A Performance Comparison of the Extended Kalman Filter and the Unscented Kalman Filter for Photovoltaic Power Output Forecasting
Received: 5 August 2025 | Revised: 30 September 2025 and 30 October 2025 | Accepted: 1 November 2025 | Online: 9 February 2026
Corresponding author: Rizky Amalia Sinulingga
Abstract
The issue of using renewable energy to replace fossil fuels has become the main focus of all countries worldwide. Fossil energy reserves in the world are no longer as abundant as in previous years. Therefore, further innovation and development are required to address these problems. Until today, many countries around world are still dependent on fossil fuels for transportation and electricity. The costs and impacts of this are significant. The combustion of fossil fuels can contaminate the environment and endanger human health. Innovation in the field of electricity began with the application of photovoltaic (PV) as a new solar-based energy source. Indonesia is a country with great potential due to its tropical climate and sunshine that lasts all year long. In line with the progress of information technology, the optimization and development of PV power can be implemented optimally to provide sufficient electrical energy that can be distributed equally. In this study, two filtering methods, namely the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), were examined based on their accuracy in forecasting PV power. Both methods are robust in addressing non-linearity issues. Based on the simulation results, the EKF achieved the best Root Mean Squared Error (RMSE) value of 0.09381, and the UKF achieved 0.09041.
Keywords:
electricity, estimation, machine learning, photovoltaicDownloads
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Copyright (c) 2025 Rizky Amalia Sinulingga, Teguh Herlambang; Puguh Triwinanto; Zuraini Othman, Ariff Idris, Norazlin Mohammed, Gagas Gayuh Aji, Mochammad Romli Arief
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