Energy Consumption Optimization Method for Intelligent IoT Buildings Based on Big Data Analysis

As an important symbol of modern urban development, intelligent buildings have increasingly become a bottleneck restricting sustainable development due to their high energy consumption. This not only leads to huge economic costs, but also exacerbates environmental burdens, resulting in a large amount of waste of non renewable resources. Faced with this challenge, the rapid development of Internet of Things (IoT) technology is like a clear current, bringing unprecedented opportunities for energy management in smart buildings. This technological innovation has laid a solid foundation for achieving seamless integration and data sharing among various systems within the building. In order to further explore the potential of IoT, this article innovatively proposes an intelligent IoT building energy consumption optimization method based on big data analysis. This method collects and integrates massive data generated by various systems within the building, uses advanced big data analysis techniques to deeply explore the hidden patterns and patterns behind the data, and accurately identifies peak energy consumption periods, inefficient operation links, and potential energy-saving spaces. The experimental results show that this method not only effectively reduces the overall energy consumption of buildings, but also significantly improves energy efficiency, opening up a new path for the green and low-carbon development of smart buildings.