Terminal Voltage Prediction of Solar-Wind Hybrid Systems with Time Series Decomposition: An Analysis and Comparative Study

The demand for clean and sustainable energy sources has escalated in recent years due to growing concerns about climate change and the depletion of traditional fossil fuel reserves. Renewable energy technologies, such as wind and solar power, have emerged as promising alternatives to mitigate environmental impacts and ensure long-term energy security. The integration of solar and wind energy sources in hybrid renewable energy systems presents unique challenges due to the intermittent and variable nature of these sources. Accurate prediction of voltages in solar-wind hybrid systems is crucial for efficient power management and grid integration. Time series decomposition is a useful technique for analyzing and forecasting time series data, including solar and wind hybrid power generation. It involves breaking down the data into its constituent components, such as trend, seasonality, and residual, to gain insights and make predictions. This research paper aims to develop and compare time series decomposition prediction models for solar-wind hybrid voltages. The study investigates different decomposition methods, forecasting algorithms, and performance evaluation metrics to identify the most effective model for voltage prediction in hybrid renewable energy systems. Also shows the effectiveness of decomposition on prediction.