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A. Taal
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A much occurring problem in the Energy Management Systems of existing buildings and HVAC services is that the measurements are unreliable. In this article a methodology is described which can be used to determine the presence of errors in energy monitoring, caused by faulty measurements. These errors can be detected and subsequently diagnosed. Detection of monitoring errors is done based on occurring symptoms. Determination of these symptoms is done using the laws of conservation of energy, mass and pressure. The diagnosis is done by using a statistical method based on Bayesian theory in which the chance of an error occurring is determined based on ( combinations of) the symptoms. The method is built in a Bayesian Belief Network (BBN) software tool. The advantage of BBN is that it is consistent with the working methods of experts in installation technology.
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A much occurring problem in the Energy Management Systems of existing buildings and HVAC services is that the measurements are unreliable. In this article a methodology is described which can be used to determine the presence of errors in energy monitoring, caused by faulty measurements. These errors can be detected and subsequently diagnosed. Detection of monitoring errors is done based on occurring symptoms. Determination of these symptoms is done using the laws of conservation of energy, mass and pressure. The diagnosis is done by using a statistical method based on Bayesian theory in which the chance of an error occurring is determined based on ( combinations of) the symptoms. The method is built in a Bayesian Belief Network (BBN) software tool. The advantage of BBN is that it is consistent with the working methods of experts in installation technology.
The Hague University in Delft uses an advanced climate control system. All sensors and actuators are monitored and deviations from the sensor data are reported daily. The building manager will have to combine the information from the sensor data in order to draw the right conclusions. In this paper, two possible solutions are described for analyzing the data by a computer program. The first solution is by means of a rule-based program, in which predetermined situations have been defined. The data from the sensors are fed into the program and the program checks whether it matches any of the situations. The second solution is to make use of a Bayesian Belief Network. This is a mathematical model that describes the symptoms and causes of a particular problem. With imported sensor data a computer program calculates the likelihood of particular causes of data symptoms.
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The Hague University in Delft uses an advanced climate control system. All sensors and actuators are monitored and deviations from the sensor data are reported daily. The building manager will have to combine the information from the sensor data in order to draw the right conclusions. In this paper, two possible solutions are described for analyzing the data by a computer program. The first solution is by means of a rule-based program, in which predetermined situations have been defined. The data from the sensors are fed into the program and the program checks whether it matches any of the situations. The second solution is to make use of a Bayesian Belief Network. This is a mathematical model that describes the symptoms and causes of a particular problem. With imported sensor data a computer program calculates the likelihood of particular causes of data symptoms.