Energy & Resources
Renewable Energy
Discover site-specific performance laws, degradation kinetics, and capacity fade equations for wind, solar, and battery assets.
One of 34 industries across 8 sectors served by CDE — the Causal Dynamics Engine for Science and Engineering.
The Challenge
Why Renewable Energy teams still struggle to explain what is happening
Wind farms, solar installations, and battery storage systems generate continuous operational data through SCADA systems, irradiance sensors, and battery management units. This data encodes the governing physics of energy conversion, degradation, and storage, yet operators typically rely on manufacturer-supplied performance models and generic degradation assumptions that miss site-specific conditions. The disconnect between standardized models and real-world environments leads to systematic errors in performance forecasting, suboptimal maintenance scheduling, and mispriced energy contracts.
Current approaches treat performance deviations as residuals around manufacturer curves, obscuring the physical causes of underperformance. Wind turbine power curves shift with blade erosion, icing, and yaw misalignment in ways generic models cannot distinguish. Solar degradation follows site-specific kinetics driven by local humidity, temperature cycling, and soiling patterns that differ substantially from laboratory accelerated-aging tests. Battery cycling data exhibits complex path-dependent degradation where calendar aging, depth-of-discharge patterns, and thermal history interact nonlinearly.
The CDE Approach
How CDE closes the explanation gap in renewable energy
CDE takes raw SCADA telemetry, inverter logs, and battery management data, then discovers the site-specific governing equations that determine actual performance. Instead of fitting against manufacturer baselines, it identifies closed-form physical relationships (power conversion laws, degradation kinetics, capacity fade equations) that emerge from each installation's unique conditions. Conservation law detection flags energy balance violations signaling equipment faults, wiring losses, or sensor drift before they compound into material production shortfalls.
Causal mode separates environmental effects from equipment degradation, letting maintenance teams identify which losses are addressable through intervention and which reflect inherent site conditions. Regime classification detects operating state transitions (partial wake effects, inverter clipping, battery thermal management mode changes) that standard dashboards miss. The Evidence Ledger provides deterministic replay and provenance for warranty claims, insurance adjustments, and investor reporting.
Discovery Engine
How CDE applies here
Symbolic discovery produces closed-form power curves, degradation rate laws, and capacity fade equations that asset managers can audit and incorporate into financial models. Causal mode reveals whether underperformance stems from wake interactions, soiling, component wear, or grid curtailment. For battery storage, Neuro-Symbolic mode captures complex path-dependent degradation through the engine's internal methods, then applies structure extraction to yield interpretable equations suitable for long-term capacity planning and contract structuring.
Causal Graphs
Discovers directed causal structure from observational data — identifiable causal graphs, regime classifications, and intervention predictions.
Governing Equations
Extracts compact governing laws grounded in the causal structure — interpretable equations your team can read, verify, and compare against known theory.
Intervention Design
Proposes targeted experiments to resolve ambiguous causal edges — maximizing information gain where the causal structure is still uncertain.
Causal Validation
Negative controls, falsification tests, and identifiability analysis applied to every causal claim before promotion to the evidence ledger.
Typed Scientific Claims
What CDE discovers
Every discovery CDE produces is a typed scientific claim — not a black-box prediction, but a governed, reproducible, auditable piece of scientific knowledge with full provenance.
- Power curve governing equations
- Degradation rate laws
- Energy balance conservation validation
- Weather-performance causal models
- Battery cycling dynamics
Governed Discovery
Make the finding reviewable
Every discovery CDE produces carries the review context around it: a Truth Dial setting, an evidence entry with replay context, and control results including bootstrap stability, out-of-distribution testing, and feature-shuffle validation.
For renewable energy, that means teams can compare runs, justify decisions, and decide whether a finding is ready for internal use, external review, or regulated submission.
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Put CDE on a real renewable energy problem
Whether you are exploring renewable energy data for the first time or scaling an existing research programme, CDE adapts to your workflow. Bring the dataset, the decision pressure, and the constraints. We will map the right discovery path.