Abstract: 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.

Abstract: 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.

Abstract: An optimization engine determines an optimal configuration for a solar power system projected onto a target surface. The optimization engine identifies an alignment axis that passes through a vertex a boundary associated with the target surface and then constructs horizontal or vertical spans that represent contiguous areas where solar modules may be placed. The optimization engine populates each span with solar modules and aligns the solar modules within adjacent spans to one another. The optimization engine then generates a performance estimate for a collection of populated spans. By generating different spans with different solar module types and orientations, the optimization engine is configured to identify an optimal solar power system configuration.

Abstract: 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.

Abstract: An optimization engine determines an optimal configuration for a solar power system projected onto a target surface. The optimization engine identifies an alignment axis that passes through a vertex a boundary associated with the target surface and then constructs horizontal or vertical spans that represent contiguous areas where solar modules may be placed. The optimization engine populates each span with solar modules and aligns the solar modules within adjacent spans to one another. The optimization engine then generates a performance estimate for a collection of populated spans. By generating different spans with different solar module types and orientations, the optimization engine is configured to identify an optimal solar power system configuration.