Data management and 3D model generation 

Get Complete Project Material File(s) Now! »

Overview of urban building energy models

The building sector is responsible of a great part of the global energy consumption. Hence, it has a great potential in reducing GHG emissions and improving energy e ciency, by the mean of retro tting and the use of high-e cient energy technologies in the demand side [32]. Moreover, it can contribute in the energy supply management by the integration of renewable energy production in buildings such as solar PVs. Hence, building energy modeling at city scale plays an important role as decision-making tools to plan strategies for the both demand and supply management of the energy sector. The main purposes of building energy models can ˆbe summarized as follows [33, 34]:
predict present and future energy consumption, disaggregated by the factor of interest (e.g., building type, income, etc.) by quantifying the energy use as function of di erent input parameters.
predict the technical and economic e ects of di erent policy measures and energy consumption reduction strategies.
However, urban energy modeling is facing challenges among which the rarely accessible high level of detail (LoD) data, the systematic uncertainties, the heavy required computational resources, its sensitivity to urban microclimate and human behaviors [35]. The review section below starts with a brief introduction of urban building energy modeling approaches, then dive into the subcategories of each one to end with the chosen technique for this research, its bene ts and challenges.

 Modeling approaches: Top-down and Bottom-up models

Based on the literature review, there is no unique classi cation of the urban energy models. Even though, many approaches can be considered the same with di erent terminologies. For example, the forward approach proposed by 2009 ASHRAE Handbook relies on detailed physical description of the buildings and their systems as the engineering method discussed by Swan et al. [36] and the physical models suggested by Foucquier et al. [37] . In the following, a summary classi cation is discussed. Two main approaches have been used to model energy use patterns in urban context: top-down and bottom-up models [33].

Top-down models

Top-down models are mainly applied to explore the inter-relations between the energy sector and the socio-economic factors [33]. The main stream of top-down models is going from national or regional sector to individual buildings sector. They simply use the total energy consumption estimate and disaggregate it by attributing the energy consumption to buildings components without technical details. This work ow is easy since the required data (aggregated energy consumption data) is widely available. By using historical data, top-down models extrapolate from the status quo to predict near future energy patterns. However, if paradigm shifts in the energy sector are encountered, top-down models fail, which make them unsuitable for interventions and technological studies. Moreover, the identi cation of possible improvements such retro tting or demolition is inhibited due to lack of details. The top-down models can be divided into two categories: econometric and technological. Econometric models study the energy consumption as function of economic variables such as gross domestic product (GDP), income, energy price and may include climatic conditions. As they rely on the past energy-economy interactions to predict current and future consumption, they lack technical details and are not suitable for climatic change impact evaluation as the latter may dramatically a ect the pillars of a sustainable development: society, environment and economy. On the other hand, the technological models include other factors that a ect the energy use such saturation e ects, technological progress, structural change and so on [33]. An example of top-down models was developed for the residential stock in Jordan [38]. A multivariate regression model with a time series analysis was adopted to predict future energy consumption and potential energy savings by correlating macroeconomic indicators such as income level, electricity and fuel unit prices, social indicators such as population and the weather conditions. Authors claimed that their approach might not be accurate but can inform about the future.

Bottom-up models

The rst intuition of bottom-up models was to identify future energy e ciency measures capable to reclaim wasted energy resources. The impacts of these measures implementation, costs and doubts can be evaluated by developing di erent scenarios making bottom-up analysis a more realistic approach to specify energy reduction potentials and thereby GHG emissions [39]. Bottom-up models estimate individual end-uses, aggregate results according to their impacts on energy use, then extrapolate to regional or national level. This detailed approach allows for improvements and technological studies. By relating end-use energy consumption to macro-economic indicators, bottom-up models can gain some of top-down models strengths. Behavioral factors such as occupancy behavior, heating and cooling systems uses and energy gains may be incorporated in energy assumption increasing the accuracy of the model. However, due to the complexity of these occupant dependent variables, they are often assumed. This assumption level is a major drawback of some bottom-up models. In addition, large amount of data is required to e ciently describe each component contribution, which is limited in many countries. In addition, the sensitivity of input parameters is inappropriately described. Calculation and simulation techniques are in many cases time consuming, high costly and seek high level of expertise. According to Harishˆ et al. [40], bottom-up models are based on two approaches:
Forward approach: it involves the input of detailed parameters of the buildings to predict the outputs. Models based on this approach are highly accurate as thermodynamics and heat transfer equations are applied. In addition, building energy simulation software tools are widely developed incorporating complex equations for better prediction.
Data driven approach: the inputs and the outputs of the model are known or have been measured. The data can be intrusive in case the experiments to gather information are performed under normal system operation. When controlled experiments are limited by the building operation, nonintrusive data is collected. The energy consumption is estimated using regression analysis relating it to various parameters. Arti cial algorithms such as Neural Networks and Support Vector Machines are applied when long-term energy estimation is requested to reduce the amount of performance data to be collected and the number of parameters to be identi ed when repeated operations such occupancy and set points schedules are encountered.
A more sophisticated and branched classi cation is proposed by Swan et al. [36], where the bottom-up models are classi ed into statistical and engineering models. Though, as the use of statistical learning algorithms is spreading widely, Zhao et al. [41] presented them in a separate group named Arti cial Intelligence methods. Each of the aforementioned groups can be further sub-divided as studies and modeling techniques are in the process of development and growth.

READ  L'HETEROGENEITE DES PAYSAGES AGRICOLES ET SON INFLUENCE SUR LES AUXILIAIRES GENERALISTES ET LE CONTROLE BIOLOGIQUE DES RAVAGEURS

Statistical models

Statistical models, also called inverse models [42], are primary used to identify building parameters by using existing data such as billing data and surveys information. They are particularly adopted to:
Detect energy consumption abnormalities or malfunctioned systems Analyze impacts of retro tting measures Statistical models embed the strengths of top-down models as they use macroeconomic and socioeconomic variables such as energy price and income. They incorporate occupant behavior by attributing energy consumption to end-uses. In general, they are easy to develop and use. Regression and conditional demand analysis (CDA) are well-documented techniques and widely used [36]. As examples, linear regression models have been used to assess the electricity and fuel consumption of New York City [43] and correlate the energy performance for heating with the Surface area to Voume ratio S/V in the city of Carugate, Italy [44]. However, as they dont provide a detailed description of energy end-uses, they lack of exibility and are limited when assessing new energy measures [33]. Large amount of data is required to correlate energy consumption with end-uses and to achieve an acceptable accuracy level. In addition, when based on annual metered data, they are unable to predict energy use in monthly or hourly time steps or to simulate the combined impact of several energy e ciency measures in buildings [14].

Engineering models

Engineering models (EM) complex equations are widely incorporated in building energy simulation programs to overcome their complexity [40, 45], like EnergyPlus [46] and TRNSYS [47]. However, a detailed physical description of building properties and systems and precise weather characteristics are essential for these programs [48].
By applying engineering models, end-uses energy consumption are determined based on their physical functions and thermodynamics relations. Accordingly, a detailed description of their impacts on the aggregated energy consumption is provided. The model does not rely on previous data so it is more suitable to test new technologies and e ectively estimate the low-cost energy e ciency opportunities and their appropriate combinations. Although EM are considered high accurate, occupant behavior and preferences are di cult to be included and are rather assumed. In consequence, the socio-economic factors are excluded [49]. In addition, large amount of inputs and high level of expertise are required to develop the models and solve the equations. To reduce EM complexity, some modelers proposed some alternatives to simplify the analytical approach, either by applying steady-state methods such as degree-day method and its optimization techniques [50], or by simplifying the building characteristics inputs by applying easy equations or using average values from statistical data. Filogamo et al. [51] assigned to their buildings average geometrical properties obtained from statistical data such as number of oors, number of inhabitants, oor dimensions (width, height and depth), shape ratio S/V, glazing surfaces, even Domestic Hot Water (DHW) and cooking energy intensities. Heating demands were assumed by classical calculation methods due to well-known behavior. Mata et al. [52] created an engineering bottom-up model called Energy, Carbon and Cost Assessment for Building Stocks (ECCABS). It is based on a physical approach by applying thermodynamic and transfer heat equations to estimate the net energy demand, assess energy savings measures ESM and CO2 emissions reductionsin residential building stocks in Sweden. In addition, the model is capable to calculate the costs savings from applying the ESM. The model applies its equations on representative buildings from the studied stocks, then multiply the results by weighting coe cients, each one representing the fraction of buildings in the entire stock that belong to each building category. However, simpli ed models integrate a wide range of assumptions to the calculation procedures, hence, increase model uncertainty.

Dynamic simulation

Dynamic simulation consists of using energy performance software tools, like EnergyPlus [46] and TRNSYS [47], to overcome the complexity of EM. It is suitable for large buildings with complex systems simulation and are capable to involve control strategies [42]. Energy simulation programs are often based on two modeling techniques [45]: the analytical method and the numerical method. The rst one solves linear di erential equations with time independent parameters, while the second one uses a nodal network representation of the building and applies for each node a system of nonlinear and time dependent equations. The nodal network is then simulated simultaneously (e+). As the numerical method handles more complexity in the nodes interactions, it is more preferred. In general, a detailed physical description of building properties and systems and precise weather characteristics are essential for the software. Additionally, simulation relies on simpli ed inputs and assumed values related to behavioral parameters reducing the accuracy of the model [48]. In addition, it is still expensive in terms of expertise, time and costs. Harish et al. [40] provided a recuperative overview of energy simulation programs, their applications and limitations.

Table of contents :

1 Introduction 
1.1 General context
1.2 Thesis Outline
2 State of the art 
2.1 Overview of urban building energy models
2.2 Modeling approaches: Top-down and Bottom-up models
2.2.1 Top-down models
2.2.2 Bottom-up models
2.3 Urban building energy models
2.4 Conclusion
3 Data management and 3D model generation 
3.1 Introduction
3.2 Remote sensing for data preprocessing
3.3 Machine learning for oultier detection
3.4 Generation of the 3D model
3.4.1 Archetypes generation
3.4.2 3D model
3.5 Conclusion
4 Urban daylight model 
4.1 Introduction
4.2 Presentation of DART model
4.2.1 Earth-Atmosphere scene
4.2.2 Elements optical properties
4.2.3 Earth-atmosphere radiative transfer
4.3 Impact of urban development on energy budget
4.4 Efect of urban morphology on daylight accessibility
4.4.1 Urban morphology metrics
4.4.2 Radiative Budget computation – DART
4.5 Results and discussion
4.5.1 Zones’ Urban morphology
4.5.2 Daylight availability and variability spatially and temporally
4.5.3 Daylight potential and urban forms: Neural networks approach
4.5.4 Relation between daylight and electricity consumption
4.6 Conclusion
5 BEirut Energy Model BEEM
5.1 Introduction
5.2 Mathematical formulation
5.2.1 Zone and Air system integration
5.2.2 Ideal Loads Air System
5.2.3 Outside Surface Heat Balance
5.2.4 Rections
5.2.5 Inside Surface Heat Balance
5.2.6 Inltration and ventilation
5.3 Methodology
5.3.1 Data preparation
5.3.2 Parameters Set-up and boundary conditions
5.3.3 Model Calibration
5.4 Results
5.4.1 Archetypes distribution and 3D model
5.4.2 Data processing and model calibration results
5.4.3 Loads proles
5.4.4 Spatial autocorrelation
5.4.5 Archetypes Spatial correlation
5.4.6 Temporal correlation
5.5 Discussion and conclusion
6 Conclusion and perspectives
6.1 Perspectives

GET THE COMPLETE PROJECT

Related Posts