DOI: 10.53508/ijiam.1842160 ISSN: 2667-6990

Comparative Study of CNN–LSTM and ConvLSTM Models for Short-Term Power Forecasting in Smart Street Lighting Systems

Mouaadh Yaichi
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Smart street lighting systems (SSLs) have emerged as an effective solution for reducing energy consumption and promoting environmental sustainability by providing adaptive lighting that responds to user demand without compromising safety or security, thereby improving overall efficiency. The application of computational techniques, including machine learning and deep learning, is essential for enhancing smart street lighting systems. These methods enable the prediction of power consumption, allowing dynamic adjustment of lighting levels based on real-time demand. As a result, integrating advanced algorithms improves energy efficiency, reduces operational costs, and contributes to sustainable urban lighting.This study investigates the application of deep learning models, namely Convolutional Neural Network with Long Short-Term Memory (CNN–LSTM) and Convolutional LSTM (ConvLSTM), for predicting power consumption in a smart lighting system. A univariate time-series dataset of power consumption, collected over a seven-day period, was employed. The methodology utilized a one-step-ahead prediction strategy based on actual values. Experimental results showed that both models achieved satisfactory predictive performance.

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