Therefore, we made our paper presentation in the form of a video:
In her talk, Hafsa Bousbiat describes how abnormal behavior can be detected among common household devices using Non-Intrusive Load Monitoring. The need for reducing our energy consumption footprint and the increasing number of electric devices in today’s homes is calling for new solutions that allow users to efficiently manage their energy consumption. Real-time feedback at device level would be of significant benefit for this application. In addition, the aging population and their wish to be more autonomous have motivated the use of this same real-time data to indirectly monitor the household’s occupants for their safety. By breaking down aggregate power consumption into appliance level consumption, Non-Intrusive Load Monitoring allows for reducing the energy consumption footprint and has the potential to indirectly monitor the elderly and help them to fulfil their wish to be more autonomous in a secure manner. Therefore, the work aims to depict an architecture supporting non-intrusive measurement with a smart electricity meter and the handling of these data using an open-source platform that allows us to visualize and process real-time data about the total consumed energy. The proposed architecture is depicted in the figure below.
Proposed architecture for integrating an AAL with an energy monitoring system
More details about our work can be found in the full version of our paper here.
With the development and introduction of smart metering, the energy information from costumers changes from infrequent manual meter readings to fine-grained energy consumption data. On the one hand, these measurements will lead to an improvement in costumers’ energy habits, but on the other hand, the fine-grained data produces information about a household and households’ inhabitants, which give rise to privacy issues because these monitoring results disclose user behavior which could be extracted by smart algorithms and techniques. The loss of privacy by load disaggregation and data mining is a huge upcoming smart grid and social issue which enforces the need for privacy-preserving techniques, which can be divided into the following three possibilities:
Anonymization of metering data: The metering data and customer identity are separated by a third-party id
Privacy-preserving metering data aggregation: Metering data is geographically encapsulated by aggregating the metering data of co-located consumers
Masking and obfuscation of metering data: Masking the power demand by adding or withdrawing the to the meter visible energy demand with the help of rechargeable batteries or controllable loads.
a state-of-the-art battery-based load hiding (BLH) technique, which uses a controllable battery to disguise the power consumption and a novel load hiding technique called load-based load hiding (LLH) are presented and compared. A load-based load hiding system controls appliances in a specific way to obfuscate a household’s power demand. For example, an electric water boiler could be instrumented to consume energy in a way that masks the power consumption of smaller household devices like coffee machines or a TV. There is no comfort loss expected for the customer: Overall, the boiler will consume a typical amount of energy and produce the expected amount of hot water. Using this approach, however, reduces the predictability of your energy consumption, which is good for privacy, but a disadvantage for grid operators.