PLAIXUS
AI is expected to dramatically reshape the energy sector. This section provides references on on-going projects and existing results in that area.
The project SuperPower 2.0 developed thanks to I-NERGY open calls has developed an AI tool for automatic detection of broken insulators using a YOLO architecture. The end-goal of the developed system is to be able to provide end clients (power line own...
The projects SuperPower and SuperPower 2.0 developed thanks to I-NERGY open calls has developed super-resolution algorithms to increment the pixels in thermal and RGB images using Convolutional Neural Networks. The end-goal of the developed system is t...
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.
GRIDouble is a comprehensive energy management tool that completely automates the finding of optimal patterns in energy consumption and production in the case of facilities with renewable energy sources.
In the energy landscape that DSOs find themselves in today, predicting demand and the generation capacity of the systems to which they distribute becomes essential. This is mainly due to the injection of renewable energy among the consumers of the network...
AI4CZC platform is the Inceptive platform for energy forecasting.
The dataset provides energy consumption readings from a specific device, identified by UUID. The data captures detailed information, including the exact timestamp of the reading, energy consumed, and voltage, among other parameters. All readings are taken...
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...