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Long-Short Term Memory Model with Univariate Input for Forecasting Individual Household Electricity Consumption

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The 8th International Conference on Advanced Machine Learning and Technologies and Applications (AMLTA2022) (AMLTA 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 113))

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Abstract

Power load forecasting is becoming the key role in nowadays power distribution networks to understand the behavior of the electrical power systems where end users can predict the future trends of electricity usage and hence manage their residential appliances to reduce high electrical bills. Some researchers used traditional methods that cannot handle the problem of large-scale nonlinear time series data. The main objective of this paper is to conduct various optimized deep learning models for power load forecasting and presents the best performing model with high accuracy and the lowest possible Mean-Square Error by fine-tuning the parameters to achieve the best possible configurations of the model. Using hybrid Recurrent Neural Network techniques, optimal results have been achieved. The research was conducted on Long-short term memory, Long-short term memory encoder-decoder, Convolutional Neural Network Long-short term memory, Gated Recurrent Units, Convolutional Long-short term memory, and Bidirectional Long-short term memory. The training process was conducted using Google Colaboratory and the results showed that Convolutional Long-short term memory has the lowest Root-Mean Square Error among the 6 Long-short term memory networks time series forecasting models. The prediction plot of prediction and real values against epochs are almost inline.

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Author information

Authors and Affiliations

  1. Department of Applied Intelligent Mechanical and Electrical Engineering, Yu Da University of Science and Technology, Miaoli County, 361, Taiwan (R.O.C.)

    Kuo-Chi Chang

  2. Fujian Province Key Laboratory of Automotive Electronics and Electric Drive, College of Transportation, Fujian University of Technology, Fuzhou, 350118, China

    Elias Turatsinze & Jishi Zheng

  3. Fujian Provincial Key Laboratory of Big Data Mining and Application, Fujian University of Science and Technology, Fuzhou, China

    Kuo-Chi Chang & Governor David Kwabena Amesimenu

  4. Department of Tourism, Shih-Hsin University, Taipei City, 116, Taiwan (R.O.C.)

    Fu-Hsiang Chang

  5. Institute of Environmental Engineering, National Taiwan University, Taipei, 10617, Taiwan (R.O.C.)

    Hsiao-Chuan Wang

  6. Department of Business Administration, North Borneo University College, 88400, Kota Kinabalu, Sabah, Malaysia

    Kuo-Chi Chang

Authors
  1. Kuo-Chi Chang
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  2. Elias Turatsinze
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  3. Jishi Zheng
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  4. Fu-Hsiang Chang
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  5. Hsiao-Chuan Wang
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  6. Governor David Kwabena Amesimenu
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Corresponding authors

Correspondence to Kuo-Chi Chang or Elias Turatsinze .

Editor information

Editors and Affiliations

  1. Faculty of Computer and AI, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Port Said University, Port Fouad, Egypt

    Rawya Y. Rizk

  3. Department of Computer Science, VŠB-TUO, Ostrava-Poruba, Czech Republic

    Václav Snášel

  4. Faculty of Engineering, Port Said University, Port Fouad, Egypt

    Rehab F. Abdel-Kader

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Chang, KC., Turatsinze, E., Zheng, J., Chang, FH., Wang, HC., Amesimenu, G.D.K. (2022). Long-Short Term Memory Model with Univariate Input for Forecasting Individual Household Electricity Consumption. In: Hassanien, A.E., Rizk, R.Y., Snášel, V., Abdel-Kader, R.F. (eds) The 8th International Conference on Advanced Machine Learning and Technologies and Applications (AMLTA2022). AMLTA 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-031-03918-8_12

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