EEI Service Deployment
Service Docker deployment accompanied by a Postgresql/PostGIS database.
AI is expected to dramatically reshape the energy sector. This section provides references on on-going projects and existing results in that area.
Service Docker deployment accompanied by a Postgresql/PostGIS database.
A Dataset with Energy Efficiency Measures available for renovations provided by REA (Riga Energy Agency)
A physics-informed deep neural network developed to predict the energy consumption of a buildings.
A forecasting service for predicting the positive active energy (in kWh) consumption of prosumers in the Italian city of Terni. The dataset was provided by ASM and the service makes use of a LightGBM model. (AIExperiments Asset)
A global forecasting service for predicting the aggregated hourly net electrical load of 20 European transmission system operators (Belgium, Czech Republic, Denmark, Estonia, Estonia, Finland, France, Greece, Hungary, Italy, Latvia, Lithuania, the Netherl...
The service is based on the Energy Performance Certificates XML database from Asturias region (in the North of Spain), and it checks data from different parameters in the XML (either an uploaded file or selecting one from the database) according to differ...
The service supports users in the definition of the Measurement and Verification Plan following the instructions provided in the International Performance Measurement and Verification Protocol (IPMVP). It is divided into two parts, one to define the prope...
We present here three scenarios of 24h that we used to test the AI4CZC model. These scenarios include an usual day scenario, a scenario where the Montenegro thermal plant is down, and a scenario were the cable between Montenegro and Italy is down.
LexaTexer provides an Enterprise AI platform to support the energy value chain with prebuilt, configurable AI applications addressing CAPEX intense hydro assets like Pelton and Francis turbines and pumps. In this project we combine our Enterprise AI platf...
This dataset presents an estimation of the emissions by kWh of CO2 equivalent of the generation types of Montenegro, Serbia, Bosnia, Italy Center-South and Kosovo
The service provides the total PV production per year for a defined site and parametrised fields, with comparative graphs between current production (based on historical weather data) and the future (short-term) production considering climate change scena...
In the context of AI4CZC project, this model forecasts the Montenegro net position (difference between generation and load) for the next 48h. The model was trained with data from 2019 to 2021 and tested on 2022 data. It performs with a MAE of 75.8 and a R...