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Bayesian calibration with augmented stochastic state-space models of district-heated multifamily buildings
Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi.ORCID-id: 0000-0003-3530-0209
Department of Building Engineering, Energy Systems and Sustainability Science, University of Gävle, Gävle, Sweden.
2020 (Engelska)Ingår i: Energies, E-ISSN 1996-1073, Vol. 13, nr 1, artikel-id 76Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Reliable energy models are needed to determine building energy performance. Relatively detailed energy models can be auto-generated based on 3D shape representations of existing buildings. However, parameters describing thermal performance of the building fabric, the technical systems, and occupant behavior are usually not readily available. Calibration with on-site measurements is needed to obtain reliable energy models that can offer insight into buildings' actual energy performances. Here, we present an energy model that is suitable for district-heated multifamily buildings, based on a 14-node thermal network implementation of the ISO 52016-1:2017 standard. To better account for modeling approximations and noisy inputs, the model is converted to a stochastic state-space model and augmented with four additional disturbance state variables. Uncertainty models are developed for the inputs solar heat gains, internal heat gains, and domestic hot water use. An iterated extended Kalman filtering algorithm is employed to enable nonlinear state estimation. A Bayesian calibration procedure is employed to enable assessment of parameter uncertainty and incorporation of regulating prior knowledge. A case study is presented to evaluate the performance of the developed framework: parameter estimation with both dynamic Hamiltonian Monte Carlo sampling and penalized maximum likelihood estimation, the behavior of the filtering algorithm, the impact of different commonly occurring data sources for domestic hot water use, and the impact of indoor air temperature readings. 

Ort, förlag, år, upplaga, sidor
MDPI AG , 2020. Vol. 13, nr 1, artikel-id 76
Nyckelord [en]
Augmented stochastic state-space modeling, Bayesian calibration, Building energy performance, Energy models, Iterated Extended Kalman Filtering, Uncertainty, Buildings, Calibration, District heating, Energy efficiency, Extended Kalman filters, Hamiltonians, Hot water distribution systems, Maximum likelihood estimation, Monte Carlo methods, State space methods, Stochastic systems, Uncertainty analysis, Water, Energy model, Extended Kalman filtering, State - space models, Stochastic models
Nationell ämneskategori
Husbyggnad Sannolikhetsteori och statistik
Identifikatorer
URN: urn:nbn:se:mdh:diva-46727DOI: 10.3390/en13010076ISI: 000520425800076Scopus ID: 2-s2.0-85077310649OAI: oai:DiVA.org:mdh-46727DiVA, id: diva2:1386287
Tillgänglig från: 2020-01-17 Skapad: 2020-01-17 Senast uppdaterad: 2023-08-28Bibliografiskt granskad
Ingår i avhandling
1. Probabilistic Calibration of Building Energy Models: For Scalable and Detailed Energy Performance Assessment of District-Heated Multifamily Buildings
Öppna denna publikation i ny flik eller fönster >>Probabilistic Calibration of Building Energy Models: For Scalable and Detailed Energy Performance Assessment of District-Heated Multifamily Buildings
2020 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

There is a global need to reduce energy consumption and integrate a larger share of renewable energy production while meeting expectations for human well-being and economic growth. Buildings have a key role to play in this transition to more sustainable cities and communities.

Building energy modeling (BEM) and simulation are needed to gain detailed knowledge ofthe heat flows and parameters that determine the thermal energy performance of a building. Remote sensing techniques have enabled the generation of geometrical representations of existing buildings on the scale of entire cities. However, parameters describing the thermal properties ofthe building envelope and the technical systems are usually not readily accessible in a digitized form and need to be inferred. Further, buildings are complex systems with indoor environmental conditions that vary dynamically under the stochastic influence of weather and occupant behavior and the availability of metering data is often limited. Consequently, robust inference is needed to handle high and time-varying uncertainty and a varying degree of data availability.

This thesis starts with investigation of meteorological reanalyses, remote sensing and onsite metering data sources. Next, the developed dynamic and physics-based BEM, consisting of a thermal network and modeling procedures for the technical systems, passive heat gains and boundary conditions, is presented. Finally, the calibration framework is presented, including a method to transform a deterministic BEM into a fully probabilistic BEM, an iterated extended Kalman filtering algorithm and a probabilistic calibration procedure to infer uncertain parameters and incorporate prior knowledge.

The results suggest that the proposed BEM is sufficiently detailed to provide actionable insights, while remaining identifiable given a sufficiently informative prior model. Such a prior model can be obtained based solely on knowledge of the underlying physical properties of the parameters, but also enables incorporation of more specific information about the building. The probabilistic calibration approach has the capability to combine evidence from both data and knowledge-based sources; this is necessary for robust inference given the often highly uncertain reality in which buildings operate.

The contributions of this thesis bring us a step closer to producing models of existing buildings, on the scale of whole cities, that can simulate reality sufficiently well to gain actionable insights on thermal energy performance, enable buildings to act as active components of the energy system and ultimately increase the operational resilience of the built environment.

Ort, förlag, år, upplaga, sidor
Västerås: Mälardalen University, 2020
Serie
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 318
Nationell ämneskategori
Energisystem
Forskningsämne
energi- och miljöteknik
Identifikatorer
urn:nbn:se:mdh:diva-49378 (URN)978-91-7485-473-2 (ISBN)
Disputation
2020-09-10, Milos + digital (Zoom), Mälardalens högskola, Västerås, 10:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2020-07-06 Skapad: 2020-07-06 Senast uppdaterad: 2020-07-10Bibliografiskt granskad

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