Urban energy management system based on intelligent linker

Introduction: The use of machine learning (ML) approaches to improve energy utilization in smart urban environments has garnered significant attention in recent years.Objective: This research presents an innovative structure called a bi-fold mechanism-driven convolutional deep network (BMCDN) for estimating the energy performance of urban public facilities in urban energy management systems.Methods: The suggested method includes two significant phases: (1) feature extraction and fusion, and (2) energy significance estimation. The attention fine-tuned ResNet (N1) processes street-view images to evaluate anticipated market significance levels, while the attention-based Bi-LSTM (N2) integrates cross-domain features using input attention. A decision tree (DT) is used to combine and evaluate the fused information and estimated values, serving as the energy value estimator to determine energy values. Data gathered related to public facilities' energy efficiency from various sources is used to analyze the effectiveness of the suggested framework. Results: The research presents an analysis of the performance gains using image-only representations and a proposed approach with morphological traits. The findings demonstrate that incorporating smart urban-related façade images improves the accuracy of the proposed framework and highlights the connection between energy usage and public facilities.Conclusions: This study shows the potential for significant precision along with rapid inference time in predicting the energy performance of urban public facilities by combining data from numerous sources. Keywords: Urban energy management; public facilities; machine learning (ML); multi-source data; bi-fold mechanism-driven convolutional deep network (BMCDN)