Building energy model calibration using automated optimization-based algorithm

Asadi, Somayeh; Mostavi, Ehsan; Boussaa, Djamel; Indraganti, Madhavi

doi:10.1016/j.enbuild.2019.06.001

Cited by 22 publications

(4 citation statements)

References 20 publications

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“…In order to select the optimum element among a series of the available candidates, an optimization method is used. Since this design deals with more than one objective, making the optimal decision needs a compromised trade-off among conflicting objectives (Asadi et al, 2019). In this study, we used nondominated sorting genetic algorithm II (NSGA-II), which is one of the most common methods to solve a multiobjective problem (Yusoff et al, 2011).…”

Section: Optimization Of Building Energy Performancementioning

confidence: 99%

Investigating the probability of designing net-zero energy buildings with consideration of electric vehicles and renewable energy

Nasab

Nasrabadi

Asadi

et al. 2021

ECAM

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PurposeDue to technological improvement and development of the vehicle-to-home (V2H) concept, electric vehicle (EV) can be considered as an active component of net-zero energy buildings (NZEBs). However, to achieve more dependable results, proper energy analysis is needed to take into consideration the stochastic behavior of renewable energy, energy consumption in the building and vehicle use pattern. This study aims to stochastically model a building integrating photovoltaic panels as a microgeneration technology and EVs to meet NZEB requirements.Design/methodology/approachFirst, a multiobjective nondominated sorting genetic algorithm (NSGA-II) was developed to optimize the building energy performance considering panels installed on the façade. Next, a dynamic solution is implemented in MATLAB to stochastically model electricity generation using solar panels as well as building and EV energy consumption. Besides, the Monte Carlo simulation method is used for quantifying the uncertainty of NZEB performance. To investigate the impact of weather on both energy consumption and generation, the model is tested in five different climatic zones in Iran.FindingsThe results show that the stochastic simulation provides building designers with a variety of convenient options to select the best design based on level of confidence and desired budget. Furthermore, economic evaluation signifies that investing in all studied cities is profitable.Originality/valueConsidering the uncertainty in building energy demand and PV power generation as well as EV mobility and the charging–discharging power profile for evaluating building energy performance is the main contribution of this study.

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Section: Optimization Of Building Energy Performancementioning

confidence: 99%

Investigating the probability of designing net-zero energy buildings with consideration of electric vehicles and renewable energy

Nasab

Nasrabadi

Asadi

et al. 2021

ECAM

Self Cite

View full text Add to dashboard Cite

show abstract

“…Chong and Chao developed a continuous Bayesian calibration method and tested the method on an actual 10-story office building in Pennsylvania (Chong and Chao 2020). Asadi et al developed an optimization-based framework to calibrate the whole building energy model, and tested the method with a real office building located in Doha, Qatar (Asadi et al 2019). Yin, Kiliccote, and Piette proposed an automated model calibration procedure that links the model to a real-time data monitoring system that allows the model to be updated any time, and applied the approach to a real campus building for testing (Yin, Kiliccote, and Piette 2016).…”

Section: Introductionmentioning

confidence: 99%

Application and evaluation of a pattern-based building energy model calibration method using public building datasets

et al. 2022

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Building performance simulation has been adopted to support decision making in the building life cycle. An essential issue is to ensure a building energy simulation model can capture the reality and complexity of buildings and their systems in both the static characteristics and dynamic operations. Building energy model calibration is a technique that takes various types of measured performance data (e.g., energy use) and tunes key model parameters to match the simulated results with the actual measurements. This study performed an application and evaluation of an automated pattern-based calibration method on commercial building models that were generated based on characteristics of real buildings. A public building dataset that includes high-level building attributes (e.g., building type, vintage, total floor area, number of stories, zip code) of 111 buildings in San Francisco, California, was used to generate building models in EnergyPlus. Monthly level energy use calibrations were then conducted by comparing building model results against the actual buildings' monthly electricity and natural gas consumption. The results showed 57 out of 111 buildings were successfully calibrated against actual buildings, while the remaining buildings showed opportunities for future calibration improvements. Enhancements to the pattern-based model calibration method are identified to expand its use for: (1) central heating, ventilation and air conditioning (HVAC) systems with chillers, (2) space heating and hot water heating with electricity sources, (3) mixed-use building types, and (4) partially occupied buildings.

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“…This research would require direct measurements. In a study performed by Asadi et al [42], an automated optimization with a harmony search algorithm was employed to calibrate the energy simulation model of an office building. It allowed them to reach a mean bias error of less than 5%.…”

Section: Introductionmentioning

confidence: 99%

Development and Analysis of a Dynamic Energy Model of an Office Using a Building Management System (BMS) and Actual Measurement Data

et al. 2021

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Calibration of the energy model of a building is one of the essential tasks required to determine the efficiency of building management systems, and both their own and other systems’ improvement potential. In order to make the building energy model as accurate as possible, it is necessary to collect comprehensive data on its operation and sometimes to assess the missing information. This paper represents the process of developing an energy model for an administrative building and its calibration procedure, using detailed long-term measurement and building management system (BMS) data. Indoor air temperature, CO₂ concentration, and relative humidity were experimentally measured and evaluated separately. Such dual application of data reduces the inaccuracy of the assumptions made and assesses the model’s accuracy. The DesignBuilder software developed the building model. During the development of the model, it was observed that the actual energy consumption needs to be assessed, as the assumptions made during the design about the operation and management of HVAC systems often do not coincide with the actual situation. After integrating BMS information on HVAC management into the building model, the resulting discrepancy between the model results and the actual heat consumption was 6.5%. Such a model can be further used to optimize management decisions and assess energy savings potential.

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Building energy model calibration using automated optimization-based algorithm

Cited by 22 publications

References 20 publications

Investigating the probability of designing net-zero energy buildings with consideration of electric vehicles and renewable energy

Investigating the probability of designing net-zero energy buildings with consideration of electric vehicles and renewable energy

Application and evaluation of a pattern-based building energy model calibration method using public building datasets

Development and Analysis of a Dynamic Energy Model of an Office Using a Building Management System (BMS) and Actual Measurement Data

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