Sun-Position Cloud Mapping for Low-Cost Sunshine Prediction
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Solution Overview
Problem
Conventional cloud observation methods, such as those using satellites or ground-based whole-sky cameras, are computationally expensive and complex, particularly when predicting sunshine probability due to cloud movement, as they require predicting the movement locus of each cloud over time.
Innovation Solution
A cloud observation device and method that calculates sunshine probability by setting a wider sunshine probability calculation area upstream in the cloud movement direction with the sun position as the base point, reducing the need to predict each cloud's position and allowing easy calculation of probability at multiple time instances by adjusting the evaluation area.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the movement locus of each cloud is predicted to calculate sunshine probability, then the prediction accuracy is improved, but the calculation cost and complexity increase significantly
Solution Approach 1:
The sky is divided into multiple calculation areas, each associated with a specific time instance. Instead of tracking each cloud's movement locus throughout the entire time period, the method segments the prediction task into separate spatial regions (calculation areas) that can be independently evaluated. This segmentation allows the system to determine sunshine probability by checking cloud presence in each area at its corresponding time, rather than performing complex predictive calculations for each cloud across all time instances.
Solution Approach 2:
The invention transforms the problem from a temporal dimension (predicting cloud positions at future time points) to a spatial dimension (defining calculation areas in the sky at different times). By establishing a correspondence between calculation areas and time instances, the method simplifies the prediction process: instead of calculating where clouds will be at time T+1, T+2, etc., the system only needs to determine whether clouds are present in the pre-defined calculation areas. This dimensional transformation significantly reduces computational complexity while maintaining prediction accuracy.
2Measurement precision
If the movement locus of each cloud is predicted to calculate sunshine probability, then the prediction accuracy is improved, but the calculation time increases
Solution Approach 1:
The calculation areas are pre-defined and associated with specific time instances before the sunshine probability prediction is performed. By establishing these calculation areas in advance (preliminary action), the system eliminates the need for real-time predictive calculations of cloud movement loci. The prediction process simply involves checking whether clouds are present in the pre-established calculation areas at their corresponding times, which is a much faster operation than performing complex trajectory predictions for each cloud.
3Device complexity
If cloud movement information is not used, then the calculation method is simpler, but the sunshine probability prediction accuracy decreases
Solution Approach 1:
The calculation areas are pre-defined and associated with specific time instances before the sunshine probability prediction is performed. By establishing these calculation areas in advance (preliminary action), the system eliminates the need for real-time predictive calculations of cloud movement loci. The prediction process simply involves checking whether clouds are present in the pre-established calculation areas at their corresponding times, which is a much faster operation than performing complex trajectory predictions for each cloud.
Solution Approach 2:
The invention transforms the problem from a temporal dimension (predicting cloud positions at future time points) to a spatial dimension (defining calculation areas in the sky at different times). By establishing a correspondence between calculation areas and time instances, the method simplifies the prediction process: instead of calculating where clouds will be at time T+1, T+2, etc., the system only needs to determine whether clouds are present in the pre-defined calculation areas. This dimensional transformation significantly reduces computational complexity while maintaining prediction accuracy.
Data Source
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AI summary
To provide a cloud observation device capable of reducing calculation cost and predicting sunshine probability by a simple method. A cloud observation device (11) comprises an image acquisition module (12) which acquires an image (G1) in which a camera photographs the sky, a cloud extraction module (13) which extracts clouds in the image (G1), a sun position determination module (14) which determines a sun position (S1) in the image (G1), a sunshine probability calculation area setting module (16) which sets a sunshine probability calculation area (Ar1, Ar 4 - 12) having the sun position (S1) as a base point in the image (G1), and a sunshine probability calculation module (17) which calculates a sunshine probability after a predetermined time has elapsed based on the sunshine probability calculation area and the extracted clouds.