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Vorpat Inkarojrit

Bio: Vorpat Inkarojrit is an academic researcher. The author has an hindex of 1, co-authored 1 publications receiving 99 citations.

Papers
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01 Oct 2005
TL;DR: Inarojrit and Vorpat as mentioned in this paper developed predictive models of window blind control that could be used as a function in energy simulation programs and provide the basis for the development of future automated shading systems.
Abstract: Author(s): Inkarojrit, Vorpat | Abstract: The goal of this study was to develop predictive models of window blind control that could be used as a function in energy simulation programs and provide the basis for the development of future automated shading systems. Toward this goal, a two-part study, consisting of a window blind usage survey and a field study, was conducted in Berkeley, California, USA, during a period spanning from the vernal equinox to window solstice. A total of one hundred and thirteen office building occupants participated in the survey. Twenty-five occupants participated in the field study, in which measurements of physical environmental conditions were cross-linked to the participants’ assessment of visual and thermal comfort sensations.Results from the survey showed that the primary window blind closing reason was to reduce glare from sunlight and bright windows. For the field study, a total of thirteen predictive window blind control logistic models were derived using the Generalized Estimating Equations (GEE) technique.

101 citations


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Journal ArticleDOI
TL;DR: Applied Regression Analysis Bibliography Update 2000–2001,” Communications in Statistics: Theory and Methods, 2051– 2075.
Abstract: Christensen, R. (2002), Plane Answers to Complex Questions: The Theory of Linear Models (3rd ed.), New York: Springer-Verlag. Crocker, D. C. (1980), Review of Linear Regression Analysis, by G. A. F. Seber, Technometrics, 22, 130. Datta, B. N. (1995), Numerical Linear Algebra and Applications, PaciŽ c Grove, CA: Brooks/Cole. Draper, N. R. (2002), “Applied Regression Analysis Bibliography Update 2000–2001,” Communications in Statistics: Theory and Methods, 2051– 2075. Golub, G. H., and Van Loan, C. F. (1996), Matrix Computations (3rd ed.), Baltimore, MD: Johns Hopkins University Press. Graybill, F. A. (2000), Theory and Application of the Linear Model, PaciŽ c Grove, CA: Brooks/Cole. Hocking, R. R. (2003), Methods and Applications of Linear Models: Regression and the Analysis of Variance (2nd ed.), New York: Wiley. Porat, B. (1993), Digital Processing of Random Signals, Englewood Cliffs, NJ: Prentice-Hall. Ravishanker, N., and Dey, D. K. (2002), A First Course in Linear Model Theory, Boca Raton, FL: Chapman and Hall/CRC. White, H. (1984), Asymptotic Theory for Econometricians, Orlando, FL: Academic Press.

862 citations

Journal ArticleDOI
TL;DR: In this paper, the state-of-the-art research, current obstacles and future needs and directions for the following four-step iterative process: (1) occupant monitoring and data collection, (2) model development, (3) model evaluation, and (4) model implementation into building simulation tools.

629 citations

Journal ArticleDOI
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

Journal ArticleDOI
TL;DR: It is concluded that personal comfort models based on occupants’ heating and cooling behavior can effectively predict individuals’ thermal preference and can therefore be used in everyday comfort management to improve occupant satisfaction and energy use in buildings.

302 citations

Journal ArticleDOI
TL;DR: In this article, the authors present a review of the literature on human dimensions of building energy use to assess the state-of-the-art in this topic area and highlight research needs for fully integrating human dimensions into the building design and operation processes with the goal of reducing energy use in buildings while enhancing occupant comfort and productivity.
Abstract: The “human dimensions” of energy use in buildings refer to the energy-related behaviors of key stakeholders that affect energy use over the building life cycle. Stakeholders include building designers, operators, managers, engineers, occupants, industry, vendors, and policymakers, who directly or indirectly influence the acts of designing, constructing, living, operating, managing, and regulating the built environments, from individual building up to the urban scale. Among factors driving high-performance buildings, human dimensions play a role that is as significant as that of technological advances. However, this factor is not well understood, and, as a result, human dimensions are often ignored or simplified by stakeholders. This paper presents a review of the literature on human dimensions of building energy use to assess the state-of-the-art in this topic area. The paper highlights research needs for fully integrating human dimensions into the building design and operation processes with the goal of reducing energy use in buildings while enhancing occupant comfort and productivity. This research focuses on identifying key needs for each stakeholder involved in a building’s life cycle and takes an interdisciplinary focus that spans the fields of architecture and engineering design, sociology, data science, energy policy, codes, and standards to provide targeted insights. Greater understanding of the human dimensions of energy use has several potential benefits including reductions in operating cost for building owners; enhanced comfort conditions and productivity for building occupants; more effective building energy management and automation systems for building operators and energy managers; and the integration of more accurate control logic into the next generation of human-in-the-loop technologies. The review concludes by summarizing recommendations for policy makers and industry stakeholders for developing codes, standards, and technologies that can leverage the human dimensions of energy use to reliably predict and achieve energy use reductions in the residential and commercial buildings sectors.

258 citations