A rapid calibration procedure and case study for simplified simulation models of commonly used HVAC systems

Liu, Guopeng; Liu, Mingsheng

doi:10.1016/j.buildenv.2010.08.002

Cited by 54 publications

(22 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Graphical plots of calibration signature and characteristic signature have also been proposed to guide the parameter tuning process (Liu and Liu, 2011). Due to its iterative nature, the use of version control to keep track of model changes and the reasons for the change has been identified as an important process in order to improve the reproducibility of manual calibration methods (Raftery et al, 2011).…”

Section: Manual Calibration Approachesmentioning

confidence: 99%

Bayesian calibration of building energy models with large datasets

Chong

Lam

Pozzi

et al. 2017

Energy and Buildings

View full text Add to dashboard Cite

Section: Manual Calibration Approachesmentioning

confidence: 99%

Bayesian calibration of building energy models with large datasets

Chong

Lam

Pozzi

et al. 2017

Energy and Buildings

View full text Add to dashboard Cite

“…Traditionally, the calibration process relied on expertise and potentially timeconsuming trial and error [4]. Today, a number of researchers are working on improved workflows to improve this process [7,18]. Logic suggests that such technical innovations coupled with the growing supply of energy models create a context that is ever more conducive to the use of energy simulation throughout the life of a building.…”

Section: Introductionmentioning

confidence: 99%

Non-technical barriers to energy model sharing and reuse

Samuelson

Lantz²,

Reinhart

2012

Building and Environment

View full text Add to dashboard Cite

An energy model generated during the design phase of a building could -in principal -be converted into a calibrated energy model and used to improve the building's operational performance. However, this rarely happens in practice. Through a survey of 306 building professionals, this research investigates whether this model reuse is technically feasible, based on today's design-phase models, and what non-technical barriers might stand in the way of its implementation. An important finding is that 75% of the engineers/energy modelers surveyed believed that their models could be used by a third party in building commissioning and operation. At the same time, many modelers voiced various reservations that might prevent them from sharing their energy models with the owner or design team.These reservations varied from a desire to protect their intellectual property, to liability concerns, to the fear of incurring additional unpaid work. In response to these findings, this paper provides suggestions for overcoming these non-technical challenges and includes references for contract precedents.

show abstract

“…However, as using these simulation programs are required to type various inputs such as building archetypes, occupancy types and HVAC (Heating, Ventilation, and Air-conditioning) information, this work is quite complex and needs to have expertise of energy [28]. To overcome these problems, many studies [10][11][12] try to simplify this modeling (e.g., researching representative of complex input). However, it is still difficult for general users to use this method.…”

Section: Difficulties Of User's Access In Using Existing Energy Consumentioning

confidence: 99%

“…There are a variety of studies in developing the model of energy consumption [6][7][8][9][10][11][12][13][14], which is based on energy simulation or data-driven model, and metamodeling. However, it is difficult for general user to use these models due to requirement of various sensing data and expertise.…”

Section: Introductionmentioning

confidence: 99%

Development of Easily Accessible Electricity Consumption Model Using Open Data and GA-SVR

2018

View full text Add to dashboard Cite

Abstract:In many countries, DR (Demand Response) has been developed for which customers are motivated to save electricity by themselves during peak time to prevent grand-scale blackouts. One of the common methods in DR, is CPP (Critical Peak Pricing). Predicting energy consumption is recognized as one of the tool for dealing with CPP. There are a variety of studies in developing the model of energy consumption, which is based on energy simulation, data-driven model or metamodelling. However, it is difficult for general users to use these models due to requirement of various sensing data and expertise. And it also takes long time to simulate the models. These limitations can be an obstacle for achieving CPP's purpose that encourages general users to manage their energy usage by themselves. As an alternative, this research suggests to use open data and GA (Genetic Algorithm)-SVR (Support Vector Regression). The model is applied to a hospital in Korea and 34,636 data sets (1 year) are collected while 31,756 (11 months) sets are used for training and 2880 sets (1 month) are used for validation. As a result, the performance of proposed model is 14.17% in CV (RMSE), which satisfies the Korea Energy Agency's and ASHRAE (American Society of Heating, Refrigerating and Air-Conditioning Engineers) error allowance range of ±30%, and ±20% respectively.

show abstract

A rapid calibration procedure and case study for simplified simulation models of commonly used HVAC systems

Cited by 54 publications

References 4 publications

Bayesian calibration of building energy models with large datasets

Bayesian calibration of building energy models with large datasets

Non-technical barriers to energy model sharing and reuse

Development of Easily Accessible Electricity Consumption Model Using Open Data and GA-SVR

Contact Info

Product

Resources

About