Enhanced Short-Term PV Power Forecasting via a Hybrid Modified CEEMDAN-Jellyfish Search Optimized BiLSTM Model

Enhanced Short-Term PV Power Forecasting via a Hybrid Modified CEEMDAN-Jellyfish Search Optimized BiLSTM Model

Accurate short-term photovoltaic (PV) power forecasting is crucial for ensuring the stability and efficiency of modern power systems, particularly given the intermittent and nonlinear characteristics of solar energy. This study proposes a novel hybrid forecasting model that integrates complete ensem...

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Bibliographic Details
Main Authors: Yanhui Liu, Jiulong Wang, Lingyun Song, Yicheng Liu, Liqun Shen
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Energies
Subjects:
PV power
CEEMDAN
jellyfish search algorithm
BiLSTM
short-term PV forecasting
Online Access:https://www.mdpi.com/1996-1073/18/13/3581
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