Concepedia

Residential Load Forecasting

Residential Load Forecasting is a specialized form of load forecasting that predicts electricity demand at the household or dwelling level by modeling consumption patterns unique to residential users, distinguished from commercial or industrial load forecasting by its focus on domestic energy usage behaviors, seasonal variability, and socio-demographic influencing factors.

568

Publications

37.1K

Citations

1.8K

Authors

651

Institutions

Publications per year

2017–2026

385

Parents

Publications

568

Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network

Weicong Kong, Zhao Yang Dong, Youwei Jia et al. · IEEE Transactions on Smart Grid · 2017 · 2.5K citations

, , +16

Deep Learning for Household Load Forecasting—A Novel Pooling Deep RNN

Heng Shi, Minghao Xu, Ran Li · IEEE Transactions on Smart Grid · 2017 · 1.1K citations · Full text

, , +16

Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks

Aowabin Rahman, Vivek Srikumar, Amanda D. Smith · Applied Energy · 2017 · 736 citations

, , +16

Short-Term Residential Load Forecasting Based on Resident Behaviour Learning

Weicong Kong, Zhao Yang Dong, David J. Hill et al. · IEEE Transactions on Power Systems · 2017 · 608 citations

, , +17

Hybrid CNN-LSTM Model for Short-Term Individual Household Load Forecasting

Musaed Alhussein, Khursheed Aurangzeb, Syed Irtaza Haider · IEEE Access · 2020 · 521 citations · Full text

, , +16

1–5 of 568

Authors

1.8K

  1. ZY
    Zhao Yang Dong

    The University of Sydney, Australia

    7 publications · 3.4K citations

    7

    h-index

  2. FL
    Fengji Luo

    The University of Sydney, Australia

    7 publications · 906 citations

    7

    h-index

  3. CR
    Chunming Rong

    University of Stavanger, Norway

    6 publications · 221 citations

    6

    h-index

  4. AC
    Antorweep Chakravorty

    University of Stavanger, Norway

    6 publications · 221 citations

    6

    h-index

  5. GR
    Gianluca Ranzi

    The University of Sydney, Australia

    5 publications · 266 citations

    5

    h-index

1–5 of 1.8K

Institutions

651

  1. UNSW Sydney

    Sydney, Australia

    11 h-index

    28 publications

    9.3K citations

  2. North China Electric Power University

    Beijing, China

    11 h-index

    31 publications

    1.8K citations

  3. The University of Sydney

    Sydney, Australia

    9 h-index

    23 publications

    5.7K citations

  4. Stanford University

    Stanford, United States

    7 h-index

    13 publications

    664 citations

  5. Aalborg University

    Aalborg, Denmark

    7 h-index

    22 publications

    1.1K citations

  6. University of California, Berkeley

    Berkeley, United States

    6 h-index

    13 publications

    470 citations

  7. University of Stavanger

    Stavanger, Norway

    6 h-index

    19 publications

    723 citations

  8. Tsinghua University

    Beijing, China

    6 h-index

    12 publications

    612 citations

1–8 of 651

Venues

  1. Applied Energy

    44 publications · 4.6K citations

    36

    h-index

  2. Energy

    33 publications · 2.6K citations

    29

    h-index

  3. Energy and Buildings

    28 publications · 2.4K citations

    25

    h-index

  4. Energies

    29 publications · 1.1K citations

    23

    h-index

  5. IEEE Transactions on Smart Grid

    25 publications · 5.8K citations

    22

    h-index

1–5