Renewable energy + industrial sustainability: Products + services
Computational precision in utility-scale solar design
calculations and design process must be independently reproducible,” says Nel. When designing large complex solar farms with thousands of components and o£en thousands of kilometres of cabling, ‘plausible’ is just too risky. The precision imperative Computational so£ware operates on fundamentally dierent principles to AI. AutoPV was built by engineers who understand electrical engineering constraints, terrain analysis, and construction requirements, using deterministic algorithms that produce design outputs which can move into the construction phase. “The acceleration in utility-scale solar deployment is increasingly powered by computational so£ware. Platforms like AutoPV are enabling engineers to rapidly model, simulate, compare configurations, and optimise complex solar power plants,” says Nel. “AutoPV enables engineers to generate multiple design configurations and compare them objectively. Each iteration includes exact cable routings, inverter placements, and complete power loss calculations. Engineers can evaluate cost versus energy yield with confidence because the numbers are exact, not estimated.” A recent 214 MW project designed with AutoPV illustrates that computational so£ware is more than a ‘guesstimation’ tool; it is an engineering tool that delivers accurate, construction-ready designs. Engineers generated eight design iterations in a single morning, comparing configurations that would traditionally require months of manual design. The analysis revealed dramatic variations across iterations. One configuration saved $1million in cable costs alone. Another optimised for maximum lifetime energy production, generating an additional $50 000 in annual revenue. A third balanced construction cost against operational performance. Each design included AutoCAD drawings that could transfer to construction, a complete bill of quantities, and validated electrical calculations. Adding another critical layer of accuracy, AutoPV has integrated terrain adaptation into its design automation so£ware. This allows engineers to upload site topography in CSV or XYZ formats directly into the design engine. As we move into more complex landscapes, integrating terrain awareness ensures that the designs remain grounded in engineering reality rather than assuming a flat-site. The path forward AI will continue to play a role in early-stage feasibility studies and in Operations and Maintenance, where it can be used for predictive maintenance and performance monitoring. Computational design so£ware will, however, likely be the primary driver for solar design, which requires accuracy and enables energy engineers to focus on value engineering rather than repetitive design tasks. “To meet global solar energy targets, it will cost engineering firms $12 billion to develop pre-construction designs for the solar projects. That equates to roughly 12 000 work years. Design automation without compromising safety and accuracy is really important if we want solar energy to lead the renewable energy mix by 2030,” says Nel. References: [1] Global renewable capacity is set to grow strongly, driven by solar PV: https:// www.iea.org/news/global-renewable-capacity-is-set-to-grow-strongly-driven-by- solar-pv [2] A.I. for Clean Energy: Accelerating Project Pipeline Development Globally: https:// exponentialroadmap.org/wp-content/uploads/2023/09/A.I.-for-Clean-Energy- Accelerating-project-pipeline-development-globally.pdf
Design software platforms like AutoPV enable engineers to model, simulate, compare configurations, and optimise complex solar power plants, quickly.
Global renewable energy is forecast to grow by 4 600 gigawatts (GW) by 2030 and solar generation capacity is expected to claim 80% of this total renewable energy growth [1] . Although AI is capturing headlines and can play a valuable role in the energy sector, for utility-scale solar design, precision is essential. The 90 to 95% accuracy that AI models produce is not good enough for engineers designing large, complex utility-scale projects. They need highly accurate, build-ready outputs. “Energy engineers tend to have a challenging relationship with AI when precision is non-negotiable,” says Paul Nel, CEO of 7SecondSolar, the solar engineering studio that developed AutoPV™. “In utility-scale solar, a single percentage point error in cable routing or inverter placement could translate into thousands in revenue loss over a project’s lifetime. That’s where computational design so£ware, based on tested engineering algorithms, delivers what AI cannot: predictable and accurate construction-ready design outputs.” Where AI fits and where it falls short AI has become a catch-all term for a plethora of data analytics tools. A key distinction should be made between Machine Learning and Generative AI. Machine Learning – ML – has been around for some time and is quite good at pattern recognition and forecasting when considering large data sets. Generative AI, however, which is driving a lot of the hype in industry today, is based mainly on large language models (LLMs) to generate new data. ML is more aligned with engineering processes as it is essentially an extension of the engineer’s data analysis toolbox, whereas Generative AI can contribute in other areas of solar project development. It serves feasibility studies, site screening, and preliminary analysis, where approximate answers can help identify feasible sites. AI is transforming the early stages of energy project development, with reports suggesting that AI- accelerated simulations and report generation can cut proposal and initial feasibility times by up to 40% [2] . But when projects advance beyond feasibility into a design phase, AI’s limitations become risks that energy engineers should avoid. The core problem with Generative AI in a design environment is that it relies on a corpus of data and information that is not easily verifiable and can include inaccuracies. While this produces plausible outputs, they are most likely not accurate ones, and very diicult, if not impossible, to verify. “Engineers must always be able to verify that their designs are safe and fit for purpose. This means the
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JUNE 2026 Electricity + Control
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