A critical review of observation studies, modeling, and simulation of adaptive occupant behaviors in offices
TL;DR: In this article, a review of the literature on occupant behaviors in building energy use is presented, and the authors discuss the limitations associated with their application, and develop recommendations for future work.
About: This article is published in Building and Environment.The article was published on 2013-12-01. It has received 220 citations till now. The article focuses on the topics: Poison control.
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TL;DR: In this paper, the authors introduced the most recent advances and current obstacles in modeling occupant behavior and quantifying its impact on building energy use, including advancements in data collection techniques, analytical and modeling methods, and simulation applications which provide insights into behavior energy savings potential and impact.
401 citations
TL;DR: The International Energy Agency (IEA) Energy in Buildings and Community (EBC) Programme Annex 66 has established a scientific methodological framework for occupant behavior research, including data collection, behavior model representation, modeling and evaluation approaches, and the integration of behavior modeling tools with building performance simulation programs as mentioned in this paper.
338 citations
TL;DR: In this article, a review of the development of energy-efficient and healthy ventilation in buildings is presented, where the influence of occupants' behaviour on the energy use and the correlation between ventilation and the occupants' health and productivity are also considered.
Abstract: Energy demand has been increasing worldwide and the building sector represents a large percentage of global energy consumption. Therefore, promoting energy efficiency in buildings is essential. Among all building services, Heating, Ventilation and Air Conditioning (HVAC) systems are significantly responsible for building energy use. In HVAC, ventilation is the key issue for providing suitable Indoor Air Quality (IAQ), while it is also responsible for energy consumption in buildings. Thus, improving ventilation systems plays an important role not only in fostering energy efficiency in buildings, but also in providing better indoor climate for the occupants and decreasing the possibility of health issues in consequence. In the last decades, many energy-efficient ventilation methods are developed by researchers to mitigate energy consumption in buildings. This paper reviews scientific research and reports, as well as building regulations and standards, which evaluated, investigated and reported the development of energy-efficient methods for ventilation in buildings. Besides energy-efficient methods such as natural and hybrid ventilation strategies, occupants’ behaviours regarding ventilation, can also affect the energy demand in buildings. Therefore, the influence of occupants’ behaviour on the energy use and the correlation between ventilation and the occupants’ health and productivity were also considered. The review showed that ventilation is interrelated with many factors such as indoor and outdoor conditions, building characteristics, building application as well as users’ behaviour. Thus, it is concluded that many factors must be taken into account for designing energy-efficient and healthy ventilation systems. Moreover, it should be mentioned that utilizing hybrid ventilation in buildings integrated with suitable control strategies, to adjust between mechanical and natural ventilation, leads to considerable energy savings while an appropriate IAQ is maintained.
294 citations
TL;DR: The data sources of uncertainty in building performance analysis are described to provide a firm foundation for specifying variations of uncertainty factors affecting building energy, and several applications of uncertainty analysis in building energy assessment are discussed.
Abstract: 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.
266 citations
Cites background from "A critical review of observation st..."
...Previous researchers have studied various aspects of occupant behaviour in building, including occupant monitoring, ontology of occupant behaviour, behaviour model development & evaluation, and model implementation [83, 91, 92]....
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...[91] Gunay HB, O'Brien W, Beausoleil-Morrison I....
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TL;DR: The attempt to rethink occupant behavior and its role in building energy performance by means of review identifies four existing research gaps, namely the needs for understanding occupant behavior in a systematic framework, for stronger empirical evidence beyond individual buildings and at a larger city scale, and for linking occupant behavior to socio-economic and policy variables.
264 citations
References
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01 Jan 1998
TL;DR: In this paper, the authors examined the semantics of thermal comfort in terms of thermal sensation, acceptability, and preference, as a function of both indoor and outdoor temperature, as predicted by the adaptive hypothesis.
Abstract: The adaptive hypothesis predicts that contextual factors and past thermal history modify building occupants' thermal expectations and preferences. One of the predictions of the adaptive hypothesis is that people in warm climate zones prefer warmer indoor temperatures than people living in cold climate zones. This is contrary to the static assumptions underlying the current ASHRAE comfort standard 55-92. To examine the adaptive hypothesis and its implications for Standard 55-92, the ASHRAE RP-884 project assembled a quality-controlled database from thermal comfort field experiments worldwide (circa 21,000 observations from 160 buildings). Our statistical analysis examined the semantics of thermal comfort in terms of thermal sensation, acceptability, and preference, as a function of both indoor and outdoor temperature. Optimum indoor temperatures tracked both prevailing indoor and outdoor temperatures, as predicted by the adaptive hypothesis. The static predicted means vote (PMV) model was shown to be partially adaptive by accounting for behavioral adjustments, and fully explained adaptation occurring in HVAC buildings. Occupants in naturally ventilated buildings were tolerant of a significantly wider range of temperatures, explained by a combination of both behavioral adjustment and psychological adaptation. These results formed the basis of a proposal for a variable indoor temperature standard.
1,747 citations
Book•
01 Jan 2005
TL;DR: In this paper, the authors describe the learning process of statistical methods for the generation of knowledge and their application to the problem of statistical analysis of real-world data, and present several examples.
Abstract: Preface to the Second Edition. Chapter 1. Catalizing the Generation of Knowledge. 1.1. The Learning Process. 1.2. Important Considerations. 1.3. The Experimenter's Problem and Statistical Methods. 1.4. A Typical Investigation. 1.5. How to Use Statistical Techniques. References and Further Reading. Chapter 2. Basics: Probability, Parameters and Statistics. 2.1. Experimental Error. 2.2. Distributions. 2.3. Statistics and Parameters. 2.4. Measures of Location and Spread. 2.5. The Normal Distribution. 2.6. Normal Probability Plots. 2.7. Randomness and Random Variables. 2.8. Covariance and Correlation as Measures of Linear Dependence. 2.9. Student's t Distribution. 2.10. Estimates of Parameters. 2.11. Random Sampling from a Normal Population. 2.12. The Chi-Square and F Distributions. 2.13. The Binomial Distribution. 2.14. The Poisson Distribution. Appendix 2A. Mean and Variance of Linear Combinations of Observations. References and Further Reading. Chapter 3. Comparing Two Entities: Relevant Reference Distributions, Tests and Confidence Intervals. 3.1. Relevant Reference Sets and Distributions. 3.2. Randomized Paired Comparison Design: Boys' Shoes Example. 3.3. Blocking and Randomization. 3.4. Reprise: Comparison, Replication, Randomization, and Blocking in Simple Experiments. 3.5. More on Significance Tests. 3.6. Inferences About Data that are Discrete: Binomial Distribution. 3.7. Inferences about Frequencies (Counts Per Unit): The Poisson Distribution. 3.8. Contingency Tables and Tests of Association. Appendix 3A. Comparison of the Robustness of Tests to Compare Two Entities. Appendix 3B. Calculation of reference distribution from past data. References and Further Reading. Chapter 4. Comparing a Number of Entities: Randomized Blocks and Latin Squares. 4.1. Comparing k Treatments in a Fully Randomized Design. 4.2. Randomized Block Designs. 4.3. A Preliminary Note on Split-Plot Experiments and their Relationship to Randomized Blocks. 4.4. More than one blocking component: Latin Squares. 4.5. Balanced Incomplete Block Designs. Appendix 4A. The Rationale for the Graphical ANOVA. Appendix 4B. Some Useful Latin Square, Graeco-Latin Square, and Hyper-Graeco-Latin Square Designs. References and Further Reading. Chapter 5. Factorial Designs at Two Levels: Advantages of Experimental Design. 5.1. Introduction. 5.2. Example 1: The Effects of Three Factors (Variables) on Clarity of Film. 5.3. Example 2: The Effects of Three Factors on Three Physical Properties of a Polymer Solution. 5.4. A 23 Factorial Design: Pilot Plant Investigation. 5.5. Calculation of Main Effects. 5.6. Interaction Effects. 5.7. Genuine Replicate Runs. 5.8. Interpretation of Results. 5.9. The Table of Contrasts. 5.10. Misuse of the ANOVA for 2k Factorial Experiments. 5.11. Eyeing the Data. 5.12. Dealing with More Than One Response: A Pet Food Experiment. 5.13. A 24 Factorial Design: Process Development Study. 5.14. Analysis Using Normal and Lenth Plots. 5.15. Other Models for Factorial Data. 5.16. Blocking the 2k Factorial Designs. 5.17. Learning by Doing. 5.18. Summary. Appendix 5A. Blocking Larger Factorial Designs. Appendix 5B. Partial Confounding. References and Further Reading. Chapter 6. Fraction Factorial Designs: Economy in Experimentation. 6.1. Effects of Five Factors on Six Properties of Films in Eight Runs. 6.2. Stability of New Product, Four Factors in Eight Runs, a 24 1 Design. 6.3. A Half-Fraction Example: The Modification of a Bearing. 6.4. The Anatomy of the Half Fraction. 6.5. The 27 4III Design: A Bicycle Example. 6.6. Eight-Run Designs. 6.7. Using Table 6.6: An Illustration. 6.8. Sign Switching, Foldover, and Sequential Assembly. 6.9. An Investigation Using Multiple-Column Foldover. 6.10. Increasing Design Resolution from III to IV by Foldover. 6.11. Sixteen-Run Designs. 6.12. The 25 1 Nodal Half Replicate of the 25 Factorial: Reactor Example. 6.13. The 28 4 IV Nodal Sixteenth Fraction of a 28 Factorial. 6.14. The 215 11 III Nodal Design: The Sixty-Fourth Fraction of the 215 Factorial. 6.15. Constructing Other Two-Level Fractions. 6.16. Elimination of Block Effects. References and Further Reading. Chapter 7. Other Fractionals, Analysis and Choosing Follow-up Runs. 7.1. Plackett and Burman Designs. 7.2. Choosing Follow-Up Runs. 7.3. Justifications for the Use of Fractionals. Appendix 7A. Technical Details. Appendix 7B. An Approximate Partial Analysis for PB Designs. Appendix 7C. Hall's Orthogonal Designs. References and Further Reading. Chapter 8. Factorial Designs and Data Transformation. 8.1. A Two-Way (Factorial) Design. 8.2. Simplification and Increased Sensitivity from Transformation. Appendix 8A. Rationale for Data Transformation. Appendix 8B. Bartlett's chi2nu for Testing Inhomogeneity of Variance. References and Further Reading. Chapter 9. Multiple Sources of Variation: Split Plot Designs, Variance Components and Error Transmission. 9.1. Split-Plot Designs, Variance Components, and Error Transmission. 9.2. Split-Plot Designs. 9.3. Estimating Variance Components. 9.4. Transmission of Error. References and Further Reading. Chapter 10. Least Squares and Why You Need to Design Experiments. 10.1. Estimation With Least Squares. 10.2. The Versatility of Least Squares. 10.3. The Origins of Experimental Design. 10.4. Nonlinear Models. Appendix 10A. Vector Representation of Statistical Concepts. Appendix 10B. Matrix Version of Least Squares. Appendix 10C. Analysis of Factorials, Botched and Otherwise. Appendix 10D. Unweighted and Weighted Least Squares. References and Further Reading. Chapter 11. Modelling Relationships, Sequential Assembly: Basics for Response Surface Methods. 11.1. Some Empirical Models. 11.2. Some Experimental Designs and the Design Information Function. 11.3. Is the Surface Sufficiently Well Estimated? 11.4. Sequential Design Strategy. 11.5. Canonical Analysis. 11.6. Box-Behnken Designs. References and Further Reading. Chapter 12. Some Applications of Response Surface Methods. 12.1. Iterative Experimentation To Improve a Product Design. 12.2. Simplification of a Response Function by Data Transformation. 12.3. Detecting and Exploiting Active and Inactive Factor Spaces for Multiple-Response Data. 12.4. Exploring Canonical Factor Spaces. 12.5. From Empiricism to Mechanism. 12.6. Uses of RSM. Appendix 12A. Average Variance of y. Appendix 12B. References and Further Reading. Chapter 13. Designing Robust Products: An Introduction. 13.1. Environmental Robustness. 13.2. Robustness To Component Variation. Appendix 13A. A Mathematical Formulation for Environmental Robustness. Appendix 13B. Choice of Criteria. References and Further Reading. Chapter 14. Process Control, Forecasting and Times Series: An Introduction. 14.1. Process Monitoring. 14.2. The Exponentially Weighted Moving Average. 14.3. The CuSum Chart. 14.4. Process Adjustment. 14.5. A Brief Look At Some Time Series Models and Applications. 14.6. Using a Model to Make a Forecast. 14.7. Intervention Analysis: A Los Angeles Air Pollution Example. References and Further Reading. Chapter 15. Evolutionary Process Operation. 15.1. More than One Factor. 15.2. Multiple Responses. 15.3. The Evolutionary Process Operation Committee. References and Further Reading. Appendix Tables. Author Index. Subject Index.
1,720 citations
TL;DR: The origin and development of the adaptive approach to thermal comfort is explained, and recommendations made as to the best comfort temperature, the range of comfortable environments and the maximum rate of change of indoor temperature.
1,564 citations
Journal Article•
TL;DR: In this paper, the adaptive hypothesis predicts that contextual factors and past thermal history modify building occupants' thermal expectations and preferences, which is contrary to static assumptions underlying the current ASHRAE comfort standard 55-92.
Abstract: The adaptive hypothesis predicts that contextual factors and past thermal history modify building occupants' thermal expectations and preferences. One of the predictions of the adaptive hypothesis is that people in warm climate zones prefer warmer indoor temperatures than people living in cold climate zones. This is contrary to the static assumptions underlying the current ASHRAE comfort standard 55-92. To examine the adaptive hypothesis and its implications for Standard 55-92, the ASHRAE RP-884 project assembled a quality-controlled database from thermal comfort field experiments worldwide (circa 21,000 observations from 160 buildings). Our statistical analysis examined the semantics of thermal comfort in terms of thermal sensation,
1,455 citations