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Article

20 Citations

3,019 Views

19 Pages

A Compound Approach for Monthly Runoff Forecasting Based on Multiscale Analysis and Deep Network with Sequential Structure

Shi Chen,
Shuning Dong,
Zhiguo Cao and
Junting Guo

Water2020, 12(8), 2274;https://doi.org/10.3390/w12082274

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13 August 2020

Accurate runoff forecasting is of great significance for the optimization of water resource management and regulation. Given such a challenge, a novel compound approach combining time-varying filtering-based empirical mode decomposition (TVFEMD), sam...

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Article

3 Citations

2,159 Views

21 Pages

Ultra-Short-Term Wind Speed Forecasting Using the Hybrid Model of Subseries Reconstruction and Broad Learning System

Ming Pang,
Lei Zhang,
Yajun Zhang,
Ao Zhou,
Jianming Dou and
Zhepeng Deng

Energies2022, 15(12), 4492;https://doi.org/10.3390/en15124492

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20 June 2022

The traditional decomposition–combination wind speed forecasting model has high complexity and a long calculation time. As a result, an ultra-short-term wind speed hybrid forecasting model based on a broad learning system (BLS) that combines im...

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200 Views

25 Pages

Multi-Scale Photovoltaic Power Forecasting with WDT–CRMABIL–Fusion: A Two-Stage Hybrid Deep Learning Framework

Reza Khodabakhshi Palandi,
Loredana Cristaldi and
Luca Martiri

Energies2026, 19(2), 455;https://doi.org/10.3390/en19020455

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16 January 2026

Ultra-short-term photovoltaic (PV) power forecasts are vital for secure grid operation as solar penetration rises. We propose a two-stage hybrid framework, WDT–CRMABIL–Fusion. In Stage 1, we apply a three-level discrete wavelet transform...

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