Individual Building Level Research Articles

Deep Neural Network for Predicting Changing Market Demands in the Energy Sector for a Sustainable Economy

Increasing access to power, enhancing clean cooking fuels, decreasing wasteful energy subsidies, and limiting fatal air pollution are just a few of the sustainable development goals that all revolve around energy (E). Energy-specific sustainable development objectives were a turning point in the global shift towards a more sustainable and just system. By understanding energy resources, markets, regulations, and scientific studies, the country can progress more quickly towards a sustainable economy (SE). Investment in renewable energy industries is hampered by institutional obstacles such as market-controlled procedures and inconsistent supporting policies. Power plant building is currently incompatible with existing transmission and distribution networks, posing significant risks to investors. Deep neural networks (DNN) are specifically investigated in this article for energy demand forecasting at the individual building level. Other relevant information is supplied into fully connected layers along with the convolutional output. A single customer’s power usage data were used and analyzed for the final fuel and electricity consumption by various energy sources and consumer groups to test the DNN-SE technique. The energy intensity and labor productivity indexes for several economic sectors are displayed. A wide range of economic activities are examined to determine their impact on environmental pollution indicators, greenhouse gas emissions, and other air pollutants. A more effective and comprehensive energy efficiency strategy should be implemented to lower emission levels at lower prices. Research-based conclusions must be enhanced to help policymaking. The results of the experiment using the proposed method show that it is possible to predict 98.1%, grow at 96.8%, meet 98.5% of electricity demand, use 97.6% of power, and have a renewable energy ratio of 96.2%.

Holistic assessment methodology for Positive Energy Districts

Globally, cities are responsible for most of the world’s energy consumption and most of the carbon dioxide emissions. Therefore, the efficient transformation of urban energy systems is becoming a crucial step towards carbon neutrality and more effective climate protection. The Positive Energy District (PED) concept has been pointed out as one of the key principles of this path. The Positive Energy District concept leads towards an integrated and sustainable urban development with an emphasis on the use of renewable energy sources and attention shifting from energy performance at the level of individual buildings to the level of the entire district. However, PEDs represent a relatively new and still not sufficiently explored area in the field of sustainable development.The aim of this paper is to describe a new holistic assessment and innovative process of stakeholder engagement to identify Positive Energy Districts in the early development phase that arises within the PED-ID project. The early development phase is crucial since the structure of the urban area is being decided along with its sustainability and climate protection levels.The assessment methodology is based on the creation and optimization of energy balance scenarios.Case studies of existing urban areas have been developed in order to verify the methodology.It is concluded that this innovative methodology devised for PED evaluation, together with strengthening of knowledge-based and decision-making framework, will help to accelerate the process of transforming the current urban energy systems into more efficient and renewable systems.

Urban road network design for alleviating residential exposure to traffic pollutants: Super-block or Mini-block?

This study addresses the knowledge gap between urban road network design and residents’ exposure to traffic pollutants. Computational fluid dynamics (CFD) simulations are employed to evaluate three representative design strategies: (1) Mini-block with 1-lane roads in high density; (2) Regular-block with 2-lane roads in median density; and (3) Super-block with 4-lane roads in low density. Daily activities of different age groups are considered to examine the vulnerable population, using the personal intake fraction (PIg, ppm) that represents the emitted pollutant inhaled averagely by each resident. By changing from Super-block to Mini-block, the outdoor PIg decreases for the young and adult groups, but remains largely unchanged for the elderly group. The indoor PIg is given for individual building levels. The Mini-block leads to a relatively uniform distribution of indoor PIg and the value goes up to 0.251 ppm for the elderly group. For the Regular-block and Super-block, residents living in buildings with a long roadside façade tend to bear an unequal level of pollutant burden. With the Super-block, elderly residents in 25% of the buildings have an high indoor PIg with the maximum value of 0.554 ppm, nearly four times larger than that of 0.121 ppm in buildings not close to the roads. The results can serve as a basis for supporting the new design and urban redevelopment to improve neighborhood liveability.

The Balance between Energy Efficiency and Renewable Energy for District Renovations in Denmark

Emissions can be reduced by increasing the energy efficiency of buildings and supply systems or increasing the share of renewable energy in the energy system. Denmark has a long tradition for district heating and today it supplies more than 65% of dwellings, which indicates that a major part of the transition from fossil fuels to renewables can be achieved at the supply system level rather than on the individual building level. This paper presents calculations performed on a generic Danish district undergoing major renovation. The generic district is based on an existing district in Aalborg and consists of 1019 dwellings spread over three different building typologies. The purpose of the investigation was to determine which combination of energy saving measures would achieve the optimal level of energy efficiency. Calculations were made with average data for district heating in Denmark, district heating based on natural gas, and district heating from renewable energy sources, such as solar heating, biofuels, and heat pumps, respectively. The calculations include costs for investment, maintenance, and operating as a function of the primary energy needs. Global warming potential (GWP) was calculated and included CO2 emissions from space heating, domestic hot water, and electricity for operation and household. The calculations show that for the generic Danish district, which is already connected to a district heating network, the optimal solution is to add 200 mm insulation to roofs (in total 300 mm) and 150 mm insulation to walls (in total 200 mm) and replacing the existing windows with new three-layer low energy windows. Furthermore, the calculations show that in a future scenario with a significantly higher level of renewable energy in the energy system, a shift to individual heat pumps can reduce total emissions by up to 1.5 kg CO2 eq/m2 per year (20% reduction) at an additional cost of EUR 8.0/m2 per year (40% increase). The calculations described in this paper are part of a larger investigation carried out in IEA EBC Annex 75.

Urban form and COVID-19 cases and deaths in Greater London: An urban morphometric approach.

The COVID-19 pandemic generated a considerable debate in relation to urban density. This is an old debate, originated in mid 19th century’s England with the emergence of public health and urban planning disciplines. While popularly linked, evidence suggests that such relationship cannot be generally assumed. Furthermore, urban density has been investigated in a spatially coarse manner (predominantly at city level) and never contextualised with other descriptors of urban form. In this work, we explore COVID-19 and urban form in Greater London, relating a comprehensive set of morphometric descriptors (including built-up density) to COVID-19 deaths and cases, while controlling for socioeconomic, ethnicity, age and co-morbidity. We describe urban form at individual building level and then aggregate information for official neighbourhoods, allowing for a detailed intra-urban representation. Results show that: (i) control variables significantly explain more variance of both COVID-19 cases and deaths than the morphometric descriptors; (ii) of what the latter can explain, built-up density is indeed the most associated, though inversely. The typical London neighbourhood with high levels of COVID-19 infections and deaths resembles a suburb, featuring a low-density urban fabric dotted by larger free-standing buildings and framed by a poorly inter-connected street network.

Subgrid-scale modeling of tsunami inundation in coastal urban areas

Numerical tsunami inundation simulations using high-resolution (HR) topographical data are important to predict tsunami damage in urban areas at individual building levels accurately but are challenging due to the high computational cost. To address this issue, a subgrid-scale (SGS) model of tsunami inundation, called the individual drag force model (iDFM), is developed. In this model, the sum of the building drag forces in a computational mesh is fed back to the flow field by taking into account spatial information about multiple structures in the mesh. To evaluate the model performance, idealized numerical experiments using simple urban topography with several types of spatial layout patterns and hindcast physical experiments focusing upon Onagawa Town affected by the 2011 Tohoku, Japan tsunami as a historical event are conducted. Results are compared with HR tsunami simulations using building-resolved topographical data, called the structure resolving model (SRM). In general, the iDFM (and other SGS models) characterizes the effects of structures as resistance elements that reduce the fluid velocity in momentum conservation. On the other hand, the SRM evaluates these effects as topography and feeds them back into not only momentum conservation but also mass conservation and wet/dry boundary conditions. Therefore, the iDFM can model the overall inundation characteristics, such as the limits of inundation, more than 10 2 times efficiently compared with the SRM, although it cannot capture local phenomena driven by structures, such as surface rising due to blocking and contracting current between structures. In both numerical and historical tsunami (Onagawa) cases, there were small differences between the modeled inundation characteristics, such as the maximum surface elevation and fluid velocity, and the time series of inundation leading edges determined by the iDFM due to the computational grid cell sizes. The observed differences between the idealized numerical experiment and the historical tsunami case can be affected by the drag coefficient and the Froude number during the tsunami run-up.

SARS-CoV-2 RNA and N Antigen Quantification via Wastewater at the Campus Level, Building Cluster Level, and Individual-Building Level.

Monitoring wastewater for SARS-CoV-2 from populations smaller than those served by wastewater treatment plants may help identify small spatial areas (subsewersheds) where COVID-19 infections are present. We sampled wastewater from three nested locations with different sized populations within the same sewer network at a university campus and quantified SARS-CoV-2 RNA using reverse transcriptase droplet digital polymerase chain reaction (PCR). SARS-CoV-2 RNA concentrations and/or concentrations normalized by PMMoV were positively associated with laboratory-confirmed COVID-19 cases for both the sewershed level and the subsewershed level. We also used an antigen-based assay to detect the nucleocapsid (N) antigen from SARS-CoV-2 in wastewater samples at the sewershed level. The N antigen was regularly detected at the sewershed level, but the results were not associated with either laboratory-confirmed COVID-19 cases or SARS-CoV-2 RNA concentrations. The results of this study indicate that wastewater monitoring based on quantification of SARS-CoV-2 RNA using PCR-based methods is associated with COVID-19 cases at multiple geographic scales within the subsewershed level and can serve to aid the public health response.

Statistical data-driven analysis and modelling of total energy use in new or thoroughly renovated single-family houses

The subject of this paper is to analyse how the regulatory calculated energy use relates to the real total energy use for new or thoroughly renovated Flemish single-family houses where electricity is the only energy carrier. Additionally, the authors determine whether statistical data-driven models can help inform current and future home owners and tenants about their energy use (and thus also potential energy savings when applying energy saving measures). These questions are investigated by using housing datasets from the Flemish energy performance database and real energy use data from the Belgian grid operator. The paper comprises outlined database cleansing and filtering choices and enlightening statistical database analyses and figures. The results clearly demonstrate that the regulatory calculation method poorly estimates the real energy use (RMSE-/MAE-results of respectively 7227 kWh/y and 5242 kWh/y), yet both are moderately correlated (τ = .548, p < .001). Further, the statistical regression models show good results at stock level for new or thoroughly renovated Flemish single-family houses (where electricity is the only energy carrier) (adj. R2 up to 65.3%). Nevertheless, their performance at individual building level is still limited and considered too poor for inference as a considerable part of the variance is left unexplained.

Residential energy flexibility characterization using non-intrusive load monitoring

To accelerate progress in building sustainability as well as to aid balance supply and demand in the presence of renewable energy generation, a tailored characterization method for the energy flexibility (EF) of buildings is needed. In this paper, a novel two-stage non-intrusive EF characterization method is proposed. In the first stage, unlike the previous studies in which an individual meter is installed on appliances to extract their consumption pattern, a novel unsupervised event-matching non-intrusive load monitoring method is utilized which is time and cost-effective. Moreover, previous research characterize the EF considering as early and as late as possible appliances’ start-time. However, the usage behavior of consumers affects the start-time of appliances. To tackle this issue, in the second stage of the proposed method, the usage behavior of consumers is taken into account for the EF characterization. The proposed method is verified in an individual building level and aggregated level including 50 residential buildings. The obtained results show that the proposed usage behavior-oriented method, characterizes the available aggregated EF with higher accuracy, without adding complexity to the system. These results can be used by aggregators to harness the available EF of buildings to flatten demand consumption by incentivizing potential consumers.

Present vs. Future Property Losses From a 100-Year Coastal Flood: A Case Study of Grand Isle, Louisiana

Louisiana, U.S.A., is among the world's most vulnerable places to coastal flooding. Increasing frequency/intensity of natural hazards under climate change scenarios is expected to exacerbate Louisiana's coastal flood risk. Although many factors are involved, land subsidence from marshland compaction and underground resource extraction, shoreline erosion accelerated by eustatic sea level rise (ESLR), and tropical-cyclone-induced storm surge are among the most important. While past research has focused on flood risk assessment and mitigation strategies, including climate change scenarios, few studies examine all of these factors collectively. This study shows present pluvial flood depth and the contributions of additional coastal subsidence and ESLR toward future (2050) pluvial flooding. Then, current tropical-storm-induced storm surge and future storm surge depth modeled by Louisiana's Coastal Protection and Restoration Authority (CPRA) under climate change scenarios are presented. Present and future 100-year pluvial flood and storm surge losses (separately, 2020$) to structures and their contents are estimated at the individual building level for Grand Isle, Louisiana, a barrier island town of ecological, economic, historical, recreational, cultural, and aesthetic treasure. Results suggest that the average 100-year pluvial flood depth in buildings will increase by 1.35 feet by 2050, with subsidence contributing over 82% of this total. Subsidence is projected to escalate structure and content losses by ~17% above losses in 2017, while ESLR may contribute ~3% above 2017 losses. A 100-year tropical-cyclone-induced storm surge event amid a “low” scenario of environmental change as defined by CPRA would increase Grand Isle's structure and content losses by 68–74% above the 2017 value by 2027, 141–149% by 2042, and 346–359% by 2067. The (“high”) scenario of environmental change would increase the 100-year storm surge losses by 85–91% above the 2017 value by 2027, 199–218% by 2042, and 407–415% by 2067. Outcomes from this study will offer a more realistic risk assessment model and will direct flood risk managers, property owners, and other stakeholders to build a comprehensive framework to minimize future flood risk in one of the most vulnerable sites in the U.S.A. to coastal flooding.

Comparison of Neighborhood-Scale, Residential Property Flood-Loss Assessment Methodologies

Leading flood loss estimation models include Federal Emergency Management Agency’s (FEMA’s) Hazus, FEMA’s Flood Assessment Structure Tool (FAST), and (U.S.) Hydrologic Engineering Center’s Flood Impact Analysis (HEC-FIA), with each requiring different data input. No research to date has compared the resulting outcomes from such models at a neighborhood scale. This research examines the building and content loss estimates by Hazus Level 2, FAST, and HEC-FIA, over a levee-protected census block in Metairie, in Jefferson Parish, Louisiana. Building attribute data in National Structure Inventory (NSI) 2.0 are compared against “best available data” (BAD) collected at the individual building scale from Google Street View, Jefferson Parish building inventory, and 2019 National Building Cost Manual, to assess the sensitivity of input building inventory selection. Results suggest that use of BAD likely enhances flood loss estimation accuracy over existing reliance on default data in the software or from a national data set that generalizes over a broad scale. Although the three models give similar mean (median) building and content loss, Hazus Level 2 results diverge from those produced by FAST and HEC-FIA at the individual building level. A statistically significant difference in mean (median) building loss exists, but no significant difference is found in mean (median) content loss, between building inventory input (i.e., NSI 2.0 vs BAD), but both the building and content loss vary at the individual building scale due to difference in building-inventory-reported foundation height, foundation type, number of stories, replacement cost, and content cost. Moreover, building loss estimation also differs significantly by depth-damage function (DDF), for flood depths corresponding with the longest return periods, with content loss differing significantly by DDF at all return periods tested, from 10 to 500 years. Knowledge of the extent of estimated differences aids in understanding the degree of uncertainty in flood loss estimation. Much like the real estate industry uses comparable home values to appraise a home, flood loss planners should use multiple models to estimate flood-related losses. Moreover, results from this study can be used as a baseline for assessing losses from other hazards, thereby enhancing protection of human life and property.

Comparison of short-term electrical load forecasting methods for different building types

The transformation of the energy system towards volatile renewable generation, increases the need to manage decentralized flexibilities more efficiently. For this, precise forecasting of uncontrollable electrical load is key. Although there is an abundance of studies presenting innovative individual methods for load forecasting, comprehensive comparisons of popular methods are hard to come across.In this paper, eight methods for day-ahead forecasts of supermarket, school and residential electrical load on the level of individual buildings are compared. The compared algorithms came from machine learning and statistics and a median ensemble combining the individual forecasts was used.In our examination, nearly all the studied methods improved forecasting accuracy compared to the naïve seasonal benchmark approach. The forecast error could be reduced by up to 35% compared to the benchmark. From the individual methods, the neural networks achieved the best results for the school and supermarket buildings, whereas the k-nearest-neighbor regression had the lowest forecasting error for households. The median ensemble narrowly yielded a lower forecast error than all individual methods for the residential and school category and was only outperformed by a neural network for the supermarket data. However, this slight increase in performance came at the cost of a significantly increased computation time. Overall, identifying a single best method remains a challenge specific to the forecasting task.

Micro-Geographies of Information and Communication Technology Firms in a Shrinking Medium-Sized Industrial City of Ostrava (Czechia)

We aimed to explain the spatial distribution of information and communication technology (ICT) firms in the city of Ostrava as an example of a medium-sized, shrinking, polycentric industrial city. The primary research question was to what extent micro-geographic location factors affect the current spatial clustering of ICT firms in polycentric cities characteristic by relatively weak urbanization economies and mostly routine character of ICT activities. We analyse and test the effects of the urban form at the level of urban blocks and individual buildings (considering their height, technical condition, age and dominant function) on the clustering of ICT firms of various sizes and ownership statuses. The inquiry was based on a detailed field mapping (using ArcGIS Collector) of ICT firms and physical/functional characteristics of the buildings and their immediate surroundings. ICT firms are significantly spatially concentrated in the historic city centre and inner city. Spatial patterns of ICT firms focused on less knowledge-intensive, routine and/or lower value-added functions do not differ fundamentally from innovative firms developing new products. Preference of denser, walkable, mixed-use neighbourhoods in urban cores/inner cities can be found in the group of firms focusing on routine functions: rather for larger than for smaller firms and domestic than foreign-owned firms.

School psychologists as agents of change: Implementing MTSS in a rural school district

AbstractThis case study focuses on the role of school psychologists during the transition to and implementation of multi‐tiered systems of support (MTSS) in a small, rural school district. It provides guidance to other school psychologists in similar rural districts on how to support a system‐level change from the individual building level to the entire district level. The case study highlights the ways that the school psychologists' roles have changed due to the MTSS process as well as challenges that the district overcame. Further, it showcases that school psychologists can be valuable team members and can successfully advocate for change. It concludes with a summary of specific core components that have been essential in effectively implementing the frameworks at the school district level. Last, recommendations are provided to other practitioners regarding anticipated changes to MTSS given the extended school closure occurring due to the global pandemic.

Development of a Measure of State-Level Supports for School Nursing Services Delivery.

States have key roles and responsibilities in protecting and promoting the health of all of school-age youth. Though assessment and planning instruments exist to support quality school nursing service delivery at individual school building, community, and district levels, no comparable measure was available to assess state-level support for the delivery of quality school nursing. This project, conducted in three phases, resulted in the development of a novel measure to identify state-level infrastructure supports for school nursing services delivery. The State School Health Infrastructure Measure is comprised of seven domains with 24 indicators and demonstrated initial content validity and test-retest reliability. States can use this measure to self-assess, identify, benchmark, prioritize, and address state-level infrastructure strengths and gaps related to supporting the delivery of high-quality, equitable school nursing services.

An exploratory model to characterize the vulnerability of coastal buildings to storm surge flooding in Miami-Dade County, Florida

Miami-Dade County is highly susceptible to storm surge flooding. The current Federal Insurance Rate Map (FIRM), used to determine flood insurance rates for buildings, is an excellent resource yet lacks attributes that could provide a finer-resolution of actual vulnerability for building level characteristics from one parcel to the next within the same flood zone. This study developed a novel Storm Surge Building Vulnerability (SSBV) model to provide initial protocols for developing storm surge vulnerability county maps. The model was tested on transversal sections of 1254 buildings selected from Miami Beach, East Little Havana, and Sweetwater. Buildings in the county were determined to be moderately vulnerable to storm surge flooding. High-rises, masonry buildings, and buildings whose first habitable space is elevated above the ground constitute the majority of low and very low vulnerability buildings, but 97% of high and very high vulnerability buildings are between 0 and 2 story tall and three-quarters of these have slab-on-grade finished floor elevation. Our model exposes limitations of FIRM designated hazard zones: buildings within the same zone exhibit different vulnerability characteristics. This indicates the importance of creating initial protocols to consider upgrading flood insurance rate requirement from zonal categories to individual building level.

An information sharing strategy based on linked data for net zero energy buildings and clusters

Buildings now incorporate increasing levels of renewable energy to the point where net zero energy buildings (NetZEBs) and net zero energy clusters (NetZECs) have the potential to become widely used. Information exchange among stakeholders is essential to enable energy sharing among buildings that form a NetZECs. However, traditional, centralised energy management usually places emphasis on the supply side and does not address data exchange among individual stakeholders. This paper proposes an information sharing strategy based on linked data for improved management of NetZEBs and NetZECs. This strategy systematically integrates stakeholders' engagement by using energy performance indicators as information carriers and linked data as engagement channels. A case study proves the advantages of the developed strategy for improving the energy balance at individual building level, building cluster level, and community level. It is found that the strategy could help achieve NetZEBs and NetZECs through empowering stakeholders' interaction and information sharing.

Two frugal options to assess class fragility and seismic safety for low-rise reinforced concrete school buildings in Southern Italy

A study is presented on the seismic fragility of a sample of 15 reinforced-concrete school buildings built between 1960 and 1980s in the province of Foggia, Southern Italy, for which near-perfect information is provided on geometrical and mechanical parameters. In particular, the focus is on the application of two probabilistic methods, employing different compromises in terms of complexity versus accuracy. First, a Eurocode 8 compatible nonlinear static approach is employed, which is augmented via the use of the regression expressions of the SPO2FRAG tool to incorporate record-to-record variability. Second, a nonlinear dynamic approach is used to directly account for record-to-record variability via a constrained multi-stripe analysis. In particular, in view of practitioners’ needs (analysis time, computational efforts, software used), a practical mode for application of the multi-stripe analyses is proposed, based on conducting few stripe analyses, in order to predict the behavior of structures in both the elastic and inelastic ranges of response. Both the static and the dynamic approaches are shown to be viable alternatives, offering fairly matching results at the individual building-level and even closer predictions at the level of ensemble regional fragility curves that incorporate both inter-building and intra-building uncertainties.

Modeling Spatial Charging Demands Related to Electric Vehicles for Power Grid Planning Applications

The electrification of the transport sector together with an increasing share of renewable energies has the potential to reduce CO2 emissions significantly. This transformation requires the rollout of charging infrastructure, which has an impact on power grids. For grid planning and dimensioning purposes, it is crucial to assess this rapidly growing impact. We present an approach using socio-economic data such as income levels together with a model for demographic changes to estimate where electric mobility is likely to be concentrated, especially during the transformation phase. We present a total-cost-of-ownership approach for the ramp-up of electric mobility, considering an increased penetration of renewable energies. With the city of Wiesbaden in Germany as an example for an application area, the possible expansion of vehicle ownership and charging points is modeled on the level of individual buildings. Compared to a simpler approach, the detailed model results in more consistent charging point allocations, higher line/transformer loadings and lower bus voltages for the investigated grids. Predicting future distributions of charging points with such a level of detail in terms of ramp-up and spatial resolution proves potentially beneficial for grid analysis and planning purposes, especially in urban areas, where infrastructure changes are expensive and time-consuming.

Power outage prediction for natural hazards using synthetic power distribution systems

Power outage prediction for natural hazards usually relies on one of two approaches, statistical models or fragility-based methods. Statistical models have provided strong predictive accuracy, but only in an area-aggregated manner. Fragility-based approaches have not offered strong prediction accuracy and have been limited to systems for which system topology or performance models are available. In this paper, we create an algorithm that (1) generates a synthetic power system layout for any U.S. city based only on public data and then (2) simulates power outages at the level of individual buildings under hazard loading using fragility functions. This approach provides much more localized, building-level estimates of the likelihood of losing power due to a natural hazard. We validate our model by comparing the network properties and power outage events based on our approach with data from a real power system in Ohio. We find that our model relies on less input data comparing to statistical learning approaches yet can make accurate predictions, provided accurate fragility curves are available.