Solar Power Forecasting Using Clear-Sky and Cloud Cover Data
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Solution Overview
Problem
Electricity providers face challenges in accurately predicting solar power generation due to varying capabilities of solar power systems and environmental factors, leading to inefficiencies in scaling electricity production to meet demand.
Innovation Solution
A computer-implemented method that generates a clear-sky solar power generation level and a measurement device index to forecast solar power generation within a geographical area, using a forecast engine that divides the area into solar regions, assigns measurement devices to photovoltaic installations, and scales clear-sky output based on real-time insolation data and cloud cover.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If electricity providers use traditional demand prediction methods based on historical consumption data, then they can predict general electricity demand patterns, but they cannot accurately predict solar power generation levels due to varying system capabilities and environmental factors
Solution Approach 1:
The patent segments the geographical area into multiple solar regions with distinct insolation characteristics. Each solar region is further divided into zones with similar weather patterns. This segmentation allows the system to account for spatial variations in solar irradiance and weather conditions, improving prediction accuracy for diverse solar power systems across different locations.
Solution Approach 2:
The patent introduces key parameters including clear-sky insolation levels, cloud cover indices, and solar region classifications to characterize and differentiate solar power generation potential across various locations. By monitoring and utilizing these parameters, the system adapts to varying environmental conditions and system capabilities, resolving the contradiction between prediction precision and system diversity.
2Productivity
If electricity providers scale electricity production based on historical demand patterns, then they can prepare for anticipated demand, but they cannot efficiently adjust to actual solar power generation levels, reducing overall efficiency
Solution Approach 1:
The patent implements a feedback mechanism where real-time insolation data and cloud cover measurements are continuously monitored and fed into the prediction model. This feedback loop allows electricity providers to adjust electricity production scaling decisions based on actual solar generation conditions rather than relying solely on historical demand patterns, thereby improving productivity while utilizing available solar generation information.
Solution Approach 2:
The patent performs preliminary forecasting of solar power generation levels by analyzing current insolation data, cloud cover indices, and historical patterns before electricity production scaling decisions are made. This preliminary action enables providers to proactively adjust production plans based on predicted solar generation, improving efficiency by aligning production scaling with actual solar power availability rather than reacting to past demand alone.
3Measurement precision
If electricity providers ignore solar power generation in their demand predictions, then their prediction models remain simple, but they cannot accurately determine net electricity generation needs
Solution Approach 1:
The patent develops a multi-functional forecasting system that simultaneously performs solar region classification, insolation level estimation, cloud cover indexing, and net generation prediction. This universal system handles multiple functions within a unified framework, improving net generation prediction accuracy while managing complexity through integration rather than separate independent systems.
Solution Approach 2:
The patent applies local quality by tailoring forecasting parameters and models to specific solar regions and zones based on their unique insolation characteristics and weather patterns. Rather than using a single uniform model, the system adapts local forecasting approaches to match regional conditions, improving prediction accuracy while maintaining manageable complexity through localized rather than overly generalized models.
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 allows for accurate prediction of solar power generation, enabling electricity providers to efficiently scale production and supply electricity to consumers, independent of environmental factors like cloud cover and seasonal changes.
Implementation Method 1
a first photovoltaic installation...indicates an amount of electricity generated by the first photovoltaic installation
Data Source
AI summary
Techniques for forecasting solar power generation include a computing device determining a clear-sky solar power generation level for a photovoltaic installation; receiving, from a first measurement device, measurement data indicating an amount of cloud cover at a first location of the first measurement device, wherein the first measurement device and the photovoltaic installation are located in a same geographical area; and generating a solar power generation forecast for the photovoltaic installation based on the clear-sky solar power generation level and the measurement data.