Power Monitoring and Forecasting Using ANN with Utilisation of a Gateway Server and Digital Power Meter

https://doi.org/10.65136/jati.v4i1.277
Power monitoring Forecasting systems ANN- based monitoring ARIMA based forecasting LSTM

Authors

  • Ahmed Visam Thaufeeq School of Engineering Asia Pacific University of Technology and Innovation (APU) Kuala Lumpur, Malaysia
  • Chandrasekharan Nataraj
    chandrasekharan@staffemail.apu.edu.my
    School of Engineering Asia Pacific University of Technology and Innovation (APU) Kuala Lumpur, Malaysia
  • Freddy Tan Kheng Suan School of Engineering Asia Pacific University of Technology and Innovation (APU) Kuala Lumpur, Malaysia
Vol. 4 No. 4 (2020)
Original Research
January 27, 2026

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The power monitoring and forecasting system using the ANN technique is presented. It focuses on the implementation of ARIMA, LSTM, and ANN networks-based power monitoring and forecasting. Three energy forecasting models are simulated and tested to evaluate the accuracy of the power monitoring and forecasting. ANN was trained using a Bayesian Regularization backpropagation algorithm. The ANN approach has proved to produce better results than ARIMA and LSTM with an RMSE value of 4.631.