一种融合指数平滑和梯度升压的短期负荷预测方法OA

In order to improve the accuracy of load forecasting in regional high load scenarios,while meeting the operational maintenance and protection needs of short-term distribution networks in small regional scenarios,and achieve operation and maintenance protection of short-term distribution networks in small regional scenarios,a short-term load forecasting algorithm that integrates exponential smoothing method and gradient boosting is designed.In this algorithm,the exponential smoothing is used to preprocess historical load data to reduce the impact of random load fluctuations,and a gradient boosting mechanism is established.The gradient boosting algorithm is used to learn features from the preprocessed data,enhancing its ability to handle nonlinear relationships and high-dimensional data.At the same time,various control factors are introduced to achieve accurate prediction of short-term distribution network loads.The real electricity consumption data from a certain university is collected as a sample dataset for short-term forecasting numerical experiments,and compared horizontally with similar load forecasting algorithms.The results show that the proposed algorithm has a load forecasting accuracy of 99.1%and a prediction accuracy of up to 99.3%,effectively improving the accuracy and reliability of the prediction,and providing data support for the smooth operation of the distribution network in the region.