Solar Power Configuration Search Using Decision-Tree Optimization
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Solution Overview
Problem
Conventional solar power system design is often inefficient due to limited designer knowledge and sensitivity to shading, leading to suboptimal configurations and high costs in the manual design process.
Innovation Solution
A computer-implemented method that traverses a decision tree to generate optimized solar power system configurations by refining design decisions based on site data and value functions, reducing the need for exhaustive exploration of design spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual design process is used by typical designers, then design decisions can be made, but the configurations produced are suboptimal due to limited designer knowledge and sensitivity to shading
Solution Approach 1:
The system performs automated configuration optimization without requiring expert human designers. The computer-implemented method autonomously evaluates design decisions, computes value functions, and determines optimal configurations, allowing the system to serve itself rather than relying on limited human knowledge
Solution Approach 2:
The patent replaces manual mechanical design processes with computational algorithms. Instead of designers manually making design decisions, the system uses automated computer-based optimization algorithms that traverse decision trees and compute value functions to determine optimal configurations
2Reliability
If exhaustive exploration of design space is performed, then optimal configuration can be found, but the process becomes extremely time-consuming and expensive
Solution Approach 1:
The patent divides the design space into hierarchical levels organized as a decision tree. Instead of exhaustively exploring all possible configurations at once, the system segments the search into manageable levels, making incremental design decisions at each level based on computed value functions
Solution Approach 2:
The system performs partial exploration of the design space by evaluating only the most promising configurations at each decision level. Rather than exhaustively exploring all possibilities, it computes value functions to identify and pursue optimal paths, performing sufficient action to achieve optimality without unnecessary exhaustive search
3Adaptability or versatility
If small changes are made to solar power system configuration, then design flexibility is improved, but performance may decrease dramatically due to non-linear sensitivity to shading
Solution Approach 1:
The system uses value function computation to provide feedback on how design decisions affect system performance. At each level of the decision tree, the computed value function evaluates the impact of design choices, allowing the system to adapt configurations while maintaining performance by selecting decisions that optimize the value function
Data Source
AI summary
A configuration engine traverses sequential levels of a decision tree in order to iteratively refine a configuration for a solar power system. At each level of the decision tree, the configuration engine determines the outcome of a design decision based on computing the result of a value function. The configuration engine explores configurations that optimize the value function result compared to other configurations, and may also discard less optimal configurations. When a current configuration is considered less optimal than a previous configuration generated at a previous level, the configuration engine discards the current configuration and re-traverses the decision tree starting with the previous configuration.