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Big data for renewable generation

Big data for renewable generation

Melis, Wim J.C. (2017) Big data for renewable generation. In: 5th UNI-SET Energy Clustering Event - Universities in the Energy Transition: Science & Skills for Renewables Integration, 31st May - 2nd June 2017, KU Leuven, Belgium.

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Abstract

Data is knowledge, but that requires one to turn it into information. For quite a few years our university has invested in installing various renewable generation technologies. Consequently, there are various types of Photo Voltaic (PV) panels, and more recently also plans to install a Photo Voltaic Thermal installation. Additionally, a Combined Heat and Power plant running on bio-fuel is soon to be switched on. Considering that most of these installations are part of our facility management and can therefore not be used for “life” research purpose, the aim is always to install them with suitable data gathering technologies to collect as much data as possible. This has e.g. meant that various years of data have already been gathered about the various PV panels, and the same will applies to all new installations. More recently a project has looked at bringing all this data together in one location with also data from local weather stations to allow for research to be performed on these types of generation and how the generation is e.g. linked with weather patterns, can match demand at those points in time and so on. The purpose is to not only make this data available internally, but also ensure there is open access where-ever possible so that others can also build on it.

Item Type: Conference or Conference Paper (Lecture)
Uncontrolled Keywords: Big Data, Renewable Energy
Subjects: T Technology > TD Environmental technology. Sanitary engineering
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Department of Engineering Science
Faculty of Engineering & Science > Future Technology and the Internet of Things
Last Modified: 29 Jun 2017 14:09
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/17429

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