摘要
随着改革的不断深化,市场对电力的需求也在不断的变化。现阶段对电力系统短期负荷预测的要求逐渐提升,传统负荷预测方法的精度并不能满足应用需求,因此,提出一种基于贝叶斯分类的电网系统短期负荷预测方法,首先要建立电网系统短期负荷预测指标,通过预测指标建立基于贝叶斯分类的预测模型,然后运用基于贝叶斯分类的预测模型,在模型计算之后对数据进行并行化处理,最终得到电网系统短期负荷预测结果。通过测试实验证明该短期负荷预测方法比传统方法预测的精度更高。
As the deepening of reform,the market demand for electricity is also changing.The requirements for power system short-term load forecasting are gradually improved at this stage.The accuracy of traditional load forecasting methods can not meet the application requirements.Therefore,a power system short-term load forecasting method based on Bayesian classification is proposed.Firstly,we should establish the short-term load forecasting index of the power grid system,establish the forecasting model based on Bayesian classification through the forecasting index,use the forecasting model based on Bayesian classification,parallelize the data after the model calculation,and finally get the short-term load forecasting results of the power grid system.The test results show that the short-term load forecasting method has higher accuracy than the traditional method.
作者
邓智广
DENG Zhiguang(Foshan Power Supply Bureau of Guangdong Power Grid Co.,Ltd.,Foshan 528000,Guangdong,China)
出处
《电气传动自动化》
2021年第5期28-31,共4页
Electric Drive Automation
