Investigating the Performance of RNN Model to Forecast The Electricity Power Consumption in Guangzhou China

Irni Hamiza binti Hamzah, Azman Ab Malik, Han Mingying, Siti Hazyanti Mohd Hashim, Noormadinah Allias, Hasanul Fahmi

Abstract


The project initiatives to create a reliable prediction model for power loads in Guangzhou, China. The power industry is facing issues due to rapid market growth and the necessity for better grid management, prompting this response. The project aimed to develop a Jupiter-based power load forecasting model to predict future trends and improve grid efficiency and reliability. Estimate Guangzhou's power consumption using ARIMA, LSTM, and RNN models to identify demand trends and potential shortages. Optimize the models to fit with the city's electrical workflow. The RNN model was found to be optimal for the project, with an R² value of 0.85, RMSE of 0.13107, MAE of 0.0176, and MSE of 0.000512. The project improved power resource planning and management, selected an acceptable forecasting model (RNN), and contributed to forecasting technology developments. The study identified limits in historical data availability and quality, as well as external influences affecting the studies. RNN models can help optimize resource allocation and improve energy planning. The project in data science and analytics provides practical knowledge and insights from the power industry, in addition to presenting creative concepts.

Keywords


Autoregressive Integrated Moving Average; Long Short-Term Memory; Recurrent Neural Network; Root Mean Square Error; Mean Absolute Error; Mean Square Error

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DOI: http://doi.org/10.11591/ijres.v14.i2.pp%25p

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