Dynamic energy consumption monitoring and scheduling for green buildings: A comprehensive approach

Traditional green building energy efficiency management methods lack real-time optimization and intelligent management and lack effective coordination between systems, resulting in energy waste and limited building energy efficiency optimization effects. This paper proposes a comprehensive approach to solve this problem, combining dynamic energy consumption monitoring, intelligent scheduling, multi-objective optimization, and prediction adjustment to construct an efficient building energy efficiency optimization framework. The building energy consumption data are collected in real time through the Internet of Things (IoT) technology and sensor networks, and the Kalman filter algorithm is used to fuse and correct the data to ensure the accuracy of the monitoring data. The energy consumption prediction model is based on historical energy consumption data and external environmental factors. Long short-term memory (LSTM) neural networks are used to predict future energy consumption demand and provide data support for real-time scheduling. Based on real-time energy consumption data and prediction results, fuzzy control algorithms are used to dynamically adjust the operating strategies of various energy systems in the building to ensure efficient operation of the systems under different conditions. Meanwhile, the particle swarm optimization (PSO) algorithm is used to solve the multi-objective scheduling problem to achieve the global objectives of energy conservation, cost reduction, and comfort optimization. The scheduling strategy adopts a dynamic approach based on priority to flexibly allocate energy resources to ensure the coordinated operation of various energy systems in the building. A three-month comparative experiment is conducted, and the method in this paper is effective in improving the energy efficiency of green buildings, reducing energy consumption, and optimizing system coordination. Experimental results demonstrate that the average energy consumption reduction rate is 4.63%, the comfort retention rate is improved, and the system coordination efficiency and response speed are significantly improved. This approach provides an effective solution for green building energy efficiency management, breaks through the limitations of traditional methods, and has substantial practical application value. The method can be implemented by integrating IoT devices and energy management systems in smart buildings. Existing systems can be upgraded to add sensors and IoT connections to enable real-time data collection. LSTM prediction models and PSO algorithms can be deployed to ensure efficient computation and real-time response, thus enabling applications in a variety of scenarios.