Decision making of HVAC system using Bayesian Markov chain Monte Carlo method

Kim, Young‐Jin; Ahn, Ki-Uhn; Park, Cheolsoo

doi:10.1016/j.enbuild.2013.12.039

Cited by 46 publications

(17 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Numerical indicators are descriptive statistical measures including mean, median, standard deviation, percentile, interquartile range, coefficient of variation, and quantile. Typical graphical methods include histogram [3,35,107,116,118] , density plot [13,59,107], cumulative distribution functions [56,116,122,131], and box plots [16,113]. Presently uncertainty analysis results is more complex if analysis results are multi-dimensional as a function of time or space.…”

Section: Present Uncertainty Resultsmentioning

confidence: 99%

“…Latin hyper-cube sampling (LHS) is the most widely used sampling method in the field of building energy analysis as it can produce converged results with a considerably reduced number of samples [3,51,52,58,59,107,111,116,123]. LHS is a stratified sampling method that divides the range of every input variable into N segments (the specific sample size) with equal probability.…”

Section: Sampling Methodsmentioning

confidence: 99%

“…Monte Carlo-based simulation is the most widely used uncertainty propagation method in the area of building energy assessment [107][108][109]. This is because this method is very intuitive and easy to implement compared to other uncertainty propagation approaches (such as full factorial numerical integration, stochastic Galerkin, and discrete projection).…”

Section: Monte Carlo Sampling-based Simulationmentioning

confidence: 99%

See 2 more Smart Citations

A review of uncertainty analysis in building energy assessment

Tian

Heo

Wilde

et al. 2018

Renewable and Sustainable Energy Reviews

334

122

View full text Add to dashboard Cite

Uncertainty analysis in building energy assessment has become an active research field because a number of factors influencing energy use in buildings are inherently uncertain. This paper provides a systematic review on the latest research progress of uncertainty analysis in building energy assessment from four perspectives: uncertainty data sources, forward and inverse methods, application of uncertainty analysis, and available software. First, this paper describes the data sources of uncertainty in building performance analysis to provide a firm foundation for specifying variations of uncertainty factors affecting building energy. The next two sections focus on the forward and inverse methods. Forward uncertainty analysis propagates input uncertainty through building energy models to obtain variations of energy use, whereas inverse uncertainty analysis infers unknown input factors through building energy models based on energy data and prior information. For forward analysis, three types of approaches (Monte Carlo, non-sampling, and non-probabilistic) are discussed to provide sufficient choices of uncertainty methods depending on the purpose and specific application of a building project. For inverse analysis, recent research has concentrated more on Bayesian computation because Bayesian inverse methods can make full use of prior information on unknown variables. Fourth, several applications of uncertainty analysis in building energy assessment are discussed, including building stock analysis, HVAC system sizing, variations of sensitivity indicators, and optimization under uncertainty. Moreover, the software for uncertainty analysis is described to provide flexible computational environments for implementing uncertainty methods described in this review. This paper concludes with the trends and recommendations for further research to provide more convenient and robust uncertainty analysis of building energy. Uncertainty analysis has been ready to become the mainstream approach in building energy assessment although a number of issues still need to be addressed.

show abstract

Section: Present Uncertainty Resultsmentioning

confidence: 99%

Section: Sampling Methodsmentioning

confidence: 99%

Section: Monte Carlo Sampling-based Simulationmentioning

confidence: 99%

See 1 more Smart Citation

A review of uncertainty analysis in building energy assessment

Tian

Heo

Wilde

et al. 2018

Renewable and Sustainable Energy Reviews

334

122

View full text Add to dashboard Cite

show abstract

“…If uncertainties are taken into account, e.g. heating and cooling setpoint temperatures, infiltration, and occupant behavior with regard to lights and blinds, an optimal design will be determined based on minimizing the degree of risk [16,30].…”

Section: Discussionmentioning

confidence: 99%

Heuristic vs. Meta-Heuristic Optimal Energy Design for an Office Building

Suh

Park

2017

Sustainability

Self Cite

View full text Add to dashboard Cite

Abstract:In this paper, an application of heuristic vs. meta-heuristic approaches to the design of an office building is presented. The building was first optimized by a heuristic approach based on the designers' expertise, prior experiences and intuitions with the use of a whole building simulation tool, EnergyPlus. Then, a meta-heuristic approach was completed in MATLAB platform where EnergyPlus and Genetic Algorithm (GA) were coupled. M-script files were developed to automate execution of EnergyPlus simulation runs (reading output files and overwriting input files of EnergyPlus) in integration to GA. Based on a comparison between the heuristic and the meta-heuristic approach, it is shown that GA performs much better in finding a global optimum even under a constrained search space than the heuristic approach. The heuristic approach has advantages, such as reflection of a design context in decision-making and fast communication between stakeholders.

show abstract

“…Then, the internal thermal capacity was added to the thermal capacity of indoor air when calculating the heat balance equation in EnergyPlus [28]. All parameters in Table 1 were assumed to follow a normal distribution, which is widely accepted in many Monte Carlo studies [22,[29][30][31]. selected for explanation.…”

Section: Monte Carlo Sampling For Generation Of Peer Buildingsmentioning

confidence: 99%

Objective Building Energy Performance Benchmarking Using Data Envelopment Analysis and Monte Carlo Sampling

Yoon

Park

2017

Sustainability

Self Cite

View full text Add to dashboard Cite

An objective measure of building energy performance is crucial for performance assessment and rational decision making on energy retrofits and policies of existing buildings. One of the most popular measures of building energy performance benchmarking is Energy Use Intensity (EUI, kwh/m 2 ). While EUI is simple to understand, it only represents the amount of consumed energy per unit floor area rather than the real performance of a building. In other words, it cannot take into account building services such as operation hours, comfortable environment, etc. EUI is often misinterpreted by assuming that a lower EUI for a building implies better energy performance, which may not actually be the case if many of the building services are not considered. In order to overcome this limitation, this paper presents Data Envelopment Analysis (DEA) coupled with Monte Carlo sampling. DEA is a data-driven and non-parametric performance measurement method. DEA can quantify the performance of a given building given multiple inputs and multiple outputs. In this study, two existing office buildings were selected. For energy performance benchmarking, 1000 virtual peer buildings were generated from a Monte Carlo sampling and then simulated using EnergyPlus. Based on a comparison between DEA-based and EUI-based benchmarking, it is shown that DEA is more performance-oriented, objective, and rational since DEA can take into account input (energy used to provide the services used in a building) and output (level of services provided by a building). It is shown that DEA can be an objective building energy benchmarking method, and can be used to identify low energy performance buildings.

show abstract

Decision making of HVAC system using Bayesian Markov chain Monte Carlo method

Cited by 46 publications

References 17 publications

A review of uncertainty analysis in building energy assessment

A review of uncertainty analysis in building energy assessment

Heuristic vs. Meta-Heuristic Optimal Energy Design for an Office Building

Objective Building Energy Performance Benchmarking Using Data Envelopment Analysis and Monte Carlo Sampling

Contact Info

Product

Resources

About