1982
DOI: 10.1016/0360-1323(82)90004-x
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The measurement of dynamic thermal response in rooms using pseudo-random binary sequences

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Cited by 21 publications
(7 citation statements)
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“…Statistical method is an alternative approach to determine thermodynamic behaviours by using building performance data [Letherman, Pailing, & Park, 1982;Crawford & Woods, 1985;Andersen et al, 2002;Madsen & Holst, 1995;Braun & Chaturvedi, 2002;Wang and Xu, 2006]. Letherman et al (1982) described a technique to measure room thermal responses using pseudo-random binary sequences. Crawford and Woods (1985) described a method for estimation of continuous-time models for the heat dynamics of buildings using discrete-time building performance data.…”
Section: Approach For Developing a Physical Model For A District Heating Substationmentioning
confidence: 99%
“…Statistical method is an alternative approach to determine thermodynamic behaviours by using building performance data [Letherman, Pailing, & Park, 1982;Crawford & Woods, 1985;Andersen et al, 2002;Madsen & Holst, 1995;Braun & Chaturvedi, 2002;Wang and Xu, 2006]. Letherman et al (1982) described a technique to measure room thermal responses using pseudo-random binary sequences. Crawford and Woods (1985) described a method for estimation of continuous-time models for the heat dynamics of buildings using discrete-time building performance data.…”
Section: Approach For Developing a Physical Model For A District Heating Substationmentioning
confidence: 99%
“…3. Poor performance in the cold weather condition, applying the controller designed for the low demand situation [8], [9] and the data-driven methods [10], [11], [12] indicated by [2].…”
Section: B Problem Definitionmentioning
confidence: 99%
“…It is explicitly described how the measurement and model errors enter into the model. Other methods based on experimental data include: utilizing pseudorandom binary sequences of the input to derive the heat dynamics [6], and employing an inverse grey box thermal network model for transient building load prediction [7].The main drawbacks of this approach are that they require a significant amount of training data and may not always reflect the physical behavior [8].…”
Section: Introductionmentioning
confidence: 99%