Urban building energy modeling (UBEM) seeks to evaluate strategies to optimize building energy use at urban scale to support a city's building energy goals. Prototype building models are usually ...developed to represent typical urban building characteristics of a specific use type, construction year, and climate zone, as detailed characteristics of individual buildings at urban scale are difficult to obtain. This study investigated the Italian building stock, developing 46 building prototypes, based on construction year, for residential and office buildings. The study included 16 single-family buildings, 16 multi-family buildings, and 14 office buildings. Building envelope properties and heating, ventilation, and air conditioning system characteristics were defined according to existing building energy codes and standards for climatic zone E, which covers about half the Italian municipalities. Novel contributions of this study include (1) detailed specifications of prototype building energy models for Italian residential and office buildings that can be adopted by UBEM tools, and (2) a dataset in GeoJSON format of Italian urban buildings compiled from diverse data sources and national standards. The developed prototype building specifications, the building dataset, and the workflow can be applied to create other building prototypes and to support Italian national building energy efficiency and environmental goals.
•46 prototypes were defined for Italian office, single and multi-family buildings.•The prototype buildings are representative of the most common climatic zone.•The characterization of the prototypes highlighted the difficulty of obtaining data.•The methodology aims to be replicable supporting district level retrofit strategies.•Integrated data collection will create the description of the whole building stock.
•Bottom-up physics-based UBEM tools have high spatiotemporal detailed results.•Each tool overlooks some aspects of the simulation and deepens some others.•Users should consider the intrinsic ...characteristics and practical limitations.•Two major development trends for UBEM tools emerged: city scale and district scale.
Regulations corroborate the importance of retrofitting existing building stocks or constructing new energy-efficient districts. There is, thus, a need for modeling tools to evaluate energy scenarios to better manage and design cities, and numerous methodologies and tools have been developed. Among them, Urban Building Energy Modelling (UBEM) tools allow the energy simulation of buildings at large scales. Choosing an appropriate UBEM tool, balancing the level of complexity, accuracy, usability, and computing needs, remains a challenge for users. The review focuses on the main bottom-up physics-based UBEM tools, comparing them from a user-oriented perspective. Five categories are used: (i) the required inputs, (ii) the reported outputs, (iii) the exploited workflow, (iv) the applicability of each tool, and (v) the potential users. Moreover, a critical discussion is proposed, focusing on interests and trends in research and development. The results highlighted major differences between UBEM tools that must be considered to choose the proper one for an application. Barriers of adoption of UBEM tools include the needs of a standardized ontology, a common three-dimensional city model, a standard procedure to collect data, and a standard set of test cases. This feeds into future development of UBEM tools to support cities’ sustainability goals.
•The procedure intends to increase reliability in building modelling and simulation.•The procedure uses machine learning algorithms to infer occupant-related input data.•Machine learning extracts ...occupant-related information from smart meters readings.•Uncertainty due to occupant-related data accounts for 8% of the heating energy need.
Improving the reliability of energy simulation outputs is becoming a pressing task to reduce the performance gap between the design and the operation of buildings. Occupant behaviour modelling is one of the most relevant sources of uncertainty in building energy modelling and is typically modelled via a priori choices made by modellers. Thus, an improvement in the description of occupant behaviour is needed. To this regard, the availability of smart meter recordings might help to generate more reliable input data for building energy models. This paper discusses a novel data-driven procedure that enables to create yearly occupancy and occupant-related electric load profiles to inform building energy modelling, using a typical uneven database made available by energy operators. The procedure is subdivided into three main tasks. The first has the intent to detect representative occupant-related electric load profiles from smart meters readings. The second task aims to generate yearly occupancy profiles from the same database. The last task assesses the impact of the generated occupancy and occupant-related electric load profiles on building energy simulation outputs. The procedure is applied to the case study of a multi-residential building in Milan, Italy and is meant to show the possibility to overcome deterministic inputs that might have little relation with the actual building operation. It showed a substantial improvement in the reliability of building energy simulation and that occupant related load profiles may account for about 8% of the building's energy need for space heating.
Heat emissions from buildings are part of anthropogenic heat leading to urban overheating. This paper aims to assess how technologies (i.e., energy conservation measures - ECMs), used to decrease ...energy use, may also reduce heat emissions from buildings. This study employs the physics-based engine EnergyPlus to simulate the main components of heat emissions from buildings to ambient air: envelope, zone, and systems. Hourly simulations are run for IECC single- and multi-family reference models with three representative climates: Miami, Baltimore, and Chicago. The results show that the performance of ECMs varies among weather, seasons, and residential typologies. Particularly, some ECMs (i.e., cool coatings, heat pumps, additional insulation, energy-awareness occupants) show a strong decrease in heat emissions, yet they are not always correlated with proportional decreases in energy use. When all ECMs are combined, the reductions are larger on heat emissions (89%) than on site energy (65%) from the base cases. During summer in Miami, the combination of ECMs shows a decrease in heat emissions from the building surface component of 80% during daytime, 92% for the HVAC component and a counterbalanced increase in the zone component of 88%, bringing to a daily decrease in total heat emissions. The main contributions of this study are quantifying how typical ECMs influence residential building heat emissions using EnergyPlus simulations and informing urban planners and stakeholders on prioritizing measures for mitigating urban overheating problems.
•EnergyPlus models are used to assess heat emissions from buildings to ambient air.•Simulations were run for single- and multi-family buildings across three climate zones.•A suite of energy conservation measures (ECMs) are compared based on heat emissions.•Annual and monthly heat emissions vary by ECM, climate and building type.•Results support urban planning and inform mitigation of urban overhaeting
Urban Building Energy Modeling (UBEM) is essential for urban energy-related applications. Its generation mainly requires four data inputs, including geometric data, non-geometric data, weather data, ...and validation and calibration data. A reliable UBEM depends on the quantity and accuracy of the data inputs. However, the lack of available data and the difficulty in determining stochastic data are two of the main barriers in the development of UBEM. To bridge the research gaps, this paper reviews appropriate acquisition approaches for four data inputs, learning from both building science and other disciplines such as geography, transportation and computer science. In addition, detailed evaluations are also conducted in each part of the study, and the performance of the approaches are discussed, as well as the availability and cost of the implemented data. Systematic discussion, multidisciplinary analysis and comprehensive evaluation are the highlights of this review.
•Appropriate and potential data acquisition approaches for UBEM are summarized.•The approaches are learnt from both building science and other disciplines.•Detailed evaluations are conducted on the performance of the approaches.•The availability and cost of the implemented data are also analyzed.
•A novel physics-based bottom-up approach is developed to calculate heat emissions from buildings to ambient air.•Heat emissions are grouped into three components: envelope, zone ...exhaust/exfiltration, and HVAC system.•Heat emissions of 16 building types in 4 climates and 2 efficiency levels are simulated.•Daily and seasonal patterns of heat emissions and their variations by building type, climate and efficiency are analyzed.•Detailed hourly anthropogenic heat from buildings can improve urban microclimate modeling.
Heat emissions from buildings is a significant source of anthropogenic heat influencing the urban microclimate; however, they are usually oversimplified in urban climate and microclimate modeling. This study developed a bottom-up physics-based approach to calculate heat emissions from buildings to the ambient air and implemented the approach in EnergyPlus. A simple result verification was conducted by comparing the EnergyPlus simulated results against the spreadsheet calculations. Simulations covering 16 commercial building types, four climates, and two energy efficiency levels were conducted to understand and evaluate the building heat emissions and their temporal patterns as well as three major components: (1) building envelope (convective heat transfer to ambient air), (2) zones (air exfiltration and exhaust air), and (3) HVAC systems (relief air and heat rejection from condensers or cooling towers). The main findings are: (1)heat emissions are usually higher than the site energy use (about 2.5 times), and their dynamics should be considered; (2)building characteristics and their energy systems lead to differences in heat emission contributions from the three components, and their dynamics, for example, in the warehouse models, the envelope component accounts for 90.4%, while it is 12.7% for the large office models; (3) for most building typologies, the climate has a strong impact on heat emissions, for example, buildings with dominant heat emissions from the zone exhaust air and/or the HVAC reject heat, a general decrease in heat emissions in hotter climates is observed, while envelope-dominated buildings show the opposite; and (4)building technologies that reduce energy use in buildings may perform differently in reducing heat emissions. The developed heat emissions calculation method can be adopted in EnergyPlus and most other building energy modeling programs. It can provide dynamic building heat emissions as an input to urban climate computational fluid dynamics (CFD) models at a higher spatial and temporal resolution than is currently available, to improve the simulation accuracy of the urban microclimate and capture the urban heat island effect and urban overheating.
•COVID-19 lockdown led to a significant increase in residential electricity usage.•Shift in energy usage from morning peak to central hours registered during lockdown.•The study provides a set of ...daily load profiles for residential buildings.•Remote working can have a significant impact on energy use in residential buildings.•Findings can assist cities analysts, regulators and businesses in weighing effects of remote working.
The COVID-19 pandemic had a profound impact on society, causing changes in various aspects of people's lives, including their energy use habits. This has prompted a need for checking and updating standard energy use profiles, particularly for residential electricity use. To address this topic, a study was conducted on 24 multifamily buildings in Milan, using clustering to extract patterns from a database of quarter-hourly electricity use data from 2019 to 2020. This study found an increase in electricity usage during the COVID-19 lockdown period for residential buildings, likely associated with the imposed restrictions. The research also highlighted a shift in energy usage from the morning peak to the central hours of the day during the working days of the lockdown period, while a gradual increase in electricity usage throughout the day and no morning peak was observed during the Autumn (post-COVID) period. The findings can assist regulators and businesses in weighing the benefits and drawbacks of remote working and provide modellers with a complete set of daily load profiles for an Italian residential case study.
Heat emissions from buildings are part of anthropogenic heat leading to urban overheating. This paper aims to assess how technologies (i.e., energy conservation measures - ECMs), used to decrease ...energy use, may also reduce heat emissions from buildings. This study employs the physics-based engine EnergyPlus to simulate the main components of heat emissions from buildings to ambient air: envelope, zone, and systems. Hourly simulations are run for IECC single- and multi-family reference models with three representative climates: Miami, Baltimore, and Chicago. The results show that the performance of ECMs varies among weather, seasons, and residential typologies. Particularly, some ECMs (i.e., cool coatings, heat pumps, additional insulation, energy-awareness occupants) show a strong decrease in heat emissions, yet they are not always correlated with proportional decreases in energy use. When all ECMs are combined, the reductions are larger on heat emissions (89%) than on site energy (65%) from the base cases. During summer in Miami, the combination of ECMs shows a decrease in heat emissions from the building surface component of 80% during daytime, 92% for the HVAC component and a counterbalanced increase in the zone component of 88%, bringing to a daily decrease in total heat emissions. The main contributions of this study are quantifying how typical ECMs influence residential building heat emissions using EnergyPlus simulations and informing urban planners and stakeholders on prioritizing measures for mitigating urban overheating problems.
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The reliability of building performance simulation is hindered by several uncertainties, with aleatory uncertainty due to occupant behavior being one of the most critical. The present ...study aims to assess the propagation of uncertainty due to the adoption of stochastic models for modeling Occupant Presence and Actions (OPAs) available in the literature on the annual electric energy use of a reference office building. To this purpose, a global sensitivity analysis was designed and carried out by analyzing model inputs and energy outputs of 144 permutations of 15 different stochastic models for OPAs for a total of 7200 simulations. Building energy use computed considering stochastic OPAs modeling resulted in being sensibly higher than the reference value estimated assuming scheduled occupancy and rule-based occupant’s actions as suggested by reference standards. The median value of the electric energy use was 58.6% higher than the base case electric energy use. Furthermore, the stochastic models used to model window operation have the highest effect on energy output, followed by light switch-off, and occupancy models. Light switch-on models showed a lower influence on the overall building energy performance. Furthermore, the Generalized Estimating Equations method was adopted to assess the interdependence among stochastic models for OPA and showed that changing the stochastic model in window operation, occupancy estimation, and light switch-off behavior generates a considerable difference in building’s energy performance. Contrariwise, the available stochastic models for light switch-on and blind operation perform quite similarly among each other and have a limited impact on a building’s energy performance.
This study explores the applicability of data augmentation techniques for reconstructing missing energy time-series in limited data regimes. In particular, multiple synthetic copies of a relatively ...small training dataset are stacked together with pseudo-random noise. First, an existing convolutional denoising autoencoder is selected from a previous work, as the base imputation model of this study. Then, an optimal augmentation rate, which minimizes the training set of the model, is chosen based on the preliminary results obtained from one building. The results proved that, augmenting 80 times a nine days-long training set could reduce the initial average root mean squared error (RMSE) by 37% and 48%, for continuous and random missing scenarios. Additionally, the augmented model outperformed the benchmark methods with 23% and 12% lower average RMSE. No additional tuning or calibration costs were required for the existing base imputation model. Therefore, the presented data augmentation technique could significantly reduce the expensive computational costs associated with deep learning models.
•A data augmentation strategy for existing deep learning models is proposed.•The existing deep learning model exploits a denoising criterion.•Missing energy time-series can be accurately imputed with limited training samples.•No tuning or calibration costs are required for the existing deep learning model.
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