A data analyst working within a utility or energy infrastructure company must correlate environmental factors with operational performance. However, the datasets available to them are coarse, inconsistent, and fail to capture site-level conditions. This lack of granularity limits the predictive accuracy of performance models, particularly those used to optimize cooling, maintenance scheduling, or grid balancing under extreme heat.
FortyGuard's Solution
FortyGuard’s Temperature API (starting at $79/month), part of our detailed heat analytics platform, provides analysts with consistent, structured datasets measured at 2 meters above the ground with 10 m² spatial resolution. These datasets can be integrated directly into analytical models, AI tools, and enterprise data lakes. By combining operational metrics with hyperlocal temperature inputs, analysts can uncover correlations that improve system reliability, asset performance, and energy efficiency.
Value Creation
Cost Optimization – Improves forecasting precision, reducing inefficiencies in asset utilization and maintenance planning.
Commercial Uplift – Enables data-backed infrastructure investments that align with resilience goals.
Enablement – Provides a scalable, standardized data foundation for predictive analytics and AI applications.
More Energy Case Studies
Energy
Plant / HSE Managers
A plant manager in a refinery or energy facility must ensure worker safety and equipment reliability during peak heat conditions. Existing safety systems monitor temperature inside controlled areas but neglect outdoor heat exposure along pipelines, tanks, and field work...
A smart grid manager must balance power loads across a distributed network during heatwaves that drive unpredictable surges in demand. Conventional grid management systems lack localized temperature inputs, resulting in reactive load redistribution and inefficient demand...
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