Due to the increasing number of household electrical consumers, it is currently not possible for consumers to recognize which electrical consumers make a significant contribution to power consumption. At the end of the billing cycle, household electricity customers only receive information as to whether they have to pay more or less than in the previous period. Impacts caused by changes in user behavior or measures taken to increase energy efficiency are therefore incomprehensible to them. There is thus no possibility for electricity customers to determine the influencing factors on the energy requirement when dealing with electrical equipment. The aim of this project is therefore to develop a method for automated energy analysis that offers consumers a visually processed method for reducing, assessing and monitoring energy consumption. For this purpose, the method of load profile analysis is used, which is to be used with the help of the measurement data of smart meters. The basic idea of the method is to filter characteristic load profiles of individual large electric consumers from the summation load of smart meters. These include e.g. Refrigerators and freezers, stoves, ovens, washing machines, dryers, dishwashers, electric water heaters and heaters, as well as standby consumption. The devices are recognized by their specific changes caused by a recurrent pattern from the summation of the load. In addition, the possibility of end user calibration should be provided to identify large consumers that can not be detected or assigned by the automated algorithm, thus maximizing the accuracy of the analysis. In addition, deviations of the characteristic load profiles are used for the assessment of user behavior deficits and used for an automated display of energy saving possibilities. In order to be able to use synergies without incurring high costs for the purchase or installation of measuring devices (with high investment and operating costs), a prototype has to be developed in the project that reads out and analyzes the measured data from smart meters. Customers are presented the edited information visually. By analyzing the measurement data of a total of up to 20 households and verifying this by questioning the end customer and additional measurements, for a budget of energy consumption can be automatically visualized and broken down for the first time, energy saving potentials can be identified and the influence of user behavior on energy consumption analyzed.
|Effective start/end date||1/03/10 → 30/04/12|
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