A CNN-LSTM-Twisted Power Optimization Scheme for LoRaWAN-Based Intelligent Streetlight System

Abstract

With the development of smart cities, energy saving has become an important goal, and intelligent streetlight systems (ISLS) play an important role in reducing power consumption. For energy saving purposes, this paper proposes an ISLS based on LoRaWAN technology, incorporating a twisted Artificial Intelligence (AI) Model that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, intertwining their strengths to achieve automatic brightness control and power consumption optimization. The CNN in the model is responsible for accurately analysing current brightness and traffic flow levels, while the LSTM is used to predict future visibility changes. With the combination, the system can be flexible in dynamic environments and cope with changes in severe weather by adjusting the streetlights’ brightness in advance. This research utilizes the traffic road image dataset from the Kaggle platform and the weather dataset from the Szeged area to simulate the real environment. During the 100-day simulation, the average daily power consumption of ISLS was reduced by 13.98% compared to the typical energy-saving method, which shows relatively high adaptability and energy saving effect in the complex dynamic environment. Hence, this paper provides an efficient energy-saving solution for intelligent streetlights and an innovative idea for energy management in smart cities.