A Validated Framework for Far Field Noise Prediction of Containerized BESS From Optimized Sound Power Measurement to Simulation

This is the author's accepted manuscript (preprint). This paper has been accepted for presentation at the 55th International Congress and Exposition on Noise Control Engineering (Inter-Noise 2026), to be held in August 2026. The final published version will be available in the conference proceedings. The proliferation of large-scale Battery Energy Storage System (BESS) stations poses significant environmental noise challenges, necessitating accurate far-field noise prediction in the early design stages. The accuracy of these predictions is critically dependent on precise sound source data of individual BESS containers. This paper presents a novel framework using a mobile robotic arm to perform high-resolution sound intensity scanning (ISO 9614-2) across the container's surfaces. This method enables the precise identification of noise-contributing areas, yielding a high-resolution source map that is used to create a high-fidelity area source model in CadnaA. The model's predictions were validated against on-site measurements at a receiver 71.2 m from the source, achieving a prediction error in overall Sound Pressure Level (SPL) of only 1.5 dB. In contrast, a conventional model based on the ISO 3744 standard yielded a prediction error of 7.5 dB in the same scenario. The proposed framework thus significantly enhances noise prediction accuracy, providing a more reliable tool for designing effective noise control measures for BESS facilities.