A Hybrid CNN-LSTM Model for Predicting Energy Consumption and Production Across Multiple Energy Sources

Year 2024, Volume: 5 Issue: 2, 63 - 73

Mahmut Kaya , Anıl Utku , Yavuz Canbay

https://doi.org/10.55195/jscai.1577431

Abstract

This study utilizes a robust dataset provided by Energy Exchange Istanbul (EXIST), a leading authority in energy data, which contains hourly energy consumption and production data from 01/01/2018 to 31/12/2023 across Turkey. Various machine learning and deep learning methods such as linear regression (LR), random forest (RF), support vector machines (SVR), convolutional neural networks (CNN), long short-term memory networks (LSTM), and the proposed hybrid CNN-LSTM model are applied to predict energy consumption and production more accurately. This study transforms time series data into a regression problem using the sliding window method. The experimental results show that the hybrid CNN-LSTM model outperforms the other models in forecasting total energy consumption and natural gas, hydro dam, lignite, hydro river, wind, and fuel oil production. The CNN-LSTM model achieved the lowest RMSE and MAE values and the highest R² scores. The success of the proposed hybrid approach is due to the combination of CNN's ability to identify local patterns and LSTM's ability to learn long-term dependencies. This study demonstrates the hybrid CNN-LSTM model's effectiveness in accurately forecasting energy consumption and production. It makes an important contribution to more efficient use of energy resources.

Keywords

Energy Prediction, Deep Learning, CNN, LSTM

Ethical Statement

The authors declare that this study complies with research and publication ethics.

Supporting Institution

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Thanks

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