Solar Power Configuration Using Decision-Tree Value Optimization
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Solution Overview
Problem
Conventional solar power system design is often suboptimal due to limited designer knowledge and sensitivity to shading, leading to decreased performance and high costs associated with manual configuration processes.
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 configuration methods are used by designers with limited knowledge, then the design process is simple to perform, but the solar power system performance deteriorates due to suboptimal design decisions and sensitivity to shading
Solution Approach 1:
The system performs self-configuration by automatically generating optimized solar panel arrangements using computational algorithms. The computer-implemented method autonomously evaluates multiple design decisions and selects optimal configurations without requiring human designer intervention, thereby achieving high performance while maintaining operational simplicity for the end user.
Solution Approach 2:
The manual mechanical design process is replaced with a computer-implemented computational system. The patent substitutes human designer activities with automated software that traverses decision trees, computes value functions, and generates optimized configurations, thereby eliminating the need for designers with specialized knowledge while improving system performance.
2Reliability
If exhaustive exploration of design space is performed to find optimal configurations, then the solar power system performance is maximized, but the time and computational resources required increase significantly
Solution Approach 1:
The design space is segmented into discrete decision tree levels, where each level represents a specific design decision (e.g., panel orientation, spacing, configuration type). This segmentation allows the system to systematically explore configurations in a structured manner, evaluating and pruning branches based on intermediate results, thereby reducing the overall computational burden while maintaining optimization quality.
Solution Approach 2:
The system performs partial exploration of the design space by traversing the decision tree to a sufficient depth to achieve near-optimal configurations without exhaustively evaluating every possible arrangement. The computational method stops when the value function indicates satisfactory optimization, avoiding unnecessary exhaustive search while still achieving high performance.
3Reliability
If multiple design decisions are made to optimize solar power system configuration, then the system performance is improved, but the device complexity and number of parameters to manage increase
Solution Approach 1:
The computer-implemented configuration system serves multiple functions: it generates initial configurations, evaluates design decisions, computes value functions, and outputs optimized arrangements. This multi-functional automated system replaces multiple separate design tools and processes, managing the complexity of multiple design decisions through a unified software platform that handles all configuration aspects.
4Productivity
If traditional manual design methods are used, then the implementation cost is lower in terms of computational resources, but the overall cost increases due to designer expertise requirements and time consumption
Solution Approach 1:
The manual design process is replaced with a computer-implemented automated system that uses computational algorithms to generate optimized configurations. This substitution dramatically increases configuration speed by performing calculations and evaluations that would be time-consuming for human designers, while the software manages the complexity of multiple design decisions through structured decision tree traversal and value function computation.
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.