Behind-the-Meter Solar Forecasting from Panel Mapping and Weather Data
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Solution Overview
Problem
Electricity providers face challenges in accurately predicting behind-the-meter solar power generation due to its variability based on weather conditions and other environmental factors, which affects their ability to scale electricity generation and distribution effectively.
Innovation Solution
A computer-implemented method that analyzes aerial and satellite images to identify solar panels, estimates their generation capacity, and combines this data with meteorological information to forecast solar power generation across a geographic area, also considering infrastructure impediments to electricity distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional electricity meters are used to monitor electricity distribution, then electricity provider can track consumption, but cannot measure behind-the-meter solar power generation
Solution Approach 1:
The patent introduces satellite imagery and meteorological data as intermediary elements to indirectly measure solar power generation. Instead of directly measuring electricity at the meter, the system uses satellite images to detect solar panels and combines this with weather data to estimate generation levels, thereby overcoming the limitation of conventional meters.
Solution Approach 2:
The patent replaces the mechanical/electrical measurement system (conventional electricity meters) with a remote sensing and data processing system. Satellite imagery captures visual data of solar panels, and meteorological data provides environmental context, together substituting for direct electrical measurement to estimate behind-the-meter generation.
2Reliability
If electricity providers scale generation based on historical trends, then they can predict demand, but solar power variability makes predictions inaccurate
Solution Approach 1:
The patent changes the parameters used for prediction from purely historical consumption data to include real-time meteorological parameters (solar irradiance, cloud cover, temperature) and satellite-detected solar panel characteristics. This allows the prediction model to adapt to environmental variability and accurately forecast solar generation impacts on demand.
Solution Approach 2:
The system performs preliminary identification of solar panels via satellite imagery and pre-computes their potential generation capacity based on location and historical performance. This advance preparation enables faster, more accurate real-time demand predictions when combined with current weather conditions.
3Measurement precision
If more sophisticated prediction methods are used to account for solar power, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The patent makes existing satellite imagery and meteorological data sources serve multiple functions: they not only predict solar generation but also identify solar panel locations, estimate panel capacity, and provide contextual information about environmental conditions. This multi-functionality reduces the need for dedicated complex measurement infrastructure.
Solution Approach 2:
The system uses publicly available satellite imagery and meteorological data that self-update without requiring active probing or specialized sensors. The satellite passes automatically capture images, and weather stations continuously report conditions, providing free, ongoing data that reduces system operational complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables more accurate predictions of solar power generation, allowing electricity providers to better match electricity supply with demand, reducing instances of blackouts or excess generation.
Implementation Method 1
computing a plurality of insolation levels associated with the set of solar panels based on geographic data associated with the geographic area, where each insolation level indicates an amount of solar irradiance present at a different solar panel
Data Source
AI summary
Techniques for evaluating electricity distribution infrastructures include a method comprising: determining, by a computing device, respective positions for individual solar panels included in a plurality of solar panels located within a geographical region; determining, by the computing device based on the respective positions and meteorological data for the geographical region, respective predicted solar power generation levels for the individual solar panels; determining, by the computing device based on the respective predicted solar power generation levels for the individual solar panels, a solar power generation estimate for the geographical region; and determining, by the computing device based on the solar power generation estimate for the geographical region and one or more properties of an electricity distribution infrastructure for the geographical region, one or more infrastructure modifications for the electricity distribution infrastructure.