Our Client had:

Traditional approaches to anomaly detection in grid networks are operator based in a grid opartaing centre. Operators use collected near real time metrics from the grid to asses and antipicate anamolies. The Operator must decide based on experience which mitigating measure to use and lacks the ability to simulate scenarios in real time.

Our Solution:

Hivemind implemented a near real time anamoly solution based on Apache Kafka and Apache Spark, processing 3,6 m data points per sec to determine anamolies in the current and voltage data porints and fingerprint anamalous data sets for simualtion of a downstream proedictive systems.

Our Client Needed:

Implement an anamoly detection service with fingerprinting in energy grid networks.
 

Our Client Needed:

Due to the success of their CBIS platform, management wanted to see new reporting and API's. Therefore, the platform must now be always available and reliable to serve these new requests. Anomolies and problems from the source systems must be identified quickly and data must be up-to-date at all times.

Our Solution:

Hivemind has supported PAYONE in the design, operation and implementation of the CBIS platform. Its goal is to unify the different source systems they have gathered through various mergers into a uniform reporting tool with a central data model. To achieve this we have introduced architectures for near real time reporting and streaming as well as cloud services. By using elastic and scalable servies in public clouds we have also implemented cost reduction solutions. The PAYONE team has also been trained on the new architecture and technologies in order to independently maintain the platform.

Our Client Needed:

The main obstacle for EV adoption is range anxiety and the availability of charge points. The charge point market is heterogenous and consist of over 700 providers in Germany alone. The quality of service and the availability greatly differ thus hampering the experience for EV drivers. Elvah imtroduced the scoring platform that gives users the ability to determine the quality and availbaility of charge points from their app.

Our Solution:

Hivemind implemented a scoring solution for Elvah that uses real time and historic session data for all charge points in central europe usinhg machine learning based predictions to allow users to dertmine the availaibility and quality of charge points at any given time. Hivemind implemented the training models and an MLOps delivery pipeline and a RESt endpoint for serving near rela time proedictions using Apache Spark, Spark ML and microservices running in Kubernetes in AWS

Why choose Hivemind?

Benefit from the longstanding experience of our senior development team.

We believe in sharing knowledge to empower your own teams.

We always pursue a best-practice approach, striving to create things as simple and efficient as possible.