Work

A Data-Driven Calibration of the FEM Temperature Model with Wind Direction Input

Year: 2024

Type: conference-paper

Abstract: This paper proposes an enhanced method for photovoltaic (PV) temperature (TPV) modeling using a datadriven approach. By modifying the methodology of Herteleer et al. [1], we aim to make the FEM temperature model easier to calibrate and more accurate. The calibration of Faiman’s model [2] coefficients is optimized using modern computational techniques, minimizing the error between measured and estimated temperatures. A novel component is introduced: the inclusion of wind direction as a variable influencing the cooling effect on PV systems. Our results demonstrate an improvement in model accuracy, reducing the mean absolute error (MAE) by 0.29°C compared to previous methods. This advancement provides a more refined tool for estimating TPV in real-world conditions.

Source: EU PVSEC

DOI: 10.4229/eupvsec2024/3av.3.9

Authors: Kladas, Anastasios, Herteleer, Bert, Cappelle, Jan

Language: English

Topic: Solar Radiation and Photovoltaics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action

Open Access status: green

OpenAlex ID: W6929319676

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