Performance Assessment of a Novel Eco-Friendly Solar Panel Mounted Hybrid Rotating Energy System with Renewable Energy Applications

Abstract

It is a known fact that fossil fuels will be depleted in the near future owing to the negative effects on the environment. The number of applications using renewable energy sources instead of fossil fuels to obtain energy has increased significantly. It is aimed to provide effective energy production with the proposed rotary energy system (RES) installation for regions with high wind energy and solar energy potential. In the paper, the design, manufacturing process, installation, and output power prediction of a novel RES are presented. In the proposed system, a hybrid system whose energy is derived from solar and wind energy is envisaged. The electrical characteristics of the solar panels with dimensions 140 × 60 × 2.5 mm mounted on the prototype are 6 V 100 mA. The prototype has been tested at different rotation speeds to evaluate the effect of wind energy. Moreover, the output power prediction based on Feedforward Neural Network (FFNN) and Particle Swarm Optimization trained Feedforward Neural Network (PSO-FFNN) has been performed with the data obtained from the prototype system. The three quantitative standard statistical performance evaluation measures, root mean square error (RMSE), mean absolute percentage error (MAPE) and Theil's inequality coefficient (TIC) are employed to compare the performances of these architectures. FFNN architecture, the RMSE, MAPE and TIC values are calculated as 0.0690, 0.0455 and 0.0278, respectively. For the PSO-FFNN architecture, RMSE, MAPE and TIC values are 0.0530, 0.0383, and 0.0213, respectively. It has been proved that it will be produced energy more effectively thanks to the hybrid RES in meeting energy demand.

KEYWORDS:

• Building integrated photovoltaics
• Engineering optimization
• Power prediction
• Renewable energy
• Solar energy
• Wind energy

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors would like to acknowledge the Scientific Project Unit of Adana Alparslan Turkes Science and Technology University (Project Number: 19103006) for full financial support.

Notes on contributors

Abdurrahman Yavuzdeger

Abdurrahman Yavuzdeger was born in Adana, Turkey. He is pursuing his PhD degree from the Department of Electrical and Electronic at Adana Alparslan Turkes Science and Technology University, Turkey. He is a research assistant at Alparslan Turkes Science and Technology University. His current research interests include wind turbine design, solar PV panel and generators.

Firat Ekinci

Fırat Ekinci was born in Adana, Turkey. He received the MSc and PhD degree in Department of Mechanical Engineering from Cukurova University, Turkey, in 2001 and in 2016, respectively. He has worked in Adana Alparslan Turkes Science and Technology University as an associate professor doctor since 2020. His research interests include renewable energy sources, artificial neural networks, energy systems. Dr Ekinci has authored more than 20 papers published in journals and conference proceedings. Email: [email protected]