Overall concept underpinning the project
In an ideal world, it would be desirable to have a single platform that fits perfectly the necessities for all the stakeholders of the energy sector. However, the reality is that it is almost impossible to implement such an approach in real life. In fact, most of the stakeholders already have their own platform (usually more than one) that specifically meet their business requirements. Installing a new platform will require time and money that companies are not willing to spend. On the other hand, having a single platform requires the introduction of a third-party company that is responsible for managing the platform and that will incur in further costs. Also, companies continuously manifest that they do not want to rely on a single platform provider (the so-called vendor lock-in) but they want to have control over their platform and be able to change the platform provider if necessary. Therefore, PLATOON introduces a flexible and modular lightweight reference architecture that will set the framework so the different platforms from all the different stakeholders can share data with each other, analyse it and gain knowledge from it. In this sense, the reference architecture includes two key elements:
- Interoperability layer based on open APIs and open data models based on existing standards that will enable the effective communication amongst different platforms.
- Data governance scheme based on IDS that will ensure that the data is shared and utilized according to the specific agreements signed by the different stakeholders.
Also, and above all, the reference architecture framework will set the necessary cybersecurity requirements to ensure that system is immune against malicious attacks.
Finally, as a result of the project, PLATOON will create an analytical toolbox formed by a list of modular analytical tools that can easily be integrated into specific platforms for specific applications. These analytical tools can be grouped into two categories: 1) Generic Big Data tools, such as data ingestion and integration tools, visualisation tools, etc. and 2) Energy specific tools for application in the energy sector, such as, demand forecast tools, energy usage optimisation tools, predictive maintenance tools, etc.