Abstract
Icing disasters on power grid transmission lines can easily lead to major accidents, such as wire breakage and tower overturning, that endanger the safe operation of the power grid. Short-term prediction of transmission line icing relies to a large extent on accurate prediction of daily minimum temperature. This study therefore proposes a LightGBM low-temperature prediction model based on LassoCV feature selection. A data set comprising four meteorological variables was established, and time series autocorrelation coefficients were first used to determine the hysteresis characteristics in relation to the daily minimum temperature. Subsequently, the LassoCV feature selection method was used to select the meteorological elements that are highly related to minimum temperature, with their lag characteristics, as input variables, to eliminate noise in the original meteorological data set and reduce the complexity of the model. On this basis, the LightGBM low-temperature prediction model is established. The model was optimized through grid search and crossvalidation and validated using daily minimum surface temperature data from Yongshan County (station number 56489), Zhaotong City, Yunnan Province. The root mean square error, MAE, and MAPE of the model minimum temperature prediction after feature selection are shown to be 1.305, 0.999, and 0.112, respectively. These results indicate that the LightGBM prediction model is effective at predicting low temperatures and can be used to support short-term icing prediction.
Highlights
Evidence from power grid operation shows that wire breaks and tower toppling accidents, caused by transmission line icing, lead to great damage to the transmission lines themselves and adversely affect the safe and stable operation of the power grid system more generally [1]
Aiming to address the difficulty of traditional prediction methods to learn from large amounts of data, and their inability to fully consider the impact of multiple meteorological variables and their own time correlations on temperature changes, this paper proposes a LightGBM lowtemperature prediction model based on LassoCV feature selection
LightGBM is derived from Reference [7] and related open source tools. e light gradient boosting (LGB) model is an efficient implementation of the classic gradient boosting decision tree (GBDT) model. e LGB model handles the classification, regression, and ranking problems in machine learning
Summary
Evidence from power grid operation shows that wire breaks and tower toppling accidents, caused by transmission line icing, lead to great damage to the transmission lines themselves and adversely affect the safe and stable operation of the power grid system more generally [1]. Minimum temperature data are generally based on time series, and most traditional prediction methods use univariate time series modeling [3]. Mathematical Problems in Engineering analysis, a back propagation (BP) neural network, and a radial basis function (RBF) neural network, to establish a temperature prediction model [5] This method considers the influence of multiple meteorological variables on temperature, it does not consider the time series characteristics of those meteorological variables. Aiming to address the difficulty of traditional prediction methods to learn from large amounts of data, and their inability to fully consider the impact of multiple meteorological variables and their own time correlations on temperature changes, this paper proposes a LightGBM lowtemperature prediction model based on LassoCV feature selection. E use of this method can improve the accuracy and speed of prediction and provides a sound basis for supporting the production of icing prediction data
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