Domestic smart metering infrastructure and a method for home appliances identification using low‐rate power consumption data

The deployment of domestic smart metering infrastructure in Great Britain provides the opportunity for identification of home appliances utilising non-intrusive load monitoring methods. Identifying the energy consumption of certain home appliances generates useful insights for the energy suppliers and for other bodies with a vested interest in energy consumption. Consequently, the domestic smart metering system, which is an integral part of the smart cities' infrastructure, can also be used for home appliance identification purposes taking into account the limitations of the system. In this article, a step-by-step description on accessing data directly from the domestic Smart Meter via an external Consumer Access Device is described, as well as an easy-to-implement method for identifying commonly used home appliances through their power consumption signals sampled at a rate similar to the rate available by the domestic smart metering system. The experimental results indicate that the combination of time domain with frequency domain features extracted either from the 1D/2D Discrete Fourier Transform or the Discrete Cosine Transform provides improved recognition performance compared to the case where the time domain or the frequency domain features are used separately

Unsupervised river detection in RapidEye-data

International audienceRemote sensing is a widely-used utility in supporting multilateral environmental treaties such as the Water Framework Directive (WFD). Regarding the WFD most remote sensing applications aim on the assessment of the biochemical status of surface water, while the general detection of water networks is disregarded. Therefore, a methodology for the automatic extraction of river networks from multispectral satellite data is presented. Moreover, a new index called RE-NDWI is introduced, which highlights open water bodies in optical remotely sensed data, using the red edge and green band. The river detection method is tested on RapidEye data from three test sites in Germany and clearly outperforms a regular, supervised SVM-classification