scholarly journals Integrating climate change into meteorological weather data for building energy simulation

2019 ◽  
Vol 183 ◽  
pp. 749-760 ◽  
Author(s):  
Sleiman Farah ◽  
David Whaley ◽  
Wasim Saman ◽  
John Boland
Keyword(s):  
Climate Change ◽  
Building Energy ◽  
Energy Simulation ◽  
Weather Data ◽  
Building Energy Simulation

Building Energy Simulation with On-Site Weather Station

2016 ◽  
Vol 859 ◽  
pp. 88-92 ◽  
Author(s):  
Radu Manescu ◽  
Ioan Valentin Sita ◽  
Petru Dobra
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Building Energy ◽  
Energy Simulation ◽  
Weather Data ◽  
Building Energy Simulation ◽  
Existing Buildings ◽  
Existing Building ◽  
Simulation Tools ◽  
Heating And Cooling

Energy consumption awareness and reducing consumption are popular topics. Building energy consumption counts for almost a third of the global energy consumption and most of that is used for building heating and cooling. Building energy simulation tools are currently gaining attention and are used for optimizing the design for new and existing buildings. For O&M phase in existing buildings, the multiannual average weather data used in the simulation tools is not suitable for evaluating the performance of the building. In this study an existing building was modeled in EnergyPlus. Real on-site weather data was used for the dynamic simulation for the heating energy demand with the aim of comparing the measured energy consumption with the simulated one. The aim is to develop an early fault detection tool for building management.

Procedures for Calibrating Hourly Simulation Models to Measured Building Energy and Environmental Data

1998 ◽  
Vol 120 (3) ◽  
pp. 193-204 ◽  
Author(s):  
J. S. Haberl ◽  
T. E. Bou-Saada
Keyword(s):  
Goodness Of Fit ◽  
Building Energy ◽  
Building Envelope ◽  
Energy Simulation ◽  
Environmental Data ◽  
Simulation Program ◽  
Weather Data ◽  
Mean Bias Error ◽  
Building Energy Simulation ◽  
Calibration Methods

This paper discusses procedures for creating calibrated building energy simulation programs. It begins with reviews of the calibration techniques that have been reported in the previous literature and presents new hourly calibration methods including a temperature bin analysis to improve hourly x−y scatter plots, a 24-hour weather-daytype bin analysis to allow for the evaluation of hourly temperature and schedule dependent comparisons, and a 52-week bin analysis to facilitate the evaluation of long-term trends. In addition, architectural rendering is reviewed as a means of verifying the dimensions of the building envelope and external shading placement as seen by the simulation program. Several statistical methods are also presented that provide goodness-of-fit indicators, including percent difference calculations, mean bias error (MBE), and the coefficient of variation of the root mean squared error (CV(RMSE)). The procedures are applied to a case study building located in Washington, D. C. where nine months of hourly whole-building electricity data and sitespecific weather data were measured and used with the DOE-2.1D building energy simulation program to test the new techniques. Simulations that used the new calibration procedures were able to produce an hourly MBE of –0.7% and a CV(RMSE) of 23.1% which compare favorably with the most accurate hourly neural network models (Kreider and Haberl, 1994a, b).

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