DOI: https://doi.org/10.61435/ijred.2025.61502

Data-driven reconstruction of solar spectrum in a class A+ LED solar simulator

1Department of Electrical Engineering, Faculty of Engineering and Architecture, Rajamangala University of Technology Suvarnabhumi, Nonthaburi, Thailand

2Department of Mechatronics Engineering, Faculty of Engineering and Architecture, Rajamangala University of Technology Suvarnabhumi, Nonthaburi, Thailand

3Department of Electrical Engineering, Faculty of Engineering, Rajamangala University of Technology Krungthep, Bangkok, Thailand

4 Department of Electrical Engineering, Faculty of Engineering, Bangkokthonburi University, Bangkok, Thailand

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Received: 22 Jun 2025; Revised: 17 Aug 2025; Accepted: 6 Sep 2025; Available online: 10 Sep 2025; Published: 1 Nov 2025.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2025 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract

High‑spectral‑fidelity solar simulators are indispensable for rigorous photovoltaic characterization, as they provide stable, reproducible irradiance that closely conforms to the AM 1.5G reference spectrum. The latest IEC 60904‑9:2020 standard imposes stringent limits on spectral mismatch (SM), coverage, and deviation, driving the need for innovative design strategies. This work introduces a data‑driven LED spectrum reconstruction methodology to engineer a Class A+ LED Solar Simulator (LSS) spectrum. Manufacturer‑provided spectral profiles spanning 300–1200 nm were digitized using a precision plot‑digitization tool and calibrated via a Spectral Mismatch Calculator to ensure wavelength alignment and intensity normalization. Custom numerical optimization algorithms then refined these datasets to compute the optimal mixing ratios of broadband phosphor‑converted white LEDs (400–900 nm), combined with targeted UV, visible, and NIR emitters. The finalized 13‑LED configuration achieved a Spectral Coverage (SPC) of 99.52% and a Spectral Deviation (SPD) of 17.42%, exceeding the Class A+ acceptance criteria while employing a minimal component count. Although minor uncertainties may originate from the digitization process, such as image resolution and axis calibration, these can be effectively mitigated by integrating direct numerical spectra supplied by manufacturers. This approach establishes an efficient, high‑accuracy framework for LSS spectral design. Future work will advance to hardware prototyping and empirical validation of the simulator’s irradiance spectrum under real‑world operating conditions, fully compliant with IEC 60904‑9:2020.

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Keywords: Data-Driven Reconstruction, LED Solar Simulator, Spectral Coverage, Spectral Deviation, LED Spectral Data

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Section: Original Research Article
Language : EN
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