We understand the needs of the power and energy industries in terms of reliability, security, scalability, and observability. We deliver solutions grounded in solid science, following best practices for software development.
Our solutions will help you transform your digital energy capabilities. Here are some examples:
- Fault identification and predictive maintenance: increase uptime and reduce maintenance and spare parts cost for renewable energy assets
- Estimation and activation of flexibility: manage local grid congestions
- Forecasting: high temporal and spatial resolutions support local energy management, networks, and market operations
- Processing of time series: pre-processing, storage, analytics, and forecasting
- Graph machine learning: reconstruct faulty data of PV production and forecast future generation values
- Data-driven models: technical systems, such as complete buildings or wind turbines, including linear state-space models with non-linear regressors and architectures based on recurrent neural networks, such as long short-term memory (LSTM) cells, encoder-decoder architectures, and transformers
- Data collection architectures: based on a mixture of commercial solutions and custom developments, such as the highly available instrumentation of tens of radiator valves in operational buildings for state estimation and optimal control
- Customized numerical algorithms: improve the applicability, scalability, and robustness of demanding computations. Applications include the training of data-driven models and the resolution of optimization problems, such as our patent-pending solution for optimal control of PV inverters or the aggregation and dispatch of flexibility provided by buildings.
