An Inverse Gray-Box Model for Transient Building Load Prediction

Braun, James E.; Chaturvedi, Nitin Dutt

doi:10.1080/10789669.2002.10391290

Cited by 276 publications

(100 citation statements)

References 8 publications

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“…The heat dynamics of a building can be described by lumped parameter RC-models, see for example [17], [18] and [19]. As described by [12] the response in the heat load to changes in the climate variables can be modeled with a rational transfer function, which is a description with an RC-model of the low-pass filtering effect through the building.…”

Section: Low-pass Filtering For Modeling Of Building Heat Dynamicsmentioning

confidence: 99%

Short-term heat load forecasting for single family houses

Bacher

Madsen

Nielsen³

et al. 2013

Energy and Buildings

100

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This paper presents a method for forecasting the load for space heating in a single-family house. The forecasting model is built using data from sixteen houses located in Sønderborg, Denmark, combined with local climate measurements and weather forecasts. Every hour the hourly heat load for each house the following two days is forecasted. The forecast models are adaptive linear time-series models and the climate inputs used are: ambient temperature, global radiation and wind speed. A computationally efficient recursive least squares scheme is used. The models are optimized to fit the individual characteristics for each house, such as the level of adaptivity and the thermal dynamical response of the building, which is modeled with simple transfer functions. Identification of a model, which is suitable for all the houses, is carried out. The results show that the one-step ahead errors are close to white noise and that practically all correlation to the climate variables are removed. Furthermore, the results show that the forecasting errors mainly are related to: unpredictable high frequency variations in the heat load signal (predominant only for some houses), shifts in resident behavior patterns and uncertainty of the weather forecasts for longer horizons, especially for solar radiation.

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Section: Low-pass Filtering For Modeling Of Building Heat Dynamicsmentioning

confidence: 99%

Short-term heat load forecasting for single family houses

Bacher

Madsen

Nielsen³

et al. 2013

Energy and Buildings

100

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“…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%

Eliminating oscillations in TRV-controlled hydronic radiators

Tahersima

Stoustrup

Rasmussen

2011

IEEE Conference on Decision and Control and European Control Conference

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Abstract-Thermostatic Radiator Valves (TRV) have proved their significant contribution in energy savings for several years. However, at low heat demands, an unstable oscillatory behavior is usually observed and well known for these devices. This instability is due to the nonlinear dynamics of the radiator itself which result in a large time constant and high gain for radiator at low flows. A remedy to this problem is to make the controller of TRVs adaptable with the operating point instead of widely used fixed PI controllers. To this end, we have derived a linear parameter varying model of radiator, formulated based on the operating flow rate, room temperature and the radiator specifications. In order to derive such formulation, the partial differential equation of the radiator heat transfer dynamics is solved analytically. Using the model, a gain schedule controller among various possible control strategies is designed for the TRV. It is shown via simulations that the designed controller based on the proposed LPV model performs excellent and stable in the whole operating conditions.

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“…This model type needs sufficient amount of measurement data for parameter identification and some information about the properties of the building. Generally, the methodology adopts a simplified physical model structure and then its parameters are estimated using measured data [36,37].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Model for Indoor Temperature Prediction in Buildings

2018

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A novel dynamic model for the temperature inside buildings is presented, aiming to improve energy efficiency by providing predictive information on the heat demand. To analyse the performance and generalizability of the modelling approach, real measurement data was gathered from five different types of buildings. Easily available data from various sources was utilized. The chosen model structure leads to a minimal number of input variables and free parameters. Simulations with real data from five buildings, and applying the identical model structure showed that the average modelling error during the 28-h prediction horizon was constantly below 5%. The results thus demonstrate that the model structure can be standardized and easily applied to predict the indoor temperatures of large buildings. This would finally enable demand side management and the predictive optimization of the heat demand at city level.

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An Inverse Gray-Box Model for Transient Building Load Prediction

Cited by 276 publications

References 8 publications

Short-term heat load forecasting for single family houses

Short-term heat load forecasting for single family houses

Eliminating oscillations in TRV-controlled hydronic radiators

A Dynamic Model for Indoor Temperature Prediction in Buildings

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