A Novel Deep Reinforcement Learning Framework for Energy Management and Prediction in Smart Cities

ABSTRACT Machine learning and deep learning algorithms have recently been progressively integrated into business intelligence for smart city management. Predicting smart city energy utilisation is crucial for sustainable growth and resource efficiency and become essential due to the growing challenges and demands on raw energy resources. Traditional methods for forecasting energy demand were manual and often yielded poor results. However, advancements in machine learning and deep learning have introduced new approaches to energy management within the context of business intelligence. This research presents an advanced prolonged deep reinforcement learning model incorporating Monte Carlo learning, which is compared against the convolutional neural network approach using a public sector energy dataset, including steel industry energy utilisation. The study evaluates the performance of the proposed prolonged deep reinforcement learning and convolutional neural network models based on key metrics such as accuracy and mean absolute percentage error. Data analysis was conducted using the SPSS tool, encompassing graphical illustrations, group statistics, and independent tabulations. Results indicate that the prolonged deep reinforcement learning model achieved a prediction accuracy of 96.613% and a mean absolute percentage error of 3.387%. Consequently, this efficient prediction model is well‐suited for addressing the future energy demands of smart cities through business intelligence.