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Documentation Index

Fetch the complete documentation index at: https://docs.plantpredict.com/llms.txt

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This page provides access to technical reviews and model validations of PlantPredict, often performed by trusted, independent third parties. These materials include peer-reviewed journal articles, conference papers, and independent engineering reports commissioned to evaluate the accuracy of PlantPredict’s energy predictions, the soundness of its underlying algorithms, and the bankability of the platform for utility-scale solar project development. References are listed in reverse chronological order (most recent first). Click the title of each paper to download the document or open the publisher page.

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Peer-Reviewed Publications

Deville, L., Anderson, K. S., Sutterlueti, J., Chambers, T. L., De Brabandere, K., Perez Cicala, F., Lopez-Lorente, J., Mirletz, B., Neubert, A., Nikam, M., Oliosi, M., Prilliman, M., Rhee, K., Schnierer, B., Spokes, J., Wittmer, B., & Theristis, M. (2026). Feature review of photovoltaic modeling software utilizing blind performance assessment. Solar Energy, 304, 114207. https://doi.org/10.1016/j.solener.2025.114207 The first-ever onymous blind modeling comparison, led by Sandia National Laboratories, evaluated seven commercially used PV software tools — 3E SynaptiQ, PlantPredict, PVsyst, RatedPower, SAM, SolarFarmer, and Solargis Evaluate — against measured performance from both lab- and utility-scale fixed-tilt, monofacial systems at sub-hourly time intervals. Predictions were submitted directly by software representatives, and notable feature differences (POA transposition, module temperature, shading, and performance models) were analyzed side-by-side. Software-level deviations from mean error in annual yield reached up to 2.5 % for the lab-scale system and 6.0 % for the utility-scale system, reflecting both user-decision variability and inherent model differences. The paper provides four summary tables of methodological differences across tools, intended as a resource to help users select the most suitable software for their application.
Riedel-Lyngskær, N., Berrian, D., Mira, D. A., Aguilar Protti, A., Behrensdorff Poulsen, P., Libal, J., & Vedde, J. (2020). Validation of bifacial photovoltaic simulation software against monitoring data from large-scale single-axis trackers and fixed tilt systems in Denmark. Applied Sciences, 10(23), 8487. https://doi.org/10.3390/app10238487 Researchers from the Technical University of Denmark, ISC Konstanz, and European Energy A/S validated eight bifacial PV simulation tools — including PlantPredict — against measured data from 26-kWp bifacial arrays within a 420-kWp site in Denmark (55.6° N, 12.1° E). The study covered both horizontal single-axis tracker (HSAT) and fixed-tilt configurations, comparing modeled string-level power, rear and front plane-of-array irradiance, and module temperature to field measurements from spectrally flat Class A sensors. Results showed that state-of-the-art bifacial performance models add approximately 0.5 % uncertainty to the PV modeling chain. For the site studied, 2-D view-factor fixed-tilt simulations were within ±1 % of the measured monthly bifacial gain, while 2-D view-factor and 3-D ray-tracing single-axis tracker simulations were within roughly 2 % and 1 %, respectively.
Passow, K., Ngan, L., Littmann, B., Lee, M., & Panchula, A. F. (2015). Accuracy of energy assessments in utility scale PV power plant using PlantPredict. In 2015 IEEE 42nd Photovoltaic Specialist Conference (PVSC) (pp. 1–6). IEEE. https://doi.org/10.1109/PVSC.2015.7356172 This First Solar–authored paper, presented at the 42nd IEEE PVSC in New Orleans, validated PlantPredict in two ways. First, 51 equivalent simulations were run in both PVsyst and PlantPredict, yielding a mean energy-yield difference of 0.13 % ± 0.52 % (1-σ). Second, in-depth actual-versus-expected analyses were performed on 20 utility-scale systems totaling nearly 1 GWDC of First Solar CdTe modules, where PlantPredict under-predicted energy by 0.41 % ± 2.01 % (1-σ) using First Solar’s modeling guidance. The paper also benchmarked the impact of transposition model selection (Perez vs. Hay–Davies), the Hayes advanced thermal model, and hourly versus monthly spectral correction — establishing the empirical foundation of PlantPredict’s accuracy claims.

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Independent Engineering Reports

DNV GL. (2017, May 22). Technology review of First Solar’s PV simulation software PlantPredict (Document No. 10031850-HOU-R-01, Issue C). DNV KEMA Renewables, Inc. DNV GL — a leading global independent energy advisor — performed a technology review of PlantPredict covering platform architecture, model algorithms, and validation methodology. The report evaluates PlantPredict’s implementation of irradiance transposition, shading, thermal, single-diode, and inverter performance models, and compares its outputs against PVsyst and against operational performance data from First Solar’s portfolio. DNV GL’s review supports the conclusion that PlantPredict is suitable for bankable, utility-scale PV energy assessments.
ICF Resources, LLC. (2017, May 16). Independent engineer’s report for First Solar PlantPredict PV modeling platform. ICF. ICF — a global management and analytical consulting firm with an Energy Advisory Services group focused on independent engineering and due-diligence work — performed an independent assessment of the PlantPredict platform covering the user interface, platform hierarchy, model algorithms, reporting and integration capabilities, and revision management. The report includes ICF’s own independent benchmarking study against PVsyst for four geographically diverse theoretical projects (CSi and CdTe technologies; fixed-tilt and tracker configurations) and concludes that PlantPredict is capable of producing PV generation estimates with uncertainty comparable to PVsyst and other bankable modeling platforms.
Black & Veatch Management Consulting, LLC. (2017, January 24). PlantPredict independent engineer report (Project No. 193462). Black & Veatch. Black & Veatch, a leading global engineering, procurement, and construction firm in the energy sector, performed an independent engineer’s review of PlantPredict for First Solar. The report evaluates PlantPredict’s algorithms, validation methodology, and modeling outputs in the context of utility-scale PV project development and finance, and confirms the platform’s suitability as a bankable energy assessment tool.
Leidos Engineering, LLC. (2016, November 17). Review of First Solar’s PlantPredict photovoltaic simulation software: Final report (CM002569). Leidos. Leidos Engineering — a widely recognized independent engineer for utility-scale PV projects — completed a review of the PlantPredict software tool and the associated methodology developed by First Solar. The report benchmarks PlantPredict against PVsyst (the de facto industry-standard tool at the time) and concludes that PlantPredict provides modeling accuracy equivalent to other energy prediction modeling tools used in the industry, with predictions matching operational data within the uncertainty expected of bankable simulation tools.