Hybrid Load Forecasting

Hybrid Load Forecasting is a load forecasting methodology that integrates multiple distinct modeling techniques—such as statistical, machine learning, and deep learning approaches—within a unified framework to leverage their complementary strengths and improve prediction accuracy.

309

Publications

20.7K

Citations

1K

Authors

404

Institutions

Publications per year

2017–2026

201

Authors

1K

  1. WH
    Wei‐Chiang Hong

    Asia Eastern University of Science and Technology, Taiwan

    12 publications · 974 citations

    12

    h-index

  2. GF
    Guo‐Feng Fan

    Asia University, Taiwan

    8 publications · 617 citations

    8

    h-index

  3. JW
    Jianzhou Wang

    Dongbei University of Finance and Economics, China

    5 publications · 151 citations

    5

    h-index

  4. DS
    Dipti Srinivasan

    National University of Singapore, Singapore

    5 publications · 357 citations

    5

    h-index

  5. MK
    Muhammad Kamran

    University of Engineering and Technology Lahore, Pakistan

    4 publications · 174 citations

    4

    h-index

1–5 of 1K

Venues

  1. Energies

    32 publications · 2.1K citations

    24

    h-index

  2. IEEE Access

    21 publications · 1.5K citations

    20

    h-index

  3. Applied Energy

    15 publications · 1.7K citations

    15

    h-index

  4. IEEE Transactions on Power Systems

    13 publications · 2K citations

    13

    h-index

  5. Energy

    11 publications · 1.8K citations

    10

    h-index

1–5