scholarly journals Prototype Courthouse Building Energy Model: Building and System Characteristics

2018 ◽  
Author(s):  
Mini Malhotra ◽  
Joshua Ryan New ◽  
Piljae Im
Keyword(s):  
Model Building ◽  
Building Energy ◽  
Energy Model ◽  
System Characteristics

scholarly journals Multi-step building energy model calibration process based on measured data

2021 ◽  
Vol 252 ◽  
pp. 111380
Author(s):  
José Eduardo Pachano ◽  
Carlos Fernández Bandera
Keyword(s):  
Model Calibration ◽  
Building Energy ◽  
Measured Data ◽  
Energy Model ◽  
Calibration Process

Calibrating the building energy model with the short term monitored data

2020 ◽  
Vol 224 ◽  
pp. 110207
Author(s):  
Faith Tüysüz ◽  
Hatice Sözer
Keyword(s):  
Building Energy ◽  
Energy Model ◽  
Short Term

A modified building energy model coupled with urban parameterization for estimating anthropogenic heat in urban areas

2019 ◽  
Vol 202 ◽  
pp. 109377 ◽  
Author(s):  
Yu-Chen Wang ◽  
Zheng-Fu Bian ◽  
Kai Qin ◽  
Yu Zhang ◽  
Shao-Gang Lei
Keyword(s):  
Urban Areas ◽  
Building Energy ◽  
Energy Model ◽  
Anthropogenic Heat

scholarly journals Integration of a Building Energy Model in an Urban Climate Model and its Application

2020 ◽  
Author(s):  
Luxi Jin ◽  
Sebastian Schubert ◽  
Daniel Fenner ◽  
Fred Meier ◽  
Christoph Schneider
Keyword(s):  
Temporal Variation ◽  
Urban Heat Island ◽  
Air Temperature ◽  
Indoor Air ◽  
Climate Model ◽  
Building Energy ◽  
Energy Model ◽  
Heat Island ◽  
Energy Fluxes ◽  
Urban Heat

Abstract We report the ability of an urban canopy model, coupled with a regional climate model, to simulate energy fluxes, the intra-urban variability of air temperature, urban-heat-island characteristics, indoor temperature variation, as well as anthropogenic heat emissions, in Berlin, Germany. A building energy model is implemented into the Double Canyon Effect Parametrization, which is coupled with the mesoscale climate model COSMO-CLM (COnsortium for Small-scale MOdelling in CLimate Mode) and takes into account heat generation within buildings and calculates the heat transfer between buildings and the urban atmosphere. The enhanced coupled urban model is applied in two simulations of 24-day duration for a winter and a summer period in 2018 in Berlin, using downscaled reanalysis data to a final grid spacing of 1 km. Model results are evaluated with observations of radiative and turbulent energy fluxes, 2-m air temperature, and indoor air temperature. The evaluation indicates that the improved model reproduces the diurnal characteristics of the observed turbulent heat fluxes, and considerably improves the simulated 2-m air temperature and urban heat island in winter, compared with the simulation without the building energy model. Our set-up also estimates the spatio–temporal variation of wintertime energy consumption due to heating with canyon geometry. The potential to save energy due to the urban heat island only becomes evident when comparing a suburban site with an urban site after applying the same grid-cell values for building and street widths. In summer, the model realistically reproduces the indoor air temperature and its temporal variation.

scholarly journals Learning-Based Predictive Building Energy Model Using Weather Forecasts for Optimal Control of Domestic Energy Systems

2018 ◽  
Vol 11 (1) ◽  
pp. 147 ◽  
Author(s):  
Byung-Ki Jeon ◽  
Eui-Jong Kim ◽  
Younggy Shin ◽  
Kyoung-Ho Lee
Keyword(s):  
Predictive Control ◽  
Prediction Models ◽  
Weather Forecasting ◽  
Reference Model ◽  
Weather Prediction ◽  
Building Energy ◽  
Energy Model ◽  
Weather Data ◽  
Rc Building ◽  
Mean Square Errors

The aim of this study is to develop a model that can accurately calculate building loads and demand for predictive control. Thus, the building energy model needs to be combined with weather prediction models operated by a model predictive controller to forecast indoor temperatures for specified rates of supplied energy. In this study, a resistance–capacitance (RC) building model is proposed where the parameters of the models are determined by learning. Particle swarm optimization is used as a learning scheme to search for the optimal parameters. Weather prediction models are proposed that use a limited amount of forecasting information fed by local meteorological centers. Assuming that weather forecasting was perfect, hourly outdoor temperatures were accurately predicted; meanwhile, differences were observed in the predicted solar irradiances values. In investigations to verify the proposed method, a seven-resistance, five-capacitance (7R5C) model was tested against a reference model in EnergyPlus using the predicted weather data. The root-mean-square errors of the 7R5C model in the prediction of indoor temperatures on all the specified days were within 0.5 °C when learning was performed using reference data obtained from the previous five days and weather prediction was included. This level of deviation in predictive control is acceptable considering the magnitudes of the loads and demand of the tested building.

Issues and Prospects of BIM-to-BEM (Building Energy Model)

2014 ◽  
Vol 30 (4) ◽  
pp. 199-206
Author(s):  
Seo-Young Lee ◽  
Cheol-Soo Park ◽  
In-Han Kim
Keyword(s):  
Building Energy ◽  
Energy Model

scholarly journals A methodology for creating building energy model occupancy schedules using personal location metadata

2017 ◽  
Vol 150 ◽  
pp. 211-223 ◽  
Author(s):  
James Parker ◽  
Adam Hardy ◽  
David Glew ◽  
Christopher Gorse
Keyword(s):  
Building Energy ◽  
Energy Model
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