<p>Within the next years, the amount of consumption data will increase rapidly as old meters will be exchanged in favor of meters with hourly remote reading. A new refined supervision system must be developed. The main objective of this thesis is to investigate mathematical methods that can be used to find incorrect hourly measurements in district heat and electricity consumption, for each consumer. </p><p>A simulation model and a statistical model have been derived. The model parameters in the simulation model are estimated by using historical data of consumption and outdoor temperature. By using the outdoor temperature as input, the consumption can be simulated and compared to the actual consumption. Faults are detected by using a residual with a sliding window. The second model uses the fact that consumers with similar consumption patterns can be grouped into a collective. By studying the correlation between the consumers, incorrect measurements can be found. </p><p>The performed simulations show that the simulation model is best suited for consumers whose consumption is mostly affected by the outdoor temperature. These consumers are district heat consumers and electricity consumers that use electricity for space heating. The fault detection performance of the statistical model is highly dependent on finding a collective that is well correlated. If these collectives can be found, the model can be used on district heat consumers as well as electricity consumers.</p>

Within the next years, the amount of consumption data will increase rapidly as old meters will be exchanged in favor of meters with hourly remote reading. A new refined supervision system must be developed. The main objective of this thesis is to investigate mathematical methods that can be used to find incorrect hourly measurements in district heat and electricity consumption, for each consumer. A simulation model and a statistical model have been derived. The model parameters in the simulation model are estimated by using historical data of consumption and outdoor temperature. By using the outdoor temperature as input, the consumption can be simulated and compared to the actual consumption. Faults are detected by using a residual with a sliding window. The second model uses the fact that consumers with similar consumption patterns can be grouped into a collective. By studying the correlation between the consumers, incorrect measurements can be found. The performed simulations show that the simulation model is best suited for consumers whose consumption is mostly affected by the outdoor temperature. These consumers are district heat consumers and electricity consumers that use electricity for space heating. The fault detection performance of the statistical model is highly dependent on finding a collective that is well correlated. If these collectives can be found, the model can be used on district heat consumers as well as electricity consumers.