Energy Industry Weather Solutions
ClimatePulse can help prevent 40,000 customer outages by using predictive weather analytics to identify and address grid vulnerabilities before storms hit.
The efficiency of solar power generation depends on cloud cover, sunlight hours, and irradiance levels. ClimatePulse provides solar developers and utilities with precise weather forecasts that predict cloud cover and solar irradiance, allowing them to better estimate the output of solar panels. This data is integrated with SCADA systems and grid balancing tools, enabling efficient solar energy integration into the grid. For instance, Summit Power used satellite-based cloud data to adjust solar generation plans, increasing energy reliability during overcast periods and reducing fuel penalties from over-reliance on backup generators.
A recent study showed that AI-enhanced demand forecasting improved Mean Absolute Percentage Error (MAPE) by 2.3% for daily load prediction at Summit Power. This improvement helped mitigate blackout risks during peak demand periods,especially when air conditioning load surged during heatwaves,while also reducing unnecessary standby fuel burn.
At a broader level, AI-supported forecasting has been instrumental in optimizing renewable integration by avoiding overestimates in solar and wind output, and adapting hydropower operations through weather-based river flow predictions.
Example: ClimatePulse can provide cloud cover and solar irradiance forecasts that companies like Summit Power may integrate into daily dispatch models. This can help reduce mismatches between solar generation and real-time demand while improving fuel efficiency.

Use Cases:

Cloud Cover Predictions: Solar energy producers can use high-resolution forecasts to anticipate cloudy conditions and adjust short-term energy production estimates,contributing to better coordination with the grid and reduced reliance on fossil backups.

Grid Balancing: Real-time weather and solar irradiance forecasting helps utilities balance power inflow, reducing stress on infrastructure. This approach helped Eversource Energy prevent 40,000 customer outages by using predictive analytics to forecast grid vulnerabilities before storms.

Solar Power Optimization: Forecasting solar irradiance enables better planning for distributed generation. This has helped reduce overestimation of green energy output and avoid curtailments. Additionally, AI-aided collaboration between meteorologists and grid engineers at Summit supported smarter scheduling of hydro and solar together.

Storm Preparedness & Recovery: By using AI-driven weather predictions, utilities like Eversource were able to reduce storm restoration time by 12 hours and significantly cut down on customer-minutes of interruption, offering a model for Bangladesh to build localized storm-response playbooks.