Special Issue "Bayesian Building Energy Modeling"

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (31 January 2018).

Special Issue Editors

Prof. Dr. Koen Steemers
E-Mail Website
Guest Editor
The Department of Architecture, Faculty of Architecture and History of Art, University of Cambridge, 1-5 Scroope Terrace, Cambridge, CB2 1PX, UK
Interests: environmental building and urban design; comfort, perception and well being; energy efficiency
Dr. Yeonsook Heo
E-Mail Website
Guest Editor
Department of Architecture, University of Cambridge, 1-5 Scroope Terrace, Cambridge CB2 1PX, UK.
Interests: building energy simulation; performance-based evaluation; uncertainty analysis; model calibration

Special Issue Information

Dear Colleagues,

There has been a great deal of recent interest in Bayesian approaches to model, analyze, and interpret data for building energy applications.

Bayesian approaches are well suited to uncertainty analysis—an issue of particular relevance in building energy performance. Using expert knowledge, Bayesian models can leverage statistical information on uncertain parameters related to for example building design, construction, control and behaviour. Bayesian techniques treat a probability as a numerical estimate of the degree-of-belief in a hypothesis. In these approaches, uncertain parameters are assigned prior distributions based on expert judgement and updated using observations through the Bayes formula to obtain updated posterior probability distributions.

Areas in which Bayesian approaches have been increasingly used include:

  • Calibration of uncertain model parameters in energy simulation models

  • Development of Bayesian statistical models for energy prediction based on measurements or observations

  • Development of Bayesian network models for predicting occupant behaviour related to energy use

  • Decision-making based on Bayesian decision theory

We invite colleagues from the building energy performance and modeling community to submit abstracts related to and expanding on the above themes.

Prof. Koen Steemers
Dr. Yeonsook Heo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Building energy

  • Bayesian modeling

  • uncertainty analysis

  • occupant behavior

  • energy simulation

Published Papers (5 papers)

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Open AccessCommunication
A Practical Guide to Gaussian Process Regression for Energy Measurement and Verification within the Bayesian Framework
Energies 2018, 11(4), 935; https://doi.org/10.3390/en11040935 - 14 Apr 2018
Cited by 2
Abstract
Measurement and Verification (M&V) aims to quantify savings achieved as part of energy efficiency and energy management projects. M&V depends heavily on metered energy data, modelling parameters and uncertainties that govern the energy system under consideration. M&V therefore requires a stringent handle on [...] Read more.
Measurement and Verification (M&V) aims to quantify savings achieved as part of energy efficiency and energy management projects. M&V depends heavily on metered energy data, modelling parameters and uncertainties that govern the energy system under consideration. M&V therefore requires a stringent handle on the inherent uncertainties in the calculated savings. The Bayesian framework of data analysis in the form of non-parametric, nonlinear Gaussian Process (GP) regression provides a mechanism by which these uncertainties can be quantified thoroughly, and is therefore an attractive alternative to the more traditional frequentist approach. It is important to select appropriate kernels to construct the prior when performing GP regression. This paper aims to construct a guideline for a practical GP regression within the energy M&V framework. It does not attempt to quantify energy losses or savings, but rather presents a case study that could act as a road map for energy managers and M&V professionals to apply the GP regression as a Bayesian alternative to base-line adjustment. Special attention will be given to the selection of appropriate kernels for the application of baseline adjustment and energy savings quantification in a model-independent manner. Full article
(This article belongs to the Special Issue Bayesian Building Energy Modeling)
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Open AccessArticle
Robust Building Energy Load Forecasting Using Physically-Based Kernel Models
Energies 2018, 11(4), 862; https://doi.org/10.3390/en11040862 - 08 Apr 2018
Cited by 2
Abstract
Robust and accurate building energy load forecasting is important for helping building managers and utilities to plan, budget, and strategize energy resources in advance. With recent prevalent adoption of smart-meters in buildings, a significant amount of building energy consumption data became available. Many [...] Read more.
Robust and accurate building energy load forecasting is important for helping building managers and utilities to plan, budget, and strategize energy resources in advance. With recent prevalent adoption of smart-meters in buildings, a significant amount of building energy consumption data became available. Many studies have developed physics-based white box models and data-driven black box models to predict building energy consumption; however, they require extensive prior knowledge about building system, need a large set of training data, or lack robustness to different forecasting scenarios. In this paper, we introduce a new building energy forecasting method based on Gaussian Process Regression (GPR) that incorporates physical insights about load data characteristics to improve accuracy while reducing training requirements. The GPR is a non-parametric regression method that models the data as a joint Gaussian distribution with mean and covariance functions and forecast using the Bayesian updating. We model the covariance function of the GPR to reflect the data patterns in different forecasting horizon scenarios, as prior knowledge. Our method takes advantage of the modeling flexibility and computational efficiency of the GPR while benefiting from the physical insights to further improve the training efficiency and accuracy. We evaluate our method with three field datasets from two university campuses (Carnegie Mellon University and Stanford University) for both short- and long-term load forecasting. The results show that our method performs more accurately, especially when the training dataset is small, compared to other state-of-the-art forecasting models (up to 2.95 times smaller prediction error). Full article
(This article belongs to the Special Issue Bayesian Building Energy Modeling)
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Open AccessArticle
A Bayesian Dynamic Method to Estimate the Thermophysical Properties of Building Elements in All Seasons, Orientations and with Reduced Error
Energies 2018, 11(4), 802; https://doi.org/10.3390/en11040802 - 30 Mar 2018
Cited by 5Correction
Abstract
The performance gap between the expected and actual energy performance of buildings and elements has stimulated interest in in-situ measurements. Most research has employed quasi-static analysis methods that estimate heat loss metrics such as U-values, without taking advantage of the rich time series [...] Read more.
The performance gap between the expected and actual energy performance of buildings and elements has stimulated interest in in-situ measurements. Most research has employed quasi-static analysis methods that estimate heat loss metrics such as U-values, without taking advantage of the rich time series data that is often recorded. This paper presents a dynamic Bayesian-based method to estimate the thermophysical properties of building elements from in-situ measurements. The analysis includes Markov chain Monte Carlo (MCMC) estimation, priors, uncertainty analysis, and model comparison to select the most appropriate model. Data from two case study dwellings is used to illustrate model performance; U-value estimates from the dynamic and static methods are within error estimates, with the dynamic model generally requiring much shorter time series than the static model. The dynamic model produced robust results at all times of year, including when the average indoor-to-outdoor temperature difference was low, when external temperatures had large daily variation, and measurements were subjected to direct solar radiation. Further, the probability distributions of parameters may provide insights into the thermal performance of elements. Dynamic methods such as that presented herein may enable wider characterisation of the performance of building elements as built, supporting work to reduce the performance gap. Full article
(This article belongs to the Special Issue Bayesian Building Energy Modeling)
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Open AccessArticle
Bayesian Energy Measurement and Verification Analysis
Energies 2018, 11(2), 380; https://doi.org/10.3390/en11020380 - 06 Feb 2018
Cited by 4
Abstract
Energy Measurement and Verification (M&V) aims to make inferences about the savings achieved in energy projects, given the data and other information at hand. Traditionally, a frequentist approach has been used to quantify these savings and their associated uncertainties. We demonstrate that the [...] Read more.
Energy Measurement and Verification (M&V) aims to make inferences about the savings achieved in energy projects, given the data and other information at hand. Traditionally, a frequentist approach has been used to quantify these savings and their associated uncertainties. We demonstrate that the Bayesian paradigm is an intuitive, coherent, and powerful alternative framework within which M&V can be done. Its advantages and limitations are discussed, and two examples from the industry-standard International Performance Measurement and Verification Protocol (IPMVP) are solved using the framework. Bayesian analysis is shown to describe the problem more thoroughly and yield richer information and uncertainty quantification results than the standard methods while not sacrificing model simplicity. We also show that Bayesian methods can be more robust to outliers. Bayesian alternatives to standard M&V methods are listed, and examples from literature are cited. Full article
(This article belongs to the Special Issue Bayesian Building Energy Modeling)
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Open AccessCorrection
Correction: Gori, V.; Biddulph, P.; Elwell, C.A. A Bayesian Dynamic Method to Estimate the Thermophysical Properties of Building Elements in All Seasons, Orientations and with Reduced Error. Energies 2018, 11, 802
Energies 2018, 11(9), 2353; https://doi.org/10.3390/en11092353 - 06 Sep 2018
Abstract
The authors wish to make the following corrections to their paper [1]: [...] Full article
(This article belongs to the Special Issue Bayesian Building Energy Modeling)
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