A Prediction Method for the Microclimate and Ampacity of Overhead Transmission Lines Based on EMD-LSTM and Adjacent Meteorological Stations

To ensure the large-scale integration of renewable energy, it is essential to explore the load capacity of transmission lines fully, requiring precise forecast of the microclimate and ampacity of transmission lines. However, the traditional prediction methods based on the meteorological stations could not capture the specific geographic features of the long transmission lines, and the other prediction methods based on intelligence algorithms and the microclimate data of the transmission lines don't have the strong prediction ability used in meteorological stations, making them both lacking forecast accuracy for the microclimate and ampacity of transmission lines. In this paper, a novel prediction method combining the merits of these traditional forecast techniques is developed. It uses the Empirical Mode Decomposition for the decomposition of the sequence of meteorological data error, and the Long Short-Term Memory neural network is applied to construct the model for the error prediction in the microclimate data of the transmission tower.