Causal AI For Energy & Utilities
Energy transition, price volatility, and intensified competition are among the many challenges facing this sector. Utilising Causal AI in energy and utilities enables providers to augment their decision-making processes, and helps them to navigate a sector in flux.
Our clients’ top concerns in energy
- Shifting consumer preferences and investor sentiments
- Keeping pace with technological innovation
- Anticipating commodities cycles
- AI systems that cannot be trusted for significant decisions
Solutions
The growth of PEVs represents one of the most important trends for the future of the grid. In contrast to current stationary loads, PEVs are mobile loads that may appear at any charging point, which many current grid and load forecasting methods are unable to model. Causal AI learns robust models that factor in emerging trends and new data. Increased explainability promotes collaboration to improve capital investment planning, and builds trust with all stakeholders.
As solar and wind generate more of the world’s electricity, utilities will have to manage intermittent and highly variable supply. Correlation-based machine learning adapts poorly to this new energy mix. Without accurate forecasts, risks for power outages and surplus surges are multiplied. Causal AI works with small data and new regimes to optimize renewable energy ROI.
Efficient predictive maintenance can only be achieved with root cause analysis. Existing correlation-based predictive maintenance models often fail to distinguish between cause-and-effect relationships and spurious correlations. At the same time they fail to adapt to changing conditions in the environment. With Causal AI energy providers can autonomously discover root causes in real time, enabling optimal maintenance of their systems avoiding costly downtime.
Current machine learning approaches are unable to accurately model the complexity of the energy market. Spurious correlations undermine their insights and predictions. We can cut through the enormous number of potentially relevant variables and identify the true drivers of commodity prices. With a track record of success in some of the world’s leading hedge funds and banks, causaLens allows energy traders to minimize execution costs and optimize trading outcomes.
