Development and evaluation of a building energy model integrated in the TEB scheme

The use of air-conditioning systems is expected to increase as a consequence of global-scale and urban-scale climate warming. In order to represent future scenarios of urban climate and building energy consumption, the Town Energy Balance (TEB) scheme must be improved. This paper presents a new building energy model (BEM) that has been integrated in the TEB scheme. BEM-TEB makes it possible to represent the energy effects of buildings and building systems on the urban climate and to estimate the building energy consumption at city scale (~10 km) with a resolution of a neighbourhood (~100 m). The physical and geometric definition of buildings in BEM has been intentionally kept as simple as possible, while maintaining the required features of a comprehensive building energy model. The model considers a single thermal zone, where the thermal inertia of building materials associated with multiple levels is represented by a generic thermal mass. The model accounts for heat gains due to transmitted solar radiation, heat conduction through the enclosure, infiltration, ventilation, and internal heat gains. BEM allows for previously unavailable sophistication in the modelling of air-conditioning systems. It accounts for the dependence of the system capacity and efficiency on indoor and outdoor air temperatures and solves the dehumidification of the air passing through the system. Furthermore, BEM includes specific models for passive systems, such as window shadowing devices and natural ventilation. BEM has satisfactorily passed different evaluation processes, including testing its modelling assumptions, verifying that the chosen equations are solved correctly, and validating the model with field data.French National Research Agency (ANR). MUSCADE project (ANR-09-VILL-003)European Commission Framework Program (FP7/2007–2013) (BRIDGE Project grant 211345

Cost-effective Planning of Decarbonized Power-Gas Infrastructure to Meet the Challenges of Heating Electrification

Building heat electrification is central to economy-wide decarbonization efforts and directly affects energy infrastructure planning through increasing electricity demand and reduces the use of gas infrastructure that also serves the power sector. However, the simultaneous effects on both the power and gas systems have yet to be rigorously evaluated. Offering two key contributions, we develop a modeling framework to project end-use demand for electricity and gas in the buildings sector under various electrification pathways and evaluate their impact on co-optimized bulk power-gas infrastructure investments and operations under deep decarbonization scenarios. Applying the framework to study the U.S. New England region in 2050 across 20 weather scenarios, we find high electrification of the residential sector can increase sectoral peak and total electricity demands by up to 62-160% and 47-65% respectively relative to business-as-usual trajectories. Employing demand-side measures like building envelope improvements under high electrification, however, can reduce the magnitude and weather sensitivity of peak load as well as reduce combined power and gas demand by 29-31% relative to the present day. Notably, a combination of high electrification and envelope improvements yields the lowest bulk power-gas system cost outcomes. We also find that inter-annual weather-driven variations in demand result in up to 20% variation in optimal power sector investments, which highlights the importance of capturing weather sensitivity for planning purposes

Demonstration of fault detection and diagnosis methods for air-handling units (ASHRAE 1020-RP)

Results are presented from controlled field tests of two methods for detecting and diagnosing faults in HVAC equipment. The tests were conducted in a unique research building that featured two air-handling units serving matched sets of unoccupied rooms with adjustable internal loads. Tests were also conducted in the same building on a third air handler serving areas used for instruction and by building staff. One of the two fault detection and diagnosis (FDD) methods used first-principles-based models of system components. The data used by this approach were obtained from sensors typically installed for control purposes. The second method was based on semiempirical correlations of submetered electrical power with flow rates or process control signals. Faults were introduced into the air-mixing, filter-coil, and fan sections of each of the three air-handling units. In the matched air-handling units, faults were implemented over three blind test periods (summer, winter, and spring operating conditions). In each test period, the precise timing of the implementation of the fault conditions was unknown to the researchers. The faults were, however, selected from an agreed set of conditions and magnitudes, established for each season. This was necessary to ensure that at least some magnitudes of the faults could be detected by the FDD methods during the limited test period. Six faults were used for a single summer test period involving the third air-handling unit. These fault conditions were completely unknown to the researchers and the test period was truly blind. The two FDD methods were evaluated on the basis of their sensitivity, robustness, the number of sensors required, and ease of implementation. Both methods detected nearly all of the faults in the two matched air-handling units but fewer of the unknown faults in the third air-handling unit. Fault diagnosis was more difficult than detection. The first-principles-based method misdiagnosed several faults. The electrical power correlation method demonstrated greater success in diagnosis, although the limited number of faults addressed in the tests contributed to this success. The first-principles-based models require a larger number of sensors than the electrical power correlation models, although the latter method requires power meters that are not typically installed. The first-principles-based models require training data for each subsystem model to tune the respective parameters so that the model predictions more precisely represent the target system. This is obtained by an open-loop test procedure. The electrical power correlation method uses polynomial models generated from data collected from “normal” system operation, under closed-loop control.Both methods were found to require further work in three principal areas: to reduce the number of parameters to be identified; to assess the impact of less expensive or fewer sensors; and to further automate their implementation. The first-principles-based models also require further work to improve the robustness of predictions

Large-Eddy Simulation of Flow and Pollutant Transport in Urban Street Canyons with Ground Heating

Our study employed large-eddy simulation (LES) based on a one-equation subgrid-scale model to investigate the flow field and pollutant dispersion characteristics inside urban street canyons. Unstable thermal stratification was produced by heating the ground of the street canyon. Using the Boussinesq approximation, thermal buoyancy forces were taken into account in both the Navier–Stokes equations and the transport equation for subgrid-scale turbulent kinetic energy (TKE). The LESs were validated against experimental data obtained in wind-tunnel studies before the model was applied to study the detailed turbulence, temperature, and pollutant dispersion characteristics in the street canyon of aspect ratio 1. The effects of different Richardson numbers (Ri) were investigated. The ground heating significantly enhanced mean flow, turbulence, and pollutant flux inside the street canyon, but weakened the shear at the roof level. The mean flow was observed to be no longer isolated from the free stream and fresh air could be entrained into the street canyon at the roof-level leeward corner. Weighed against higher temperature, the ground heating facilitated pollutant removal from the street canyon.Singapore-MIT Alliance for Research and Technology. Center for Environmental Sensing and Monitorin

Improving air quality in high-density cities by understanding the relationship between air pollutant dispersion and urban morphologies

10.1016/j.buildenv.2013.10.008BUILDING AND ENVIRONMENT71245-25

Fault Detection Based on Motor Start Transients and Shaft Harmonics Measured at the RTU Electrical Service

Non-intrusive load monitoring (NILM) is accomplished by sampling voltage and current at high rates and reducing the resulting start transients or harmonic content to concise load “signatures. ” Changes in these signatures can be used to detect, and possibly diagnose, equipment and component faults associated with roof-top cooling units. NILM-based fault detection and diagnosis (FDD) is important because 1) it complements other FDD schemes that are based on thermo-fluid sensors and analyses and 2) it is minimally intrusive (one measuring point in the relatively protected confines of the control panel) and therefore inherently reliable. This paper describes changes in the power signatures of fans and compressors that were found, experimentally and theoretically, to be useful for fault detection

Urban heat island mitigation in Singapore: Evaluation using WRF/multilayer urban canopy model and local climate zones

© 2020 Elsevier B.V. Mitigation and adaption measures must be designed strategically by urban planners, designers, and decision-makers to reduce urban heat island (UHI) related risks. We employed the Weather Research and Forecasting (WRF) model to assess UHI mitigation scenarios for the tropical city of Singapore during April 2016, including two heat wave periods. The local climate zones for Singapore were used as the land use/land cover data to account for the intra-urban variability. The simulations show that the canopy layer UHI intensity in Singapore can reach up to 5 °C in compact areas during nighttime. The results reveal that city-scale deployment of cool roofs can provide an overall reduction of 1.3 °C in the near-surface daytime air temperature in large low-rise areas. Increasing the thermostat set temperature to 25 °C from 21 °C in city-wide buildings can potentially reduce the air temperature due to less (~20%) waste heat discharge from air-conditioning units. A densification scenario considering an increase from approximately 7 841 people/km2 (2016) to 9040-9,600 people/km2 (2030) under the current climate leads to air temperature increase of 1.4 °C, which demonstrates the importance of limiting the densification of less compact areas in maintaining thermal comfort in the future

Anthropogenic Heat of Power Generation in Singapore: analyzing today and a future electromobility scenario

This report studies the anthropogenic heat emissions of Singapore’s power generation sector and evaluates the potential future emissions with electromobility across the island. We thus developed a power plant dispatch model to downscale the total heat released by the power sector in 2016. Taking electricity demand and fuel prices as inputs, the model was based on an energy-only model of the National Electricity Market of Singapore. Generation companies were assumed to bid at marginal cost and discount the value of cogeneration heat. This led to a higher correlation of electricity prices and demand than in reality, and sensitivity to fuel prices. The model is capable of calculating the dispatch, fuel consumption, cogeneration heat and waste heat streams of each plant. These heat profiles would then serve as inputs to a WRF mesoscale model of Singapore. The model was calibrated with the monthly fuel mix and annual fuel consumption in 2016 via hyperparameter optimization. An RMSE of 4.67 ktoe was achieved in the electricity produced per month and per fuel, and the total released heat was within 1.88% of the energy statistics. Simulation of the baseline electricity demand showed that CCGT PNG plants emit over half of the waste heat (1796 ktoe of 3282 ktoe), with the Senoko power plant releasing half of this. Cogeneration CCGT plants released about 882 ktoe of waste heat, while producing as much as 1813 ktoe of process heat. As much as 47% of the total waste heat is released into the air as sensible heat, and 27% as latent heat, with the rest released into the sea. Based on data from a previous study on the anthropogenic heat emissions in the transportation sector, we simulated a scenario wherein the road transportation in Singapore was fully electrified. This scenario could have an additional waste heat of 248 ktoe, and an additional electricity demand of 369 ktoe. This additional demand represents a reduction of vehicle heat on the roads by a factor of six, and more heat is emitted at far-away and efficient cogeneration plants. Overall, the estimated reduction in total anthropogenic heat is 1473 ktoe, or about 7% less than in 2016