4.2 Article

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

Journal

IETE JOURNAL OF RESEARCH
Volume -, Issue -, Pages -

Publisher

TAYLOR & FRANCIS LTD
DOI: 10.1080/03772063.2021.1996286

Keywords

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

Funding

  1. Scientific Project Unit of Adana Alparslan Turkes Science and Technology University [19103006]

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The paper introduces a novel rotary energy system (RES) designed to provide efficient energy production for regions with high wind and solar energy potential. By testing the prototype and training different neural network architectures, it is shown that the hybrid RES can effectively produce energy to meet energy demands.
It is a known fact that fossil fuels will be depleted in the near future and 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 x 60 x 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.

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