Comparison of Data-Driven Thermal Building Models for Model Predictive Control

Energy flexible buildings in combination with demand response will play a key role in the future smart grid. To implement control strategies, which enable demand response, like model predictive control, thermal building models are necessary. Therefore, three lumped capacitance models, are compared w...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Gernot Steindl [verfasserIn] Wolfgang Kastner [verfasserIn] Verena Stangl [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Data-driven Black-box model Gray-box model Model development Machine learning.

Übergeordnetes Werk:	In: Journal of Sustainable Development of Energy, Water and Environment Systems - SDEWES Centre, 2013, 7(2019), 4, Seite 730-742
Übergeordnetes Werk:	volume:7 ; year:2019 ; number:4 ; pages:730-742

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.13044/j.sdewes.d7.0286

Katalog-ID:	DOAJ050304933

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callnumber-first	H - Social Science
author	Gernot Steindl
spellingShingle	Gernot Steindl misc HD72-88 misc Data-driven misc Black-box model misc Gray-box model misc Model development misc Machine learning. misc Technology misc T misc Economic growth, development, planning Comparison of Data-Driven Thermal Building Models for Model Predictive Control
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topic_title	HD72-88 Comparison of Data-Driven Thermal Building Models for Model Predictive Control Data-driven Black-box model Gray-box model Model development Machine learning
topic	misc HD72-88 misc Data-driven misc Black-box model misc Gray-box model misc Model development misc Machine learning. misc Technology misc T misc Economic growth, development, planning
topic_unstemmed	misc HD72-88 misc Data-driven misc Black-box model misc Gray-box model misc Model development misc Machine learning. misc Technology misc T misc Economic growth, development, planning
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title	Comparison of Data-Driven Thermal Building Models for Model Predictive Control
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title_full	Comparison of Data-Driven Thermal Building Models for Model Predictive Control
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title_sort	comparison of data-driven thermal building models for model predictive control
callnumber	HD72-88
title_auth	Comparison of Data-Driven Thermal Building Models for Model Predictive Control
abstract	Energy flexible buildings in combination with demand response will play a key role in the future smart grid. To implement control strategies, which enable demand response, like model predictive control, thermal building models are necessary. Therefore, three lumped capacitance models, are compared with a k-Nearest Neighbor regression model. All models show accurate prediction results, if the operating condition of the building is similar during parameter identification or rather during training and the validation period. Parameter identification of lumped capacitance models is a time-consuming task. Especially for complex lumped capacitance models, the search space for certain parameters has to be reduced to avoid local minima. The investigated k-Nearest Neighbor algorithm has the advantage of easy implementation, very fast training and minimal effort for parameter identification in combination with accurate predictions. But its seasonal dependency is very strong, which can be easily overcome with periodically data update, as it is an instance-based learning algorithm.
abstractGer	Energy flexible buildings in combination with demand response will play a key role in the future smart grid. To implement control strategies, which enable demand response, like model predictive control, thermal building models are necessary. Therefore, three lumped capacitance models, are compared with a k-Nearest Neighbor regression model. All models show accurate prediction results, if the operating condition of the building is similar during parameter identification or rather during training and the validation period. Parameter identification of lumped capacitance models is a time-consuming task. Especially for complex lumped capacitance models, the search space for certain parameters has to be reduced to avoid local minima. The investigated k-Nearest Neighbor algorithm has the advantage of easy implementation, very fast training and minimal effort for parameter identification in combination with accurate predictions. But its seasonal dependency is very strong, which can be easily overcome with periodically data update, as it is an instance-based learning algorithm.
abstract_unstemmed	Energy flexible buildings in combination with demand response will play a key role in the future smart grid. To implement control strategies, which enable demand response, like model predictive control, thermal building models are necessary. Therefore, three lumped capacitance models, are compared with a k-Nearest Neighbor regression model. All models show accurate prediction results, if the operating condition of the building is similar during parameter identification or rather during training and the validation period. Parameter identification of lumped capacitance models is a time-consuming task. Especially for complex lumped capacitance models, the search space for certain parameters has to be reduced to avoid local minima. The investigated k-Nearest Neighbor algorithm has the advantage of easy implementation, very fast training and minimal effort for parameter identification in combination with accurate predictions. But its seasonal dependency is very strong, which can be easily overcome with periodically data update, as it is an instance-based learning algorithm.
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title_short	Comparison of Data-Driven Thermal Building Models for Model Predictive Control
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