Blend in data analytics with your renewable energy mix
CLEAN ENERGY WINNERS WILL BE ADOPTING DIGITALIZATION IN THEIR OPERATIONS
- Do you need to cope with the rapidly expanding demand for clean energy?
- Are you trying to integrate new technologies in a sector where changes are still occurring at a dramatic pace?
- How do you make sure your team receives the right information about your asset?
Solar, geothermal, tidal power. FieldBox.ai is the solution for fully embracing the energy revolution.
The advent of intermittent energy sources such as wind and solar are shaping a new energy landscape where renewable energy is combined with fossil-fuel generation. Smart grid provide increased flexibility for managing a distributed production system where consumers can turn into producers.
In order to do that, lots of data are necessary, including from the edge of the network. Sensors that are common in the transmission grid are now being deployed at the user level, where they may help utilities deal with a host of challenges, including balancing system reserves and incorporating power from self-generators, such as rooftop solar owners.
In this context, forecasting production is key. Machine learning algorithms are a perfect fit for analysing the data collected in the grid and from key equipments, and contextual data such as weather or consumption level. Predictive models are then trained in the FieldBox using these multivariate datasets.
REVEAL THE TRUE POTENTIAL OF NEW
ENERGY SOURCES THROUGH DATA
CASE STUDY : INCREASING THE SAFETY OF INFRASTRUCTURE WITH VIRTUAL METERING
FieldBox.ai provides a digital solution for monitoring strain in penstocks. GPS and other sensors installed on site are connected to the platform. Artificial Intelligence algorithms are used to find correlations between variables and model penstock behavior. With this model, Alperia improves the planning of maintenance operations and optimizes hardware expenses deployed in the mountain to monitor penstocks.
Virtual Metering infrastructure AI Agent is able to accurately identify when an infrastructure is in an abnormal situation. Thanks to this, our solution is able to identify the breakdown in advance and deploy maintenance to avoid the problem.