After a detailed study of end user requirements and the respective specifications, the project generated an integrated software prototype with a user-friendly web interface that can collect data (power generation and consumption data, meteorological data) and provide a) continuous load forecasts b) personalized load management and c) renewable energy sources power generation forecasts. The system as a whole as well as its sub-systems were thoroughly evaluated with respect to their functionality and results. Moreover, novel deep learning methods were introduced and some of them were incorporated in the prototype software. More specifically, novel methods were developed for short term and day ahead electric load demand forecasting as well as wind energy generation day ahead forecasting. The methods utilized diverse approaches such as tree-based ensembles, lightweight neural networks, anchored input-output learning, online self distillation, residual error learning etc. The research outcomes of the project were disseminated through in 11 scientific conference papers.

The project has been Co‐financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: Τ2EDK-03048).