2026-05-09T11:14:38.780Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:3:9669 2024-12-03T20:10:47Z 3

Employing Tuned VMD-Based Long Short-Term Memory Neural Network for Household Power Consumption Forecast, Chapter in SIST Smart Innovation, Systems and Technologies: CRM 2023: Congress on Control, Robotics, and Mechatronics, Springer, volume 364 2023 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/3/9669 https://link.springer.com/chapter/10.1007/978-981-99-5180-2_29 S. Petrovic V. Mizdrakovic M. Kljajic L. Jovanovic M. Zivkovic N. Bacanin Estimating household power consumption energy usage patterns can assist households in planning and managing their power consumption. To address elaborate time-series data, long short-term memory artificial neural networks are a promising strategy. However, a decomposition-forecasting method called variation mode decomposition is necessary to handle challenging time series. The accuracy and effectiveness of machine learning models are influenced by their hyperparameter values. This paper suggests using a altered sine cosine algorithm to optimize the hyperparameters of the long short-term memory model. This algorithm enhances the accuracy and performance of household energy consumption forecasting. The proposed model is compared to other long short-term memory models that are optimized by advanced metaheuristics. Simulation results indicated the improved sine cosine algorithm surpassed other advanced approaches in terms of standard time-series forecasting metrics. bookPart Springer, Singapore 357 371 10.1007/978-981-99-5180-2_29 SIST Smart Innovation, Systems and Technologies: CRM 2023: Congress on Control, Robotics, and Mechatronics, volume 364