Evaluation of simplified building energy models for urban-scale energy analysis of buildings

ABSTRACT Urban building energy analysis has attracted more attention as the population living in cities increases as does the associated energy consumption in urban environments. This paper proposes a systematic bottom-up method to conduct energy analysis and assess energy saving potentials by combining dynamic engineering-based energy models, machine learning models, and global sensitivity analysis within the GIS (Geographic Information System) environment for large-scale urban buildings. This method includes five steps: database construction of building parameters, automation of creating building models at the GIS environment, construction of machine learning models for building energy assessment, sensitivity analysis for choosing energy saving measures, and GIS visual evaluation of energy saving schemes. Campus buildings in Tianjin (China) are used as a case study to demonstrate the application of the method proposed in this research. The results indicate that the method proposed here can provide reliable and fast analysis to evaluate the energy performance of urban buildings and determine effective energy saving measures to reduce energy consumption of urban buildings. Moreover, the GIS-based analysis is very useful to both create energy models of buildings and display energy analysis results for urban buildings.

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Characterization Of Building Foundation In Building Energy Models

Proceedings of Building Simulation 2019: 16th Conference of IBPSA ◽

10.26868/25222708.2019.210925 ◽

2020 ◽

Author(s):

Germán Ramos Ruiz ◽

Vicente Gutierrez González ◽

Eva Lucas Segarra ◽

Germán Campos Gordillo ◽

Carlos Fernandez Bandera

Keyword(s):

Building Energy ◽

Energy Models ◽

Building Foundation

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Impact of Actual Weather Datasets for Calibrating White-Box Building Energy Models Base on Monitored Data

Energies ◽

10.3390/en14041187 ◽

2021 ◽

Vol 14 (4) ◽

pp. 1187

Author(s):

Vicente Gutiérrez González ◽

Germán Ramos Ruiz ◽

Carlos Fernández Bandera

Keyword(s):

Control Experiment ◽

Pearson Correlation ◽

Building Energy ◽

Third Party ◽

Primary Position ◽

Energy Models ◽

Urgent Task ◽

Calibration Study ◽

Study Case ◽

Reduce Energy Consumption

The need to reduce energy consumption in buildings is an urgent task. Increasing the use of calibrated building energy models (BEM) could accelerate this need. The calibration process of these models is a highly under-determined problem that normally yields multiple solutions. Among the uncertainties of calibration, the weather file has a primary position. The objective of this paper is to provide a methodology for selecting the optimal weather file when an on-site weather station with local sensors is available and what is the alternative option when it is not and a mathematically evaluation has to be done with sensors from nearby stations (third-party providers). We provide a quality assessment of models based on the Coefficient of Variation of the Root Mean Square Error (CV(RMSE)) and the Square Pearson Correlation Coefficient (R2). The research was developed on a control experiment conducted by Annex 58 and a previous calibration study. This is based on the results obtained with the study case based on the data provided by their N2 house.

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Probabilistic Load Forecasting Optimization for Building Energy Models via Day Characterization

Sensors ◽

10.3390/s21093299 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3299

Author(s):

Eva Lucas Segarra ◽

Germán Ramos Ruiz ◽

Carlos Fernández Bandera

Keyword(s):

Optimization Technique ◽

Load Forecasting ◽

Weather Forecast ◽

Building Energy ◽

Load Forecast ◽

Energy Saving Potential ◽

Energy Models ◽

Building Monitoring ◽

Probabilistic Load ◽

Grid Operators

Accurate load forecasting in buildings plays an important role for grid operators, demand response aggregators, building energy managers, owners, customers, etc. Probabilistic load forecasting (PLF) becomes essential to understand and manage the building’s energy-saving potential. This research explains a methodology to optimize the results of a PLF using a daily characterization of the load forecast. The load forecast provided by a calibrated white-box model and a real weather forecast was classified and hierarchically selected to perform a kernel density estimation (KDE) using only similar days from the database characterized quantitatively and qualitatively. A real case study is presented to show the methodology using an office building located in Pamplona, Spain. The building monitoring, both inside—thermal sensors—and outside—weather station—is key when implementing this PLF optimization technique. The results showed that thanks to this daily characterization, it is possible to optimize the accuracy of the probabilistic load forecasting, reaching values close to 100% in some cases. In addition, the methodology explained is scalable and can be used in the initial stages of its implementation, improving the values obtained daily as the database increases with the information of each new day.

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