BI-LSTM-LSTM Based Time Series Electricity Consumption Forecast for South Korea

• Published in: Advances in Artificial Intelligence and Applied Cognitive Computing pp. 897 - 902
• Main Authors: Gul, Malik Junaid Jami, Firmansyah, M. Hafid, Rho, Seungmin, Paul, Anand
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 2021
• Series: Transactions on Computational Science and Computational Intelligence
• Subjects: Bi-LSTM; Demand and supply; Electricity consumption; Forecasting; LSTM; RNN
• ISBN: 9783030702953; 3030702952
• ISSN: 2569-7072; 2569-7080
• DOI: 10.1007/978-3-030-70296-0_71

Electricity is playing an important factor to drive the economy of the nation. Every country is trying to find fuel resources alternative to gasoline. Electricity is the promising resource because of low carbon footprints as compared to other fuel resources. Right now, biggest electricity consumers are households and industries. Forecasting the need of the respective sectors, governments can decide the future direction. This can result in better planning. As the second phase of our project, we have tested LST with Bi-LSTM to check the overall performance of the neural network model. Dataset is provided by Korea Electric power supply to get insights for metropolitan city like Seoul. Dataset is in time series so we require to analyze dataset with time distributed machine learning models that can support time series dataset. This study provides experimental results from the proposed models. Our model shows RMSE scores of 0.15 on training and 0.19 for testing with tuning hyperparameters of the model to optimum level.