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52 та иш

Иш: Advanced techniques for electricity consumption prediction in buildings using comparative correlation analysis, data normalization, and Long Short-Term Memory (LSTM) networks: A case study of a U.S. commercial building

  1. Long Short-Term Memory

    Sepp Hochreiter, Jürgen Schmidhuber

    Мақола199741 иқтибос
    ABI
  2. Deep learning in neural networks: An overview

    Jürgen Schmidhuber

    Шарҳ мақола20148 иқтибос
    ABI
  3. Investigating the impact of data normalization on classification performance

    Dalwinder Singh, Birmohan Singh

    Мақола20195 иқтибос
    ABI
  4. Predicting residential energy consumption using CNN-LSTM neural networks

    Tae Young Kim, Sung-Bae Cho

    Мақола20194 иқтибос
    ABI
  5. Artificial Intelligence for Renewable Energy Systems

    Gabriel Narvaez Morales

    Китоб20223 иқтибос
    ABI
  6. Artificial neural networks in supply chain management, a review

    Mohsen Soori, Behrooz Arezoo, Roza Dastres

    Шарҳ мақола20233 иқтибос
    ABI
  7. Supervised Sequence Labelling with Recurrent Neural Networks

    Alex Graves

    Китоб20122 иқтибос
    ABI
  8. Impact of occupancy rates on the building electricity consumption in commercial buildings

    Yang‐Seon Kim, Jelena Srebric

    Мақола20162 иқтибос
    ABI
  9. A review of irregular time series data handling with gated recurrent neural networks

    Philip B. Weerakody, Kok Wai Wong, Guanjin Wang +1

    Шарҳ мақола20212 иқтибос
    ABI
  10. A review of normalization and differential abundance methods for microbiome counts data

    Dionne Swift, Kellen G. Cresswell, Robert Johnson +2

    Шарҳ мақола20222 иқтибос
    ABI
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