Direct Normal Irradiance Prediction-Based Optimum Interval Tilt Angles for Enhancement of Energy Output, Levelized Cost of Energy, and CO2 Emission in a Grid-Connected Photovoltaic System

Abstract

The tilt angle of photovoltaic (PV) panels is a crucial determinant of their performance and can be adjusted using different tracking methods. Periodically changing the tilt angle strikes a practical balance between efficiency and cost. This work introduces a bi-directional long short-term memory (Bi-LSTM)-based direct normal irradiance (DNI) prediction to estimate the time intervals for the tilt angle adjustments. DNI prediction involves 22-year (2000–2022) historical time series data and the Bi-LSTM deep learning model to predict DNI at different time frames for the location Madurai, India. Using the predicted DNI, tilt angle-based DNI is mapped using the tilt angle correlation through a nearest neighborhood interpolation method. DNI potential over a specific period is utilized to find the optimum time intervals for the tilt angle adjustments. The simulation study of this work is implemented with a 5 kW grid-connected solar PV system using pvsyst software. The effectiveness of the proposed methodology is evaluated based on the improvements in power output, levelized cost of energy (LCOE), and carbon emission reductions and compared with other existing methods. The results showed that using the proposed optimal tilt angle intervals led to a 10.31% increase in PV output power, the lowest LCOE at 3.61 c/kW h, and 8.363 tCO2/year carbon emissions.