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Adaptive Weather Correction of Energy Consumption Data
Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Eskilstuna Kommunfastighet, Eskilstuna, Sweden.ORCID iD: 0000-0003-3530-0209
2017 (English)In: Energy Procedia, Elsevier Ltd , 2017, p. 3397-3402Conference paper, Published paper (Refereed)
Abstract [en]

A framework for adaptive weather correction of energy consumption data is presented. The procedure is conducted in two steps: I) a regression model is trained on a building's recent historical energy consumption, weather and calendar data; II) energy consumption is predicted by using long term weather data as input to the trained model. Thus the buildings long term energy consumption is obtained, from which the building's expected (alias normalised or weather corrected) yearly energy consumption is derived. For older Swedish residential buildings, the proposed regression method matches traditional heating degree days method in accuracy. But for low energy and near zero energy buildings the regression method is more accurate, especially for years of extreme weather and for building with more complex installations such as heat pumps or solar thermal panels. The main benefit of the developed weather correction method is that it adapts to the data, therefore most buildings (or any kinds of weather dependent processes) can be weather corrected in an automated way. © 2017 The Authors. Published by Elsevier Ltd.

Place, publisher, year, edition, pages
Elsevier Ltd , 2017. p. 3397-3402
Keywords [en]
adaptive regression, normalisation, statistical learning, weather correction, Buildings, Energy conservation, Regression analysis, Correction method, Energy consumption datum, Residential building, Traditional heating, Zero energy building (ZEB), Energy utilization
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-36065DOI: 10.1016/j.egypro.2017.03.778ISI: 000404967903075Scopus ID: 2-s2.0-85020709866OAI: oai:DiVA.org:mdh-36065DiVA, id: diva2:1120631
Conference
8th International Conference on Applied Energy, ICAE 2016, 8 October 2016 through 11 October 2016
Available from: 2017-07-06 Created: 2017-07-06 Last updated: 2020-07-06Bibliographically approved
In thesis
1. Probabilistic Calibration of Building Energy Models: For Scalable and Detailed Energy Performance Assessment of District-Heated Multifamily Buildings
Open this publication in new window or tab >>Probabilistic Calibration of Building Energy Models: For Scalable and Detailed Energy Performance Assessment of District-Heated Multifamily Buildings
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
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.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2020
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 318
National Category
Energy Systems
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-49378 (URN)978-91-7485-473-2 (ISBN)
Public defence
2020-09-10, Milos + digital (Zoom), Mälardalens högskola, Västerås, 10:00 (English)
Opponent
Supervisors
Available from: 2020-07-06 Created: 2020-07-06 Last updated: 2020-07-10Bibliographically approved

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Lundström, Lukas

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