Prediction and fault detection of building energy consumption using multi-input, single-output dynamic model

Jouko Pakanen

Research output: Book/ReportReport

Abstract

For many years degree-days methods have been used to estimate building energy consumption. If the difference between the actual and estimated energy consumption is large enough there may be good reasons to check the condition of HVAC-system. However, because degree-day method is based only on the outdoor temperature, there may be large differences between the actual and the calculated results without any noticeable failure in the system. A new method is proposed, based upon on-line measurements of weather and other characteristic data. The method is capable of being used with building automation systems for failure detection. The method uses a multi-input, single-output (MISO) dynamic model to predict the power fluctuation of the building. The model parameters are identified recursively by measuring the actual power, outdoor temperature, solar radiation, wind velocity and indoor temperatures. Other measurements may also be used as input data. The stochastic variations in power caused by occupants, equipment, lights, etc., can be included in the model. Measurements are taken once an half hour and are used to update the model parameters by a recursive extended least square algorithm (RELS). The identified model can be used to detect failures of HVAC equipment and systems. Verification of the method has been accomplished using real weather data and the TARP-computer program for the simulation of a townhouse and measurements collected from a real test building.
Original languageEnglish
Place of PublicationEspoo
PublisherVTT Technical Research Centre of Finland
Number of pages47
ISBN (Print)951-38-4234-7
Publication statusPublished - 1992
MoE publication typeNot Eligible

Publication series

SeriesVTT Publications
Number116
ISSN1235-0621

Fingerprint

Fault detection
Dynamic models
Energy utilization
Solar radiation
Temperature
Computer program listings
Automation
HVAC

Keywords

  • models
  • predictions
  • solar radiation
  • energy consumption
  • estimates
  • HVAC
  • temperature
  • indoor air
  • dynamic properties
  • fault analysis
  • data processing
  • computer programs
  • buildings
  • methods
  • calculations
  • wind velocity
  • building automation
  • weather
  • measurement
  • failure
  • detection
  • simulation
  • variations

Cite this

Pakanen, J. (1992). Prediction and fault detection of building energy consumption using multi-input, single-output dynamic model. Espoo: VTT Technical Research Centre of Finland. VTT Publications, No. 116
Pakanen, Jouko. / Prediction and fault detection of building energy consumption using multi-input, single-output dynamic model. Espoo : VTT Technical Research Centre of Finland, 1992. 47 p. (VTT Publications; No. 116).
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abstract = "For many years degree-days methods have been used to estimate building energy consumption. If the difference between the actual and estimated energy consumption is large enough there may be good reasons to check the condition of HVAC-system. However, because degree-day method is based only on the outdoor temperature, there may be large differences between the actual and the calculated results without any noticeable failure in the system. A new method is proposed, based upon on-line measurements of weather and other characteristic data. The method is capable of being used with building automation systems for failure detection. The method uses a multi-input, single-output (MISO) dynamic model to predict the power fluctuation of the building. The model parameters are identified recursively by measuring the actual power, outdoor temperature, solar radiation, wind velocity and indoor temperatures. Other measurements may also be used as input data. The stochastic variations in power caused by occupants, equipment, lights, etc., can be included in the model. Measurements are taken once an half hour and are used to update the model parameters by a recursive extended least square algorithm (RELS). The identified model can be used to detect failures of HVAC equipment and systems. Verification of the method has been accomplished using real weather data and the TARP-computer program for the simulation of a townhouse and measurements collected from a real test building.",
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author = "Jouko Pakanen",
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Pakanen, J 1992, Prediction and fault detection of building energy consumption using multi-input, single-output dynamic model. VTT Publications, no. 116, VTT Technical Research Centre of Finland, Espoo.

Prediction and fault detection of building energy consumption using multi-input, single-output dynamic model. / Pakanen, Jouko.

Espoo : VTT Technical Research Centre of Finland, 1992. 47 p. (VTT Publications; No. 116).

Research output: Book/ReportReport

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T1 - Prediction and fault detection of building energy consumption using multi-input, single-output dynamic model

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N1 - Project code: RAK2408

PY - 1992

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N2 - For many years degree-days methods have been used to estimate building energy consumption. If the difference between the actual and estimated energy consumption is large enough there may be good reasons to check the condition of HVAC-system. However, because degree-day method is based only on the outdoor temperature, there may be large differences between the actual and the calculated results without any noticeable failure in the system. A new method is proposed, based upon on-line measurements of weather and other characteristic data. The method is capable of being used with building automation systems for failure detection. The method uses a multi-input, single-output (MISO) dynamic model to predict the power fluctuation of the building. The model parameters are identified recursively by measuring the actual power, outdoor temperature, solar radiation, wind velocity and indoor temperatures. Other measurements may also be used as input data. The stochastic variations in power caused by occupants, equipment, lights, etc., can be included in the model. Measurements are taken once an half hour and are used to update the model parameters by a recursive extended least square algorithm (RELS). The identified model can be used to detect failures of HVAC equipment and systems. Verification of the method has been accomplished using real weather data and the TARP-computer program for the simulation of a townhouse and measurements collected from a real test building.

AB - For many years degree-days methods have been used to estimate building energy consumption. If the difference between the actual and estimated energy consumption is large enough there may be good reasons to check the condition of HVAC-system. However, because degree-day method is based only on the outdoor temperature, there may be large differences between the actual and the calculated results without any noticeable failure in the system. A new method is proposed, based upon on-line measurements of weather and other characteristic data. The method is capable of being used with building automation systems for failure detection. The method uses a multi-input, single-output (MISO) dynamic model to predict the power fluctuation of the building. The model parameters are identified recursively by measuring the actual power, outdoor temperature, solar radiation, wind velocity and indoor temperatures. Other measurements may also be used as input data. The stochastic variations in power caused by occupants, equipment, lights, etc., can be included in the model. Measurements are taken once an half hour and are used to update the model parameters by a recursive extended least square algorithm (RELS). The identified model can be used to detect failures of HVAC equipment and systems. Verification of the method has been accomplished using real weather data and the TARP-computer program for the simulation of a townhouse and measurements collected from a real test building.

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KW - temperature

KW - indoor air

KW - dynamic properties

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KW - data processing

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Pakanen J. Prediction and fault detection of building energy consumption using multi-input, single-output dynamic model. Espoo: VTT Technical Research Centre of Finland, 1992. 47 p. (VTT Publications; No. 116).