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Management of Real-time Energy Data

Project description

Developing a platform for better use of data

The development and evolution of the Internet of Things (IoT) devices and sensors has resulted in them starting to play an important role in most fields of industry. They generate huge amounts of data that are used mainly for monitoring, but could provide many more benefits and advantages. Unfortunately, those benefits remain largely untapped, as there has not been enough innovation in the field to allow their efficient use. The EU-funded MORE project aims to change this by allowing industries to tap into the many advantages that better use of IoT data could provide. To achieve this, MORE will develop a platform, utilising advanced analytics, edge and cloud computing and modelling techniques for sensor data that allows more accurate predictions and better diagnostic models.


The widespread use of sensor and IoT devices is generating huge volumes of time series data in various industries like finance, energy, factories, medicine, manufacturing and others. Industries use these data for monitoring, but their main potential is still untapped. Existing techniques and software for time series management do not provide tools sufficiently scalable and sophisticated for managing the huge volumes of data or adequate forecasting, prediction and diagnostics.

MORE will create a platform that will address the technical challenges in time series and stream management, focusing on the RES industry. MORE’s platform will introduce an architecture that combines edge computing and cloud computing to be able to guarantee both responsiveness and provide sophisticated analytics simultaneously. This architecture will be combined with the usage of time series summarization techniques, or as we more accurately term them in MORE, modelling techniques for sensor data. Models are any compressed representations that allow the reconstruction of the original data points of a time series (e.g. a linear function) within a known error-bound (possibly zero). This approach has synergies with the edge computing approach, since summarization can be done at the edge, reducing the load in the whole data processing pipeline.

MORE will introduce advanced analytics tools for prediction, forecasting and diagnostics based on two technological directions: machine learning and pattern extraction, with emphasis to motifs, which is the state-of-the-art for time series. MORE will adjust these techniques to work directly on models of data, thus enabling them to scale beyond state-of-the-art. The ability to ingest huge volumes of data will have an important impact to the accuracy of the prediction and diagnostics models.

Call for proposal


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Sub call



Net EU contribution
€ 886 875,00
Artemidos 6 kai epidavrou
151 25 Maroussi

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Αττική Aττική Βόρειος Τομέας Αθηνών
Activity type
Research Organisations
Other funding
€ 0,00

Participants (6)