What to expect in this on-demand recording

Adam Sroka will showcase how a data analytics and machine learning strategy can be leveraged to optimise trading and maximise profitability in the dynamic BESS energy market.

Access the recording below.

Overview

Battery Energy Storage Systems (BESS), are playing a crucial role in ensuring grid stability and facilitating the transition to a cleaner and more sustainable future. However, the effective utilisation of BESS and energy forecasting relies on large volumes of accurate, timely data and the management of a broad range of forecasts from both internal and external sources.

The consolidation, management and analysis of physical data from battery systems, alongside market and environmental data, is often challenging without the right tools and approaches aligned to the unique requirements of BESS and energy trading.

This session will cover:

  • The data challenge for Battery Energy Storage Systems and trading.
  • Designing a data strategy for intelligent forecasting.
  • Model forecast management and machine learning operations (MLOps).
  • Building an internal data & AI capability.
  • Actionable next steps for optimising a data & AI roadmap.

Next Steps

We offer a half-day Performance Energy Trading Data & AI Assessment to optimise energy forecasting.

The workshop will cover:

  • Review of current data & AI architecture, processes and models.
  • Recommendation report for trading optimisation and best practice.
  • Next steps roadmap aligned to trading objectives.
Performance Energy Trading AssessmentListen to the Hypercube Podcast