Spotfire® for Energy & Utilities

Visual data science for energy

Grid load forecasting

As renewable energy comes online, forecasting grid load and generation becomes exponentially more complex due to weather variability and distributed assets.

Spotfire enables visual, real-time exploration of historical consumption, weather data, and generation patterns. Using built-in predictive models and integration with Python/R, operators can forecast demand and renewable supply, identify peak load risks, and simulate “what-if” grid scenarios.

Asset health & maintenance optimization

Utility-scale energy systems, like transformers, turbines, and substations, require proactive maintenance to prevent costly failures. But without a unified view of asset health, maintenance is often reactive or overly scheduled. With Spotfire, you can visualize and analyze asset health KPIs, failure trends, and service logs. Combine real-time condition analysis with historical data to optimize maintenance schedules.

Spotfire integrates advanced analytics, including AI and ML, to predict failures and optimize maintenance actions. Predictive models based on historical and sensor data enable early detection of asset degradation and support data-driven maintenance planning. Native support for Python, R, and data science workflows allows seamless model integration within interactive dashboards.

AI-driven peer-to-peer trading

The rise of distributed energy resources (DERs) and prosumers is decentralizing the energy landscape, challenging traditional utility roles. How can utilities maintain relevance and leadership in an ecosystem where consumers also generate energy?

Spotfire empowers utilities to become orchestrators of peer-to-peer (P2P) energy trading networks. By analyzing real-time data from prosumer devices, consumption and generation profiles, market prices, and weather forecasts, utilities can gain full visibility into energy flows.