Distributed Camera Cloud Coverage Forecasting
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Solution Overview
Problem
Existing cloud coverage prediction methods face accuracy challenges due to limitations in capturing spatio-temporal measurements, leading to prediction errors in applications such as solar energy forecasting and weather forecasting.
Innovation Solution
A method involving dynamic scheduling of spatially-distributed cameras to capture spatio-temporal measurements of cloud coverage, combined with meteorological data from non-camera sources, using soft body dynamics algorithms for tracking and forecasting, and incorporating opportunity cost analysis for optimal camera configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing prediction approaches are used, then implementation is simple, but accuracy of cloud coverage prediction deteriorates
Solution Approach 1:
The patent divides the prediction system into multiple spatially-distributed cameras that independently capture cloud images from different locations. Each camera segment contributes local measurements that are aggregated to form comprehensive spatio-temporal data, improving prediction accuracy while distributing system complexity across multiple simple units
Solution Approach 2:
The patent transitions from single-point or single-camera measurements to multi-dimensional spatio-temporal measurements by deploying cameras across multiple spatial locations and capturing images over time sequences. This dimensional expansion enables more accurate cloud coverage prediction through comprehensive spatial and temporal data coverage
2Measurement precision
If distributed camera networks are deployed to capture spatio-temporal measurements, then prediction accuracy improves, but system complexity and cost increase
Solution Approach 1:
The patent employs standard digital cameras that can serve multiple functions - capturing cloud images for meteorological prediction while potentially serving other surveillance or monitoring purposes. This multi-functionality reduces overall system complexity by using通用 equipment rather than specialized devices
Solution Approach 2:
The system implements feedback mechanisms where captured cloud images are processed to generate prediction results, which are then compared with actual measurements to refine future predictions. This feedback loop continuously improves accuracy while automating the complex processes of data aggregation and analysis
Data Source
AI summary
Methods, systems, and computer program products for cloud coverage estimation by dynamic scheduling of a distributed set of cameras are provided herein. A computer-implemented method includes transmitting one or more instructions to each of multiple spatially-distributed cameras in a pre-determined geographic area, wherein the one or more instructions cause each of the spatially-distributed cameras to change a context from (i) a pre-determined task to (ii) capturing one or more spatio-temporal measurements of cloud coverage; tracking the one or more spatio-temporal measurements of cloud coverage captured by the multiple spatially-distributed cameras; obtaining one or more meteorological measurements from one or more non-camera sources; and generating a cloud coverage forecast for the pre-determined geographic area based on (i) the one or more spatio-temporal measurements of cloud coverage captured by the multiple spatially-distributed cameras and (ii) the one or more meteorological measurements from the one or more non-camera sources.