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 of 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: Systems and methods are disclosed for generating permit sets. A rules engine can traverse various requirements of a rules data structure in conjunction with rule settings that define permitting rules in effect for a particular jurisdiction to determine a value for each requirement. Document objects may then be created by combining project inputs with the determined values. A composing engine can receive the document objects and populate one or more page templates with the document objects to create a permit set.

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 of 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: An optimization engine determines the optimal configuration for a solar power system by simulating the performance of different solar module arrays projected onto a target surface. The optimization engine selects the optimally-performing solar module array and then simulates the performance of additional solar module subarrays projected onto various regions of the target surface not already covered by the optimally-performing solar module array. The optimal configuration includes the optimally-performing solar module array and one or more optimally-performing solar module subarrays.

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 of 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: 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: Systems and methods are disclosed for generating permit sets. A rules engine can traverse various requirements of a rules data structure in conjunction with rule settings that define permitting rules in effect for a particular jurisdiction to determine a value for each requirement. Document objects may then be created by combining project inputs with the determined values. A composing engine can receive the document objects and populate one or more page templates with the document objects to create a permit set.

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: A computing device is equipped with a configuration engine and a solutions engine generates candidate solar power system configurations and corresponding pricing solutions, respectively. The computing device may be one of several computing devices located in a place of public accommodation, such as a retail store. Upon receiving a user selection of a candidate solar power system configuration and a pricing solution the a results engine of the computing device generates a results package for a solar power proposal that includes a signature-ready proposal that the user may execute on site.

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.

Abstract: An optimization engine determines the optimal configuration for a solar power system by simulating the performance of different solar module arrays projected onto a target surface. The optimization engine selects the optimally-performing solar module array and then simulates the performance of additional solar module subarrays projected onto various regions of the target surface not already covered by the optimally-performing solar module array. The optimal configuration includes the optimally-performing solar module array and one or more optimally-performing solar module subarrays.