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. read more of AI4CZC model testing scenarios
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... read more of AI4Hydro II
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... read more of Montenegro net position forecast model
Datasets provided are used to train ML models for forecasting electricity production on hourly basis.
Developed by Vodena doo for the GRIDouble project, part of the I-NERGY 2nd Open Call. read more of Solar energy production dataset
The Load Forecast Model is distinguished by its precision in predicting energy load demands, owing to its integration of advanced machine learning algorithms. Its cloud-hosted nature ensures scalability and adaptability, catering to both centralized and e... read more of Load Forecast Model and User Manual
The Solar Production Forecast Model is a cutting-edge tool that leverages machine learning to predict solar energy output, helping entities maximize the benefits of solar energy and streamline their consumption. read more of Solar Production Forecast Model