No abstract

Nowadays solar power generation has significantly improved all over the world. Therefore, the power estimation of photovoltaic (PV) using weather parameters presents the future management of energy utilization in power system planning. This article presents the power forecast of the Second Order Lever Principle Single Axis Solar Tracker (SOLPSAST) system. A deep neural network is developed using Long Short Term Memory (LSTM) and is validated on sunny, cloudy and partially cloudy days. The performance of the proposed LSTM in comparison with Support Vector Machine (SVM) has improved the Mean Absolute Proportion Error (MAPE) forecasts accuracy to 4.29%, 5.16%, and 4.82% for sunny, cloudy and partially cloudy days, respectively. Also, the estimated Mean Relative Error (MRE) value of the LSTM model for sunny, cloudy and partially cloudy days is 3.19%, 4.10%, and 4.02%, respectively. Finally, the forecasted power generation of the SOLPSAST system’s monthly average and annual generation is found to be 2.45 Wh, and 29.44 kWh, respectively.

This article presented the comparative study of the second-class lever principle single-axis solar tracking system (SCLPSAST) with the fixed solar axis (FSA) system. The SCLPSAST system continuously tracks the sun regardless of atmospheric conditions from sunrise to sunset. This SCLPSAST system is a cost effective and straightforward solar tracking system built with negligible operational costs. The Photovoltaic (PV) panel are directed towards the sun throughout the year without using any additional power. The main advantage is that an external motor is not required to control the solar panel. A detailed performance evaluation of the SCLPSAST system is carried out for 90 days (from Jan 2022 to Mar 2022) with the FSA system. Finally, the working functionality, efficiency improvement, and experimental consequences of the SCLPSAST system are detailed. SCLPSAST and the fixed solar system generated 8.92 kWh and 7.03 kWh, respectively, which is around 26.87% more energy than the FSA system.