Implementation of Metaheuristic-Optimized Algorithms in Tuning ANFIS for Photovoltaic Power Output Forecasting

Teguh Herlambang; Zuraini Othman; Aniza Othman

doi:10.48084/etasr.18534

Authors

Teguh Herlambang Department of Information Systems, Faculty of Economic Business and Digital Technology, Universitas Nahdlatul Ulama Surabaya, Indonesia https://orcid.org/0000-0001-7940-5104
Zuraini Othman Department of Diploma Studies, Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, Malaysia https://orcid.org/0000-0002-1404-3154
Aniza Othman Department of Diploma Studies, Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, Malaysia https://orcid.org/0000-0002-7995-635X

Volume: 16 | Issue: 4 | Pages: 37471-37478 | August 2026 | https://doi.org/10.48084/etasr.18534

Received: 4 March 2026 | Revised: 2 April 2026, 21 April 2026, and 11 May 2026 | Accepted: 27 May 2026 | Online: 9 June 2026

Corresponding author: Teguh Herlambang

Abstract

As Indonesia accelerates its transition toward renewable energy utilization, the large-scale deployment of Photovoltaic (PV) systems presents challenges regarding grid stability due to the intermittent nature of solar irradiance. To address this issue, the current study proposes a robust forecasting framework that integrates the Adaptive Neuro-Fuzzy Inference System (ANFIS) with metaheuristic optimization algorithms to accurately predict PV power output. This study focuses on optimizing ANFIS parameters to address the nonlinear complexities inherent in local weather data. Comparative experimental analysis demonstrated that the hybrid approach significantly enhanced prediction performance. The ANFIS-Particle Swarm Optimization (ANFIS-PSO) model outperformed other tested configurations, achieving the highest accuracy with a Root Mean Square Error (RMSE) of 0.68. These findings suggest that the ANFIS-PSO architecture is a highly effective tool for reliable power forecasting, thereby supporting the efficient integration of solar energy into Indonesia's electrical grid.

Keywords:

ANFIS, clean energy, electricity, forecasting, photovoltaic

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