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ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:1:11723 2026-01-26T15:54:33Z 1

Enhanced Forecasting of Photovoltaic Power Output via a Modified Metaheuristic Algorithm 2025 004.89 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/1/11723 https://ieeexplore.ieee.org/document/11240994 V. Gajic L. Jovanovic V. Zeljkovic B. Radomirovic M. Zivkovic N. Bacanin The continuously increasing energy demands of the world and the finite nature of fossil fuels are just some of the challenges contributing to the modern energy crisis. Many look toward renewable sources of energy as a solution. However, certain challenges are intrinsic to sources such as solar energy. One notable issue is the sporadic nature of production. Nevertheless, accurately predicting production could help mitigate this issue by providing better insights and aiding in planning around energy demand, thereby helping to balance consumption with grid supplementation. This work explores the application of time series forecasting methods to predict power production using real-world solar farm data. However, the use of forecasting models based on recurrent neural networks (RNNs) is not without challenges. Selecting appropriate hyperparameters is vital for achieving desirable outcomes. To address this, the study introduces a metaheuristic optimization approach based on the Reptile Search Algorithm (RSA) to improve the hyperparameter selection process. conferenceObject 42 45 10.1109/TELSIKS65601.2025.11240994 2025 17th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS), IEEE, Nis, Serbia