A Disaggregation Model for Studying Behaviours in Power Consumption

Sammanfattning: A feature of the Smart Grid is the utilization of flexible load in the power system. The presence of flexible load allows part of the power consumption to be shifted from peak hours to off-peak hours; this change in power consumption is called a load shift. If the usage pattern of appliances is identified, it is possible to estimate the capacity of a potential load shift as well as evaluate if the utilization of flexible load in the power system results in a load shift. This master thesis project aims to create a model which works as an aid when studying usage patterns by identifying when appliances that contribute to the load shift are active. The model should be able to give approximations of the switch-on and switch-off time of the appliances using only information from a single meter that measures the total power consumption of the entire household. Recently, artificial neural networks have been successfully applied to these kinds of problems. The constructed model thus includes neural networks which regress the start time and end time of a target appliance. The networks are trained and evaluated both on simulated data and on real measured data from the Stockholm Royal Seaport project. The model is able to give highly accurate estimates of the start and stop time when trained with simulated data. When using real data the accuracy of the model is relatively low. In order to increase the performance the neural network part of the model has to be trained on a larger dataset. A study of how the sampling time of the input affects the performance of the model is also carried out. The results show no evidence that the sampling time affects the accuracy of the model. However, the architecture of the neural networks trained to recognize data with different sampling frequencies are not identical; if the pooling layers of all networks were removed it might be possible to establish a connection between sampling time and performance.