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Smart Energy Automation System - advanced artificial intelligence and predictive systems for energy optimization and savings in all buildings / infrastructures

Periodic Reporting for period 1 - SEAS (Smart Energy Automation System - advanced artificial intelligence and predictive systems for energy optimization and savings in all buildings / infrastructures)

Reporting period: 2018-08-01 to 2018-11-30

Energy is a key element of the European Union’s economy. The EU consumes 12% of global energy, translated to 1,629 Mtoe in 2015 and it is the biggest energy importer worldwide.
Energy efficiency has huge economic, social, and environmental impact, thus it is one of the cornerstones of EU Energy Policy and it is closely linked to its three main pillars: security, sustainability and competitiveness. In this context, the EU published in 2012 an Energy Efficiency Directive that established a set of binding measures to help Europe reach its 20% energy efficiency target by 2020. This target was revised in 2016 including a new 30% energy efficiency target for 2030. However, the implementation of the 2012 Directive is behind schedule and the 2020 target saving is likely to be missed at the EU level.
Akse delivers energy management and automation solutions supporting industrial, commercial and residential users to achieve their energy efficiency goals. The environmental benefits of AKSE solution are obvious; citizens, enterprises and industries will be able to reduce their energy consumption, use more efficiently renewable energy sources, and optimize energy-related operations, thus eliminating their carbon footprint.
Akse will face next steps in order to reach TRL 9 with the aim of successful SEAS commercialization.
Till today, Akse have invested on SEAS prototype development. The prototype has been tested with pilot customers, demonstrating its capabilities in energy management and automation. However, the goal is to provide artificial intelligence characteristics to SEAS to optimize further the energy consumption of buildings and other types of facilities (e.g. plants) together with their operational processes.
The current energy management systems have limitations in terms of customization, integration capabilities with 3rd party systems, energy automation and predictive maintenance. In addition, they are prone to security attacks, making the whole smart grid vulnerable. SEAS is built by open protocols enabling its customization and integration with 3rd party systems. In addition, it leverages artificial intelligence and machine learning technologies to proactively identify energy savings opportunities and automatically take real-time actions to help the end users to reduce their energy costs and carbon emissions and optimize the operations of their facilities. According to our estimations SEAS will result in 10-30% energy savings depending on the use case served.
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