Energie-Monitoring im privaten Haushalt (English shop)

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Most private households have only limited information about their electricity demand. While many people know the monthly cost of their electrical energy supply, they generally do not know the time profile of their energy demand, i.e. which appliance “consumes” how much electricity and when. They have only little knowledge of how, or by which measures, electrical energy can be saved in their household, since they lack the attribution of energy consumption to the individual appliances in the household. An energy monitoring system that visualises the time profile of the household’s active power consumption and allocates it accordingly to the individual electrical appliances can close this information gap.

The present work presents concepts and boundary conditions for the retrofit installation of an energy monitoring system in private households. In addition to a topology analysis of the necessary technical components of the system, a special optical sensor is presented with which many of the electromechanical three-phase meters already installed in households can be retrofitted for read-out. This makes it possible to record and visualise the profile of a household’s active power consumption with a time resolution of up to one second. The algorithms are likewise suitable for analysing active power profiles that can be recorded with the new future electronic household meters (eHZ).

Furthermore, load monitoring methods are presented in which the focus lies on self-learning algorithms in order to avoid manual inputs by the operator during initialisation of the system. The NIALM algorithms (Non-Intrusive Appliance Load Monitoring) developed here use special clustering methods as well as genetic algorithms to identify electrical loads from the measurement data of the overall active power load profile. The algorithms developed were tested on simulated and real daily load profiles of electrical active power consumption. They can likewise be adapted and applied to the analysis of arbitrary data vectors from other technical disciplines.