Research Article Short-Term PV Power Forecasting Using a Hybrid TVF-EMD-ELM Strategy Reski Khelifi , 1 Mawloud Guermoui , 1 Abdelaziz Rabehi , 2 Ayoub Taallah , 3 Abdelhalim Zoukel , 4,5 Sherif S. M. Ghoneim , 6 Mohit Bajaj , 7,8,9 Kareem M. AboRas , 10 and Ievgen Zaitsev 11 1 Applied Research Unit in Renewable Energies URAER, Renewable Energy Development Center CDER, Ghardaia 47133, Algeria 2 Ziane Achour University of Djelfa, Djelfa, Algeria 3 College of Physics, Sichuan University, Chengdu, China 4 Laboratory Physico•Chemistry of Materials, Laghouat University, Algeria 5 Center for Scientifc and Technical Research in Physicochemical Analysis (PTAPC•Laghouat•CRAPC), Laghouat, Algeria 6 Electrical Engineering Department, College of Engineering, Taif University, P. O. Box 11099, Taif 21944, Saudi Arabia 7 Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan 8 Department of Electrical Engineering, Graphic Era (Deemed to be University), Dehradun 248002, ndia 9 Graphic Era Hill University, Dehradun 248002, ndia 10 Department of Electrical Power and Machines, Faculty of Engineering, Alexandria University, Alexandria, Egypt 11 Department of Teoretical Electrical Engineering and Diagnostics of Electrical Equipment, nstitute of Electrodynamics, National Academy of Sciences of Ukraine, Peremogy, 56, Kyiv•57, 03680, Ukraine Correspondence should be addressed to Ievgen Zaitsev; zaitsev@i.ua Received 26 October 2022; Revised 19 January 2023; Accepted 20 January 2023; Published 13 February 2023 Academic Editor: Davide Falabretti Copyright © 2023 Reski Khelif et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Tis paper discusses the efcient implementation of a new hybrid approach to forecasting short•term PV power production for four diferent PV plants in Algeria. Te developed model incorporates a time•varying flter•empirical mode decomposition (TVF• EMD) and an extreme learning machine (ELM) as an essence regression. Te TVF•EMD technique is used to deal with the fuctuation of PV power data by splitting it into a series of more stable and constant subseries. Te specifed set of features (intrinsic mode functions (IMFs)) is utilized for training and improving our forecasting extreme learning machine model. Te adjusted ELM model is used to evaluate prediction efciency. Te suggested TVF•EMD•ELM model is assessed and verifed in various Algerian locations with varying climate conditions. In all examined regions, the TVF•EMD•ELM model generates less than 4% error in terms of normalized root mean square error (nRMSE). 1. Introduction Te vision and goal of countries around the world have been to create a sustainable and environmentally friendly econ• omy by developing plans for a promising future by investing in green and renewable energies, notably solar energy. Te future installation of PV capacity is expected to reach 4,815GW by 2040, according to the IEA 2019 Sustainable Development Scenario [1]. In this regard, Algeria, like other countries in the world, has begun investing in the feld of photovoltaic energy in order to diversify energy sources and not rely entirely on fossil energy within a time frame set by the Algerian government to reach 22,000 megawatts of electricity production from renewable sources, which is 2011–2030 [2]. To achieve this goal, the task of installing photovoltaic stations was entrusted to the national company Sonelgaz, which has experience in the feld of renewable energies, as it installed 23 photovoltaic stations connected to the network and wind Hindawi International Transactions on Electrical Energy Systems Volume 2023, Article ID 6413716, 14 pages https://doi.org/10.1155/2023/6413716