Journal of Engineering Research

With the deepening of smart grid construction, the accurate prediction and efficient management of power load in power transmission and distribution projects have become the key link to ensure the safe and economical operation of the power system. This paper focuses on the research on power load forecasting and management in power transmission and distribution engineering, systematically analyzes the principles and application scenarios of common load forecasting methods, proposes improved prediction schemes based on intelligent algorithms and traditional models, and constructs a load management system based on big data. By introducing the time series model and machine learning algorithm, a mathematical model of load forecasting is established, and the empirical analysis is carried out by taking a regional power grid as an example to verify the effectiveness of the method. The results show that the prediction model fusing multi-source data can control the load prediction error within 5%, and the management strategy based on demand-side response can significantly improve the operation efficiency of the power grid. This paper provides a theoretical reference and practical guidance for the load management of power transmission and distribution engineering.

Power transmission and distribution engineering; Power load forecasting; Time series analysis; Demand-side management; Intelligent algorithms