Application of Urban Scale Energy Modelling and Multi-Objective Optimization Techniques for Building Energy Renovation at District Scale
1.1. Building Energy Efficiency in the European Framework for 2050 Decarbonization
1.2. Overview of Urban Building Energy Modeling
- Top-down models. In these methods, the aggregated energy demand of an entire region or country is calculated by means of a black box approach with no correlation whatsoever to the end users [33,35]. Such models heavily rely on historical data series—i.e., energy consumption, fuel prices, income data, etc., also often used as indicators in macroeconomic or socio-econometric studies. Because of this and due to the lack of technological details, top-down urban energy models are strongly limited for accurately addressing the potential impact of interventions at the small scale of a district or individual building [33,35].
- Bottom-up models. In contrast to top-down models, this approach not only simulates the energy consumption of individual or groups of buildings but also allows for a distinction to be made between the different end uses (i.e., heating, cooling, lighting, etc.) to help identify areas of improvement . Bottom-up models can be based on either statistical methods or engineering techniques (also known as physical methods).
1.3. Aims of This Research
- Input data collection and processing. The first step is dedicated to the collection of all the inputs required to prepare an urban simulation model, which are (a) the geometry of the district and (b) the characteristics of the building envelopes and of the HVAC systems.
Those buildings without an EPC can be associated to the cluster represented by an archetype with similar important features, such as the year of construction (or the year of first renovation intervention), the adopted construction technology, the type of usage, and the installed HVAC system. As mentioned before, in particular cases, for instance, when the neighborhood is entirely served by a district heating or cooling network, the focus can be put only on the building envelope, identifying in such a way the so-called “building envelope archetypes”.In this phase, the characteristics and the accuracy of the collected inputs have to be taken carefully under consideration to address the next development of the urban simulation model. In this respect, the available inputs can affect the target use and potential of the model, having an impact on the level of representativeness of the simulated outputs. For example, if detailed information about the HVAC system regulation and dynamic interaction of the occupancy with the building system is unknown, hourly profiles of energy demand will be affected by a high level of uncertainty. Although the methodologies for model calibration can mitigate this kind of issue, the user should understand in this first step which level of detail is actually achievable, as well as necessary, in consideration of the building renovation goals.
- Urban geometry. The geometry is meant to be kept as accurate as possible, distinguishing the shapes and features of each building in the considered district. As mentioned before, Geographical Information Systems (GIS) can provide at least the urban layout for many municipalities, which can be integrated in the development phase with in situ inspections and other sources of data (e.g., orthographic pictures, mapping platforms, etc.). The geometrical model should be prepared taking into account just those features compatible with the chosen UBEM/USEM tool, neglecting unnecessary details. For instance, if the chosen code allows only for a 2.5D approach to model the geometry, or if just the urban layout is available, pitched roofs cannot be modeled.
- Building archetypes. Detailed and complete data regarding the buildings’ fabric and HVAC systems are usually available only for a fraction of the existing buildings in a neighborhood. The best sources for collection of the required information are generally the energy performance certificates, EPCs, which can be acquired from Government Energy Agencies and local public authorities. The sample of EPCs can be processed by means of data-mining techniques, such as k-means or k-medoids clustering techniques [70,77,78], in order to identify the building archetypes, each one representative for a given cluster of buildings in the district. It is worth noting that the proposed type of clustering does not impose continuity or adjacency constraints and just focuses on the building system features.
- Preparation, calibration and validation of the urban simulation model. At this stage, all the inputs necessary for preparing an urban simulation model should be available. Consequently, the new goal is to prepare a model and assess its ability to deliver representative results.To do so, it is recommended to start comparing the energy consumption of each cluster of buildings in the district, simulated using actual meteorological years AMYs, with the recorded historical data series. Considering both time-discretization of the recordings typically available and the state-of-the-art accuracy of the results provided by urban simulation codes [21,79], annual energy consumptions can be a suitable solution for this preliminary step. If the necessary inputs are known with a good level of accuracy and recordings with short-time discretization are available, additional comparisons can be performed, also allowing the assessment of the model’s capabilities in predicting short-term energy demand.In the case of large discrepancies, the calibration and validation of inputs uncertain or alleged to be different from those found in the EPCs, e.g., infiltration and ventilation rates, temperature setpoints and HVAC system efficiencies, should be performed, for example, in agreement with the prescriptions reported by ASHRAE Guideline 14  and IPMVP Guideline . Specifically, acceptable tolerances for calibration and validation should be chosen, depending on the time discretization of the available data recordings. Nevertheless, it should also be remembered that urban simulations require a large amount of input data and some techniques traditionally adopted for BEM calibration can be inappropriate for UBEM/USEM applications .
- Definition of available energy efficiency measures and objective functions for multi-objective optimization. After the model is validated, it is possible to proceed with the selection of the energy efficiency measures EEMs by means of multi-objective optimization.In this last stage, objective functions and optimization algorithms have first to be chosen, together with the sets of EEMs. Several alternative algorithms are present in the literature and the selection should be based on the efficiency and accuracy, accounting also for the features of the neighborhood under analysis, the considered objective functions, the selected EEMs and the available computational resources. Taking into account the differences between the clusters of buildings, each one represented by its own building archetype, a specific set of EEMs can be defined for each group. Once all inputs and parameters are set, optimization can be run using a typical or modified reference year as the weather file, depending on the goal of the analysis.The space of solutions is composed by all possible combinations of n sets of EEMs, with each set applied to one of the k clusters of buildings in the modeled neighborhood. At each iteration of the optimization algorithm, a solution vector is evaluated, calculating the values of the objective functions first for each cluster of buildings and then, by addition, for the whole district. Depending on the chosen objective functions and optimization algorithm, the final output can be a Pareto front, including solutions which the policy maker can choose according to the objective for prioritization (e.g., energy efficiency, economic result, etc.).
3. Case Study
3.1. Input Data Collection and Processing for the Chosen Case Study
3.2. Preparation, Calibration and Validation of the Urban-Scale Energy Model
3.3. Simulation Scenarios and Multi-Objective Optimization
3.3.1. Business as Usual Scenario
3.3.2. Selection of Energy Efficiency Measures
- Extruded polystyrene (XPS)—with a thermal conductivity λ = 0.035 W m−1 K−1, a density ρ = 30 kg m−3, and a specific thermal capacity c = 1450 J kg−1 K−1;
- Mineral wool—with λ = 0.038 W m−1 K−1, ρ = 130 kg m−3, c = 1030 J kg−1 K−1;
- Cellulose fiber—with λ = 0.045 W m−1 K−1, ρ = 160 kg m−3, c = 2000 J kg−1 K−1.
3.3.3. Objective Functions
3.3.4. Multi-Objective Optimization and Decision Variables
- x1, x2, x3 expressing the insulation thickness for Archetypes 1, 2 and 3 (“high”, i.e., 20 cm, or “low”, i.e., the minimum requirement),
- y1, y2, y3 indicating the window type, respectively, for Archetypes 1, 2 and 3, with four alternatives each.
- z1, z2, z3, expressing the type of insulation material for Archetypes 1, 2 and 3 (XPS, mineral wool or cellulose fiber),
4. Results and Discussion
4.1. Business as Usual Scenario
4.2. Multi-Objective Optimization
4.2.1. Preliminary Parametric Analysis
4.2.2. Comprehensive Multi-Objective Optimization
- Two groups of solutions leading to high energy performance are present in the NPV-saved CO2 emissions plane (see the green colored dots)—one is positioned in the top-right region of the chart (i.e., poor economic performance but high sustainability performance) and the other is located in the bottom-central region of the chart (i.e., better economic performance but moderate sustainability performance).
- The solutions ensuring the best economic performance, although still economically inconvenient, can be detected in the bottom regions of both charts. While their sustainability performance is poor, a wide range of energy performances can be achieved, some of them very close to the energy optimum.
- Finally, the solutions which are able to maximize the sustainability performances are in the top regions of both charts—i.e., they are the most expensive solutions. Nevertheless, some of them can easily allow for high energy performance as well.
4.3. Additional Energy Efficiency Measures
4.4. Further Considerations on Energy Demand for Space Conditioning
- Climate change could have a significant impact on the energy performance of the selected case study district. Indeed, the simulations run with a 2050 climate projection showed a potential 7% reduction in heating demand, due to an increase in ambient temperature. In contrast, the energy needs for space cooling could achieve values more than double the current ones. Even if the energy requirements for space cooling are still projected to be lower than those for heating, their growth has to be carefully taken into account in the development of the local energy policies for buildings.
- Each energy efficiency measure could improve the district energy performance. Nevertheless, most of solutions belonging to the Pareto surface range between 49 and 64 kWh m−2 a−1 with respect to a baseline of 91 kWh m−2 a−1 in 2050, which means that the proposed energy refurbishment, focused only on the building envelope, could bring just up to 46% reduction of the annual heating demand in the best case.
- None of the proposed investments is sustainable from the economic point of view. Indeed, all net present values are negative, meaning that none of the energy retrofit alternatives can repay the initial investment in a 30-year period. This could indicate that neither the cost of thermal energy for this neighborhood, lower than average because of the connection to the district heating network, nor the annual savings are sufficient to make the investment convenient. Consequently, if the policy maker is willing to promote its renovation, tailored subsidization strategies should be proposed.
- All the energy efficiency measures could allow for a positive life-cycle balance of CO2 emissions. This means that annual savings could compensate for the embodied and installation CO2 emissions associated to the implementation of the energy efficiency measures. As a whole, the amount of avoided CO2 emissions is strictly dependent on the choice of insulation material and can range from 55 kgCO2-e m−2 (extruded polystyrene) to 191 kgCO2-e m−2 (cellulose fiber) during 30 years of operation. Again, considering that environmentally friendly solutions are characterized by higher unitary prices, targeted incentives can be useful for the definition of an urban energy policy aimed at promoting not only energy efficiency, but also sustainability goals.
- Although minimal differences were registered between the energy efficiency measures selected in the cases of the three energy, economic and sustainability optima for the three clusters of buildings in the case study district, this is not true if the whole set of solutions belonging to the Pareto surface is studied. For instance, while extruded polystyrene is generally preferred for Cluster 1 and Cluster 2, more sustainable insulation materials, like mineral wool and cellulose fiber, are more frequently suggested for the renovation of Cluster 3 buildings. Furthermore, while insulation is often maximized for Cluster 1 buildings, the compliance with current national requirements is often judged sufficient in the case of Cluster 2 and Cluster 3. This could be exploited by policy makers to diversify subsidization strategies while pursuing a neighborhood or city-scale Pareto optimality.
- As a whole, the results achieved are insufficient if compared with the European targets for an 80% reduction of energy demand by 2050. Starting from the best refurbished case in terms of energy savings, an additional simulation was run, assuming the installation of a mechanical ventilation system. Both energy, economic and sustainability performances are found largely improved (i.e., 27.2 kWh m−2 a−1, −119 EUR m−2, 233 kgCO2-eq m−2), confirming that, for the analyzed case study, efficiency measures applied just to the building envelope were not enough and, at least, mechanical ventilation with heat recovery should be considered.
Conflicts of Interest
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|Layer||Thickness [m]||Thermal Conductivity [W m−1 K−1]||Density [kg m−3]||Specific Heat Capacity [J kg−1 K−1]|
|3||Thermal insulating plaster||0.040||0.10||300||1200|
|3||Concrete and clay block slab||0.240||0.80||1600||1110|
|Ground Floor||1||Timber flooring||0.015||0.13||600||1610|
|Layer||Thickness [m]||Thermal Conductivity [W m−1 K−1]||Density [kg m−3]||Specific Heat Capacity [J kg−1 K−1]|
|3||Thermal insulating plaster||0.030||0.10||300||1200|
|1||Fiber cement boards||0.015||0.60||2000||1050|
|3||Air gap + timber beams (R = 1.2 m2 K W−1)||0.130|
|Ground Floor||1||Ceramic tiles||0.010||1.30||2300||840|
|Layer||Thickness [m]||Thermal Conductivity [W m−1 K−1]||Density [kg m−3]||Specific Heat Capacity [J kg−1 K−1]|
|3||Mineral wool, ventilated air gap and covering layers||0.100||0.04||30||1030|
|Ground Floor||1||Timber flooring||0.010||0.13||600||1610|
|Type of Insulation||Element||Archetype 1||Archetype 2||Archetype 3|
|XPS||Wall||8 cm||8 cm||7 cm|
|Roof||5 cm||2 cm||6 cm|
|Mineral Wool||Wall||9 cm||9 cm||8 cm|
|Roof||6 cm||2 cm||7 cm|
|Cellulose Fiber||Wall||10 cm||11 cm||9 cm|
|Roof||7 cm||2 cm||8 cm|
|Glazing System||Ugl [W m−2 K−1]||SHGC [—]|
|DH||Double glazing with high SHGC||1.14||0.61|
|DL||Double glazing with low SHGC||1.10||0.35|
|TH||Triple glazing with high SHGC||0.61||0.58|
|TL||Triple glazing with low SHGC||0.60||0.34|
|Type of insulation||Cluster 1||Cluster 2||Cluster 3|
|Insulation Thickness||Cluster 1||Cluster 2||Cluster 3|
|Type of Window||Cluster 1||Cluster 2||Cluster 3|
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Haneef, F.; Pernigotto, G.; Gasparella, A.; Kämpf, J.H. Application of Urban Scale Energy Modelling and Multi-Objective Optimization Techniques for Building Energy Renovation at District Scale. Sustainability 2021, 13, 11554. https://doi.org/10.3390/su132011554
Haneef F, Pernigotto G, Gasparella A, Kämpf JH. Application of Urban Scale Energy Modelling and Multi-Objective Optimization Techniques for Building Energy Renovation at District Scale. Sustainability. 2021; 13(20):11554. https://doi.org/10.3390/su132011554Chicago/Turabian Style
Haneef, Fahad, Giovanni Pernigotto, Andrea Gasparella, and Jérôme Henri Kämpf. 2021. "Application of Urban Scale Energy Modelling and Multi-Objective Optimization Techniques for Building Energy Renovation at District Scale" Sustainability 13, no. 20: 11554. https://doi.org/10.3390/su132011554