The InterConnect project gathers 50 European entities to develop and demonstrate advanced solutions for connecting and converging digital homes and buildings with the electricity sector. Machine Learning (ML) algorithms play a significant role in the InterConnect project. Most prominent are the services that do some kind of forecasting like predicting energy consumption for (Smart) devices and households in general. The SAREF ontology allows us to standardize input formats for common ML approaches and that explainability can be increased by selecting algorithms that inherently have these features (e.g. Decision Trees) and by using interactive web environments like Jupyter Notebooks a convenient solution for users is created where step by step the algorithmic procedures can be followed and visualized and forms an implementation example for explainable AI.
[This post is based on the Bachelor Information Sciences project of Darin Pavlov and reuses text from his thesis. The research is part of VU’s effort in the InterConnect project and was supervised by Roderick van der Weerdt]
The concepts and technologies behind the Internet of Things (IoT) make it possible to establish networks of interconnected smart devices. Such networks can produce large volumes of data transmitted through sensors and actuators. Machine Learning can play a key role in processing this data towards several use cases in specific domains automotive, healthcare, manufacturing, etc. However, access to data for developing and testing Machine Learning is often hindered due to sensitivity of data, privacy issues etc.
One solution for this problem is to use synthetic data, resembling as much as possible real data. In his study, Darin Pavlov conducted a set of experiments, investigating the effectiveness of synthetic IoT data generation by three different tools:
Darin compared the tools on various distinguishability metrics. He observed that Mostly AI outperforms the other two generators, although Gretel.ai shows similar satisfactory results on the statistical metrics. The output of SDV on the other hand is poor on all metrics. Through this study we aim to encourage future research within the quickly developing area of synthetic data generation in the context of IoT technology.
More details can be found in Darin’s thesis.
On 1 October 2019, the Horizon2020 Interconnect project has started. The goal of this huge and ambitious project is to achieve a relevant milestone in the democratization of efficient energy management, through a flexible and interoperable ecosystem where distributed energy resources can be soundly integrated with effective benefits to end-users.
To this end, its 51 partners (!) will develop an interoperable IOT and smart-grid infrastructure, based on Semantic technologies, that includes various end-user services. The results will be validated using 7 pilots in EU member states, including one in the Netherlands with 200 appartments.
The role of VU is to develop in close collaboration with TNO extend and validating the SAREF ontology for IOT as well as and other relevant ontologies. VU will lead a task on developing Machine Learning solutions on Knowledge graphs and extend the solutions towards usable middle layers for User-centric ML services in the pilots, specifically in the aforementioned Dutch pilot, where VU will collaborate with TNO and VolkerWessel iCity and Hyrde.