Develop a smart Deep Learning model (AI, ML, NN) to analyze thousands of energy sources in a Smart city / Municipality (public lighting, buildings energy consumption, use of external data sources, degree days, Temps, local weather conditions, demographics, geospatial data, financial data, utility bills, etc) and create specific KPIs (quantitative, qualitative) in order to model, predict and detect anomalies and outliers (energy, C02, etc) so that to propose automatically specific solutions (storage, RES, Eco mobility, DERs) to covert this City into a Net-Zero City           The model will be applied to one Smart City in Greece (we have data) and then after the supervised learning process and the finalization, the model will be able to be applied to ALL Cities in the World, in order to convert them into Net-Zero Cities     This applies to UNSDG 11   We will also apply this model to our customers (Smart Cities) as one of the leading Utilities in Greece                                                           Team Name PIL Track Data for Good Use Case Smart City decision support system to self-adapt and propose Net-zero driven solutions, based on various large data inputs Technology Platform-based offering, Python Algos, AI approach, Data analysis, Agents, Supervised Learning Region Europe, Smart Cities, Municipalities Team lead Dr. Vassilis Nikolopoulos @Vnikolop Team members Nikos Babis, Dimitris Karpodinis (Data Scientists)   Smart City Assets ... View more