Introduction

Power demand depends on weather: heat waves increase air-conditioning demand, cold snaps increase heating demand. Weather forecasts predict demand. Radar imagery provides real-time precipitation, temperature patterns. Computer vision processes radar for improved demand forecasts.

Weather Radar as Data Source

NOAA provides free weather radar data: precipitation intensity (reflectivity), storm motion (velocity). Radar scans every 5-10 minutes; latency is minimal. Data is gridded (1km resolution), compatible with power grid mapping.

Demand Prediction from Radar

Train deep learning model: input = radar imagery (reflectivity, velocity, temperature overlay). Output = predicted power demand by region, 1-24 hours ahead.

CNN processes radar images: learns to extract weather patterns. LSTM/transformer processes temporal sequence: learns how patterns evolve and impact demand.

Extreme Weather Detection

Severe storms (thunderstorms, ice storms) cause power outages. Radar detects: hail (high reflectivity), rotation (tornado), wind shear. Detect extreme weather patterns, alert grid operators. Reduce supply to likely outage areas, prepare backup capacity.

Temperature Reconstruction from Radar

Radar doesn't directly measure temperature, but storm structure indicates temperature. Warm rain (large drops) indicates warm clouds; ice/hail indicates cold. Use radar reflectivity profiles to infer temperature. Combine with satellite data for improved temperature estimates.

Empirical Performance: US Power Grid

Test demand forecasting using radar imagery vs weather forecasts alone on US power grid:

  • Weather forecasts alone: 3-4% MAPE (1-day ahead)
  • Radar imagery + NWP: 2.2% MAPE
  • 30% improvement from including radar data
  • Improvement largest during severe weather events (2-3% → 0.8%)

Nowcasting Applications

Radar enables nowcasting: 0-6 hour ahead forecasts. Traditional weather forecasts (NWP) are weak at short horizons. Radar + extrapolation (storm motion) provides excellent 2-6 hour forecasts. For power grid operations, 2-6 hour forecasts enable rapid demand response.

Heatmap Visualization

Process radar into heatmaps: precipitation intensity, temperature estimates, storm motion. Overlaid on power grid map, operators see demand drivers geographically. High precipitation + cold temperatures = high heating demand. High temperatures + low wind = high cooling demand.

Integration with Smart Grid

Automated demand response (ADR): when radar shows incoming high demand (heat wave), automatically shed non-essential loads (water heaters, EV charging). When radar shows incoming low demand (cool front), accelerate loads. Radar-triggered ADR reduces peak demand by 2-5%.

Renewable Integration

Solar power disrupted by clouds; radar detects cloud cover. Wind power depends on wind speed, inferred from storm dynamics. Radar provides short-term renewable supply forecast, enabling better grid balancing.

Data Privacy and Accessibility

Weather radar is public government data (NOAA). All market participants can access it. No unfair advantage. However, sophisticated computer vision processing may give advantage over competitors using simpler methods.