We are specialists in the practical application of Artificial Intelligence including Process Optimisation AI, Machine Learning, Deep Learning and Data Science Software used within Wind Power processes and operations

AI can be fed complex historical operational data. It can then use this to quickly identify processes and steps that can be improved.

The power of process optimisation

Being able to optimise processes is a powerful tool in all aspects of business and industry operations.

By learning from past operations and processes, including positive events, negative events, and all the in-between variable and unpredictable events that happened before; processes can be optimised using a more meaningful, targeted, knowledge-led and modelled approach.

Crucially, a faster, accurate process optimisation process saves time, cost and other resources in the future.

The more past data, experience and process events to learn from the more insightful and informed we can base our process optimisation on.

AI makes automatic process optimisation possible, even in the most diverse and complex of processes

Learning from past events to improve a process might sound straightforward - it is of course exactly what a human brain does - but making this process automatic and asking a machine to do this is extremely complex and demanding, simply due to the huge amount of variation involved.

If a traditional software programming approach was used, the computer would continually be asking "if this, then do that; if x and y, do z". This stepwise methodology is extremely limited and tedious as the program will only do what it has explicitly been told to look for by the programmer; making it next to impossible to account for all possibilities and variations, even in the simplest of processes. You certainly couldn't use this approach for complex processes and operations.

With AI and Machine Learning technology it is now possible to learn from huge amounts of variable, constantly changing data and past events in order to carry out much more accurate and targeted process optimisation.

ELDR Process is a powerful Deep Learning package that can help optimise processes based on variable past and complex data it has learnt from

ELDR Process is a variant of Fennaio's core AI predictive analytics software - ELDR Predictor. Using our powerful ELDR AI Engine, which is a Deep Learning Artificial Neural Network, ELDR Process uses both Supervised and Unsupervised learning to make and link complex relationships from data (of any size or complexity) passed to it.

When ELDR has learnt (trained) from the process data, it is ready to accept real time process data to respond to.

ELDR Process is highly dynamic, autonomous, configurable, graphical and easily integrated

Dynamic

ELDR Process can handle and learn from multiple sources, sizes and complexities of data for numerous prediction requirements simultaneously. Data can be changed at any time and it can continually learn. The software can carry out supervised and unsupervised learning.

Autonomous

By default ELDR is plug and play - you can simply give it appropriately formatted data and it will automatically learn from it, including self optimisation and self scaling.

Configurable

In many cases you may be happy with plug and play, however almost everything in ELDR is configurable; from which data fields to use as inputs and outputs, to colours, displays, output format, learning modes, learning accuracy, all the way through to Artificial Neural Network dynamics and dimensions.

Graphical

ELDR Process uses a rich intuitive GUI Dashboard from which to manage the whole AI process (data preparation, learning, outputting and testing), including a comprehensive suite of gamified charts and other visual displays to monitor everything.

Easily Integrated

AI Integration is our speciality. We understand that AI can be used in a variety of ways and in numerous system-types and processes. We build our software to be entirely modular and there are multiple integration methods and points ranging from network-based RESTFulAPI integration to direct coupling at the code level, depending on the response time required, amongst other considerations.