Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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A Novel Framework Based on CNN-LSTM Neural Network for Prediction of Missing Values in Electricity Consumption Time-Series Datasets

  • Hussain, Syed Nazir (Faculty of Engineering and Technology, Multimedia University) ;
  • Aziz, Azlan Abd (Faculty of Engineering and Technology, Multimedia University) ;
  • Hossen, Md. Jakir (Faculty of Engineering and Technology, Multimedia University) ;
  • Aziz, Nor Azlina Ab (Faculty of Engineering and Technology, Multimedia University) ;
  • Murthy, G. Ramana (Dept. of Electronic and Computer Engineering, Vignan's Foundation) ;
  • Mustakim, Fajaruddin Bin (Universiti Tun Hussein Onn Malaysia (UTHM))
  • Received : 2020.12.17
  • Accepted : 2021.03.05
  • Published : 2022.02.28
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Abstract

Adopting Internet of Things (IoT)-based technologies in smart homes helps users analyze home appliances electricity consumption for better overall cost monitoring. The IoT application like smart home system (SHS) could suffer from large missing values gaps due to several factors such as security attacks, sensor faults, or connection errors. In this paper, a novel framework has been proposed to predict large gaps of missing values from the SHS home appliances electricity consumption time-series datasets. The framework follows a series of steps to detect, predict and reconstruct the input time-series datasets of missing values. A hybrid convolutional neural network-long short term memory (CNN-LSTM) neural network used to forecast large missing values gaps. A comparative experiment has been conducted to evaluate the performance of hybrid CNN-LSTM with its single variant CNN and LSTM in forecasting missing values. The experimental results indicate a performance superiority of the CNN-LSTM model over the single CNN and LSTM neural networks.

Keywords

Acknowledgement

The research has been supported by Telecom Malaysia (TM) Research & Development (R&D) Institute under the grant number MMUE/190007.02.

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