Uncertain Data Processing Algorithm for Base Station Energy Consumption: Optimized Branch Energy Consumption Algorithm for Multi-Functional DC Power Meter Devices

Uncertain Data Processing Algorithm for Base Station Energy Consumption: Optimized Branch Energy Consumption Algorithm for Multi-Functional DC Power Meter Devices

연구 분야: Infrastructure

학회: EITCE '23: Proceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering

The current base station management faces challenges such as imprecise information perception, a lack of precise prediction techniques for load and energy consumption, and the absence of refined optimization methods for multi-source comprehensive scheduling. It can only achieve a quantitative complementarity of energy on the supply side, the grid side, and the load side, without considering the differences in energy quality of various forms of energy and their optimal scheduling in conversion, transmission, storage, and utilization. Simultaneously, there is a severe deficiency in cross-temporal and spatial allocation and utilization of energy, as well as the use of edge computing and big data analytics for precise prediction and optimization scheduling. This has resulted in low overall energy utilization efficiency, high carbon emissions, and other issues. There is an urgent need to break through the key technologies of accurate perception, precise prediction, precise scheduling, and fine control in the energy Internet of Things system for base stations. The project team has put forth a scientifically sound solution, addressing issues related to precise perception of base station status, accurate load prediction, fine optimization of energy management, and precise control of comprehensive energy systems. They have proposed the "Uncertain Data Processing Algorithm for Base Station Energy Consumption" to tackle and solve the challenge of precise load prediction in energy IoT based on high-noise, low-quality data.