It is the first 'all-in-one' solution for all the stakeholders of the renewable energy industry, the connection point between manufacturers, installers, operators, investors and owners.
It is a fact, Renewable Energy is the future.
In recent years, renewable energy resources such as solar and wind generation have outgrown the 'alternative' label.View details »
Two most important tasks for engineers in the future are the conservation of resources and the environment.
Costs of new energy technologies are falling in a way that it’s more a matter of when than if.View details »
We apply state of the art technology such as machine learning techniques, data science libraries and algorithms in order to provide a 'smart' solutions for our clients to help them towards the direction of 'Paperless office'View details »
This module generates automatically technical reports after the corrective or preventive maintenance of the photovoltaic plants or wind installation. No more hard copy documents such as checklists, protocols etc. denergea platform is designed with focus to get rid of all the outdated technologies.
Predictive models for photovoltaic plants and wind farms are vital if you consider that a component which might cost 5€ can cause losses of hundreds of thousands. Therefore we inform our users before such an error occurs. We test and validate our model to best predict the probability of an outcome. We use data technology, statistics and math to predict such errors.
Our risk evaluation framework, a set of algorithms and equations, calculates for each one of your investments an existing risk and proposes mitigation measures. Our investors-customers say that it helps them a lot to decide whether to take an action or not and for our operators-customers to convince the owners about the mitigation measure to be taken.
Our dynamic algorithms are taking into consideration all the characteristics of each task and generate two parameters for each one of them, a value and a weight. Afterwards a set of dynamic sorting techniques are applied to calculate the optimum scheduling of the tasks.
It is a machine learning technique which categorizes the photos taken from the user according to a set of rules and layers. The goal is to help the users on-site 'recording the data and identifying a failure.
It is a module that helps the engineers to learn about the failures that occur in photovoltaic plants or wind turbines. This module is very helpful for new employees, as it allows them to become productive in a very short time. This module uses a learning technique that incorporates increasing intervals of time between subsequent reviews of previously learned material.