Top 10 Best Solar Energy Calculation Software of 2026

HOMER Energy is designed for iterative PV system sizing with power balance over time, which means model changes propagate to dispatch and energy outcomes. The workflow supports PV and inverter configuration inputs, battery settings, and grid connection assumptions so energy storage coupling and time-of-use modeling can be represented in the same scenario set.

A key tradeoff is that high-fidelity PV layout and shading detail still depends on importing upstream geometry or simplified assumptions, which can limit string-level design specificity. It fits when teams need repeatable architecture comparisons and cost-relevant dispatch behavior for many design variants.

Solargis targets organizations that run many solar studies and need consistent yield estimation results across locations, with repeatable assumptions for array geometry and system configuration. The workflow centers on managing project inputs such as horizon and shading data assumptions, then generating structured performance outputs that can be included in PV project documentation. The reporting side is built for iteration, which helps when engineering teams refine assumptions after interconnection, layout, or site survey changes.

A key tradeoff is that Solargis leans toward yield and performance modeling rather than detailed electrical design tasks like wire sizing and string-level electrical layouts. Solargis fits usage situations where an engineering or development team needs a controlled modeling baseline for ROI modeling and early feasibility studies, then passes the results to a separate electrical design workflow.

Aurora Solar is built around a proposal-to-design loop where PV arrays, layouts, and performance outputs stay attached to the same project context. The workflow supports visual layout iteration and scenario changes that update the energy estimate and reporting artifacts. Modeling coverage commonly aligns with inverter behavior and temperature effects used in yield estimation workflows.

A tradeoff is that deep custom studies, like highly specialized interconnection and protection modeling, are not its core strength. Aurora Solar fits best when horizon inputs and shading assumptions are gathered during design discovery, then refined through iterative layout adjustments. It is less ideal when teams need a heavy-duty research sandbox or strict interchange formats for every intermediate step.

Polysun focuses on end-to-end solar energy calculations that connect design assumptions to yield and loss outputs for practical PV sizing. It provides workflow tooling for shading analysis, horizon handling, and temperature and inverter effects inside one project so results stay consistent across revisions.

Model outputs are exportable for external review and can be aligned to common study artifacts used in energy yield reporting and interconnection documentation. Its strongest fit is repeatable project modeling where assumptions change often, and the team needs fast re-runs without rebuilding calculation setups.

EasySolar performs PV energy yield and ROI calculations from a user-entered system design, including sizing inputs like array capacity and orientation. The workflow centers on generating a loss-informed production estimate with configurable assumptions for temperature and performance parameters.

Results support exportable calculation outputs suitable for project reporting. The model focuses on practical estimation rather than full engineering-grade simulations.

Scanifly targets solar PV sizing and energy yield workflows with a calculation-oriented interface that supports end-to-end project modeling. The tool focuses on irradiance inputs, system configuration parameters, and output reports used to compare design options.

It supports common PV study needs such as shading and loss-factor modeling used for yield estimation. Results are packaged for review handoff through structured exports and project documentation.

Solar-Planit targets solar PV project calculations with geometry-first modeling that can reuse the same layout across sizing and yield steps. The workflow centers on horizon and terrain inputs to drive shading losses, then carries those losses into yield estimation and ROI reporting.

Solar-Planit supports time-series weather inputs such as TMY3 data and can align results with standard loss-diagram style auditing. The tool also produces exportable artifacts for downstream engineering review and documentation.

Sunny Design is a solar energy calculation tool focused on PV system modeling for design workflows that need repeatable outputs. It supports PV energy yield estimation with configurable component inputs such as module and inverter parameters, plus site orientation and elevation inputs for horizon effects.

The workflow is geared toward generating consistent loss diagrams and reporting results for design review rather than only running single what-if calculations. The tool is also used to run array configuration scenarios where changes to layout inputs drive updates to energy and performance assumptions.

Global Solar Atlas calculates PV yield using global irradiance and solar resource layers with location-based modeling rather than manual dataset assembly. It provides horizon inputs and shading outputs tied to site geometry for approximating near-field shading impacts.

The workflow centers on selecting system assumptions and exporting results for further engineering use. It targets quick feasibility estimates across many locations where consistent irradiance inputs matter.

SolarEdge Designer is a PV system calculation workflow aimed at SolarEdge-based projects, with design inputs that map closely to SolarEdge hardware constraints. The software supports yield estimation inputs like module electrical parameters, inverter operating limits, and layout-driven shading inputs that feed a loss-oriented performance view.

For multi-configuration work, it enables repeatable scenario builds and report outputs aligned to common interconnection and engineering review needs. Integration depth is strongest where SolarEdge ecosystem data and component selection are already part of the design process.