assumptions of the boundary conditions based on expertise. However, both studies did not
account for the different properties of buildings envelope. All the above studies can be
complemented by an archetypal classification of the buildings and more detailed BEM for more accuracy and applicability.