Work

Modelling soil deposition predictions on solar photovoltaic panels using ANN under Malaysia’s meteorological condition

Year: 2024

Type: article

Abstract: Solar photovoltaic (PV) panels performance is influenced by various external factors such as precipitation, wind angle, ambient temperature, wind speed, transient irradiation, and soil deposition. Soiling accumulation on panels poses a significant challenge to PV power generation. This paper presents the development of an artificial neural network (ANN)-based soil deposition prediction model for PV systems. Conducted at a Malaysian solar farm over three months, the research utilized power output data from the inverter as model output and meteorological data as input variables. The model employed the Levenberg-Marquardt backpropagation method with Tansig and Purline activation functions. Performance assessment via statistical comparison of experimental and simulated results revealed a coefficient of determination (R2) value of 0.68073 for the ANN architecture of 5 input layers, 30 hidden layers, and 1 output layer (5-30-1). Sensitivity analysis highlighted relative humidity and wind direction as the most influential parameters affecting PV soiling rate. The developed ANN model, combined with sensitivity analysis, serves as a robust foundation for enhancing the efficiency of smart sensors in PV module cleaning systems.

Source: International Journal of Advances in Applied Sciences

DOI: 10.11591/ijaas.v13.i4.pp796-805

Authors: Muhammad Aiman Amin Muhammad Suhaimi, Nofri Yenita Dahlan, Saidatul Habsah Asman, Natarajan Rajasekar, Hassan Mohamed

Institutions: Universiti Teknologi MARA System, Universiti Teknologi MARA, Vellore Institute of Technology University, Universiti Tenaga Nasional

Language: English

Topic: Photovoltaic System Optimization Techniques

Subfield: Renewable Energy, Sustainability and the Environment

Field: Energy

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: diamond

Funder: Universiti Teknologi MARA