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

Gülay Yıldız Doğan (), Aslı Aksoy and Nursel Öztürk
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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

Sustainability, 2024, vol. 16, issue 15, 1-16

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.

Keywords: electrical energy demand forecast; LSTM; CNN; CNN-LSTM hybrid model; deep learning model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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