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The large-scale and comprehensive artificial dataset introduced in this research reflects the energy demands of two neighbourhoods and with some reasonable limitations mimics monitoring campaigns otherwise collected on-site from buildings in use. The monitoring campaigns are created using white-box simulation models for single-family houses representing typical neighbourhoods in Flanders. The datasets are generated using Dymola and the IDEAS package embedded in TEASER. Each house varies in geometry, size, envelope properties, occupancy schedules, and installed gas heating systems. In this research, two datasets are created, one reflecting the properties of a low-performing building stock dating before the introduction of the EPBD (2006), and the other reflecting properties of a well-performing stock built after 2006. The envelope properties for older houses are allocated using EPC data grouped in four construction periods, while for newly built houses the properties are based on EPB reports, both were collected in Flanders. The datasets include heavy-weight houses in a detached, semi-detached, or terraced typology. Furthermore, the houses are simulated as one or two-zone buildings, depending on the number of floors which range from one to three floors. In the simulations, a natural infiltration model is implemented as well as a stochastic occupant behaviour model mimicking gains from occupants and appliances. Due to the complexity of the large-scale simulation, the heating system is post-processed in a data-driven approach and the heat source for both datasets are gas-fired heating systems. In total six system configurations are considered including condensing and non-condensing boilers with three types of domestic hot water (DHW) sub-systems (no integrated DHW, direct and with a storage tank). For all configurations, a variable production efficiency is considered dependent on the load ratio. The urban-scale simulation is carried out at a 10-minute frequency for the weather data assuming the location of Heverlee (Belgium) in the year 2016.The original purpose of this dataset was the development of statistical tools for the assessment of the heat loss coefficient of the building fabric. However, the generated artificial datasets provide a large spectre of usually difficult-to-measure inputs suitable to assess the importance of different components in the overall energy balance. Even though the original work looked into individual building behaviour, the datasets can be also used from an urban perspective for energy planning purposes.

This dataset contains the present-day, global, survey-based, and spatially explicit air-conditioning adoption rate dataset developed in Li et al. (2024), “Enhancing Urban Climate-Energy Modeling in the Community Earth System Model (CESM) through Explicit Representation of Urban Air-conditioning Adoption”, published in Journal of Advances in Modeling Earth Systems. It also contains the simulation results analyzed in the article. Details about this dataset (data sources, data collection and processing methods, simulation setup, etc.) are described in the article. The air-conditioning adoption rate dataset is publicly available in tabular, vector, and gridded formats. It is compatible with CESM, and can also be leveraged in other climate and energy modeling applications and socioeconomic or integrated assessment analyses. This dataset may be useful for multiple scientific communities regarding urban climate and energy, impacts, vulnerability, risks, and adaptation applications. For more detailed description, please refer to the README file (global_AC_adoption_rate_README.txt) included in the dataset.

This work proposes the use of a data-driven, agent-based model of building occupants’ activities and thermal comfort in an urban university campus in order to assess how district operation strategies can be leveraged to support the transition to flexible work arrangements. The results show that when users are given the flexibility to pursue more comfortable workspaces, they are still comfortable only 58% of the time. BuildSys '23: Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation ISBN:979-8-4007-0230-3

Urban building energy models (UBEM) have the potential to become integral planning tools for district energy systems due to the dynamic, interactive and complex nature of temporal building energy demand patterns. Although the demand patterns are related to the occupancy profiles of buildings supplied by district energy systems, occupant behavior in current UBEM approaches does not usually consider diversity in occupancy profiles among buildings of the same use-type. In this work, a novel method to create context-specific, data-driven commercial building occupancy profiles was used to generate diverse and non-diverse urban building occupant presence models (UBOP). Diverse UBOP randomly assigned occupancy profiles to buildings. Non-diverse UBOP assigned the data-driven mean or median profile to all buildings. ASHRAE standard profiles and occupant densities served as a baseline for comparison. The impact of diverse vs. non-diverse UBOP was assessed by comparing UBEM simulations for district energy efficiency benchmarking, renewable energy integration potential, and district energy system design, using a case study in Singapore. The results demonstrate that, because of the relationship between occupant presence and building systems operation, occupancy profiles are highly sensitive parameters for district energy demand predictions. For the case study, the energy demand estimation is significantly influenced by the shape of occupancy profiles. In particular, the choice of UBOP influences the cooling demand to the degree that district cooling system design decisions might be impacted. Therefore, it is advisable to use diverse UBOP and to run probabilistic UBEM simulations for district energy system design. Applied Energy, 277 ISSN:0306-2619 ISSN:1872-9118

District-scale building energy models can be a powerful tool for the integration of renewable energy sources and efficiency measures in urban areas. One key limitation of these models, however, has been their rather simplified treatment of building occupants. Since it is their activities which create the needs for energy in an area, an improved analysis of the effects of occupants on demand at the district scale is needed. This paper presents a novel population-based approach (PopAp) inspired by agent-based transportation models, in which a population of occupants was defined based on class and employee registers and each was given an individual daily schedule. This approach was then used to assess the effect of occupant presence modeling on district-scale energy demand simulations by comparing the data-centric PopAp method to standard-based deterministic and stochastic approaches. The maximum number of occupants in the area was found to be 33% higher for the deterministic model compared to the data-centric PopAp results, a deviation that was especially pronounced in education buildings. The results for space heating, space cooling and electricity demand for lighting and appliances show that while the mean deviation between models on a yearly basis is within 10% for all demands, on an hourly scale the deviation for space cooling and electricity exceeded 15%. Given the importance of the hourly scale for peak demand prediction for technology sizing, more detailed occupant modeling approaches should be considered when planning energy systems. Building and Environment, 181 ISSN:0360-1323 ISSN:0360-1323

Input uncertainty is one of the major obstacles urban building energy models (UBEM) must tackle. The aim of this paper was to quantify the effects of two of the main sources of stochastic uncertainty, namely building occupants and urban microclimate, on electrical and thermal supply system sizing at the district scale. In order to analyze the effects of the former, three different methods of occupant modeling were implemented in a UBEM. The effects of the urban heat island on system sizing were studied through the use of measured temperature data from a weather station in the case study district compared to measured data from a national weather station. The methods developed were used to assess the sizing and costs of centralized and decentralized technologies for a case study in central Zurich, Switzerland. The choice of occupant modeling approach was found to affect the district’s total annualized costs for space heating and cooling by ±5%, whereas for the costs of electricity the variation was ±8%. Regarding outdoor temperature, the effects on the heating demands proved be negligible, however the costs of the cooling alternatives were found to vary by about 4% at the district scale due to the effect of urban climate, for individual buildings this deviation was as high as 40%.

This paper presents a building-level analysis of almost 600,000 houses in London,using EPC data alongside 3DStock, a new highly detailed urban model.Focussing on the building envelope (specifically roofs, walls and glazing), the paper examines the current condition of the stock, as well as the opportunities for improving energy efficiency as defined by the EPC recommendations.Using highly detailed building-level data, the areas of single-glazed windows, uninsulated walls, and poorly insulated lofts are quantified across the sample. It also examines the magnitude of this low-efficiency envelope that is not currently recommended for improvement in the EPCs. Finally, the paper estimates the total retrofit potential for houses in London.

This paper describes work in progress toward an urban-scale system aiming to reduce energy use in neighboring buildings by providing three components: a database for accessing past and present weather data from high quality weather stations; a network for communicating energy-saving strategies between building owners; and a set of modeling tools for real-time building energy simulation.