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A Hybrid Deep Learning Model to Estimate the Future Electricity Demand of Sustainable Cities

Author

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  • Gülay Yıldız Doğan

    (Department of Industrial Engineering, Faculty of Engineering, Bursa Uludağ University, 16059 Bursa, Türkiye)

  • Aslı Aksoy

    (Department of Industrial Engineering, Faculty of Engineering, Bursa Uludağ University, 16059 Bursa, Türkiye)

  • Nursel Öztürk

    (Department of Industrial Engineering, Faculty of Engineering, Bursa Uludağ University, 16059 Bursa, Türkiye)

Abstract

Rapid population growth, economic growth, and technological developments in recent years have led to a significant increase in electricity consumption. Therefore, the estimation of electrical energy demand is crucial for the planning of electricity generation and consumption in cities. This study proposes a hybrid deep learning model that combines convolutional neural network (CNN) and long short-term memory (LSTM) techniques, both of which are deep learning techniques, to estimate electrical load demand. A hybrid deep learning model and LSTM model were applied to a dataset containing hourly electricity consumption and meteorological information of a city in Türkiye from 2017 to 2021. The results were evaluated using mean absolute percent error (MAPE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R 2 ) metrics. The proposed CNN-LSTM hybrid model was compared to the LSTM model, with lower MAPE, MAE, and RMSE values. Furthermore, the CNN-LSTM model exhibited superior prediction performance with an R 2 value of 0.8599 compared to the LSTM model with an R 2 value of 0.8086. These results demonstrate the effectiveness of the proposed deep learning model in accurately estimating future electrical load demand to plan electricity generation for sustainable cities.

Suggested Citation

  • Gülay Yıldız Doğan & Aslı Aksoy & Nursel Öztürk, 2024. "A Hybrid Deep Learning Model to Estimate the Future Electricity Demand of Sustainable Cities," Sustainability, MDPI, vol. 16(15), pages 1-16, July.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:15:p:6503-:d:1445970
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    References listed on IDEAS

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    1. Fan, Guo-Feng & Peng, Li-Ling & Hong, Wei-Chiang, 2018. "Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model," Applied Energy, Elsevier, vol. 224(C), pages 13-33.
    2. Elamin, Niematallah & Fukushige, Mototsugu, 2018. "Modeling and forecasting hourly electricity demand by SARIMAX with interactions," Energy, Elsevier, vol. 165(PB), pages 257-268.
    3. García-Ascanio, Carolina & Maté, Carlos, 2010. "Electric power demand forecasting using interval time series: A comparison between VAR and iMLP," Energy Policy, Elsevier, vol. 38(2), pages 715-725, February.
    4. Apadula, Francesco & Bassini, Alessandra & Elli, Alberto & Scapin, Simone, 2012. "Relationships between meteorological variables and monthly electricity demand," Applied Energy, Elsevier, vol. 98(C), pages 346-356.
    5. Fan, Cheng & Wang, Jiayuan & Gang, Wenjie & Li, Shenghan, 2019. "Assessment of deep recurrent neural network-based strategies for short-term building energy predictions," Applied Energy, Elsevier, vol. 236(C), pages 700-710.
    6. Behm, Christian & Nolting, Lars & Praktiknjo, Aaron, 2020. "How to model European electricity load profiles using artificial neural networks," Applied Energy, Elsevier, vol. 277(C).
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