2026-05-04T20:59:54.064Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:3:11694 2025-11-05T23:49:13Z 3

Forecasting Energy Production at Solar Substations Using LSTM Networks Optimized by Adaptive Metaheuristics, Chapter in LNEE Lecture Notes in Electrical Engineering: ICMEET 2024: Signal Processing, Telecommunication & Embedded Systems: AI and ML Applications, Springer, volume 1430 2025 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/3/11694 https://link.springer.com/chapter/10.1007/978-981-96-7249-3_1 L. Jovanovic M. Antonijevic M. Zivkovic B. Radomirovic A. Njegus N. Bacanin The world’s demand for energy is consciously on the rise. Renewable energy sources have numerous advantages over conventional fossil fuels. Although they provide many benefits, such as sustainability and ecological advantages, the practical integration of these sources into traditional grid systems faces numerous challenges, given that they are relatively new technologies. One of the main challenges is the intermittent nature of energy production from renewable resources. Employing artificial intelligence (AI) in time series forecasting can represent a potential solution to the challenges connected to balancing the production and consumption of energy from renewable resources. This paper aims to employ long short-term memory (LSTM) networks to forecast energy production from renewable sources, specifically solar power plants. To achieve satisfactory performance, metaheuristics were applied for hyperparameter optimization. Additionally, this paper presents an adjusted type of a popular optimization algorithm, aiming to overcome the limitations of the original approach. Two experiments were conducted on publicly available datasets. The optimized models show good results, with an error of less than 1 V in predictions for one hour ahead. bookPart Springer, Singapore 1 15 10.1007/978-981-96-7249-3_1 LNEE Lecture Notes in Electrical Engineering: ICMEET 2024: Proceedings of Ninth International Conference on Microelectronics Electromagnetics and Telecommunications, volume 1430