基于深度条件概率密度函数的居民电力负荷预测
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引用本文:陈伟,赵裕童.基于深度条件概率密度函数的居民电力负荷预测[J].电网与清洁能源,2022,38(5):36~41
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作者单位
陈伟 国网上海市区供电公司 
赵裕童 国网上海市区供电公司 
基金项目:国家电网有限公司科技项目(C4761618K001)
中文摘要:居民电力负荷预测主要用于电力调度工作的停电计划,以提高供电可靠度及居民用户满意度。由于电力数据量大且不确定性因素过多,对其负荷进行预测的难度较大。现有的电力负荷预测方法无法获取电力数据的自由度数值,导致负荷预测过程稳定性差、预测结果精度低。提出一种基于深度条件概率密度函数的居民电力负荷预测方法。引入四次方核函数,得出随时间变化下居民电力负荷数据的观测值与预测值间的变量关系;通过高斯回归方程使得预测向量值符合正态分布;利用交叉验证方法提取预测值的最优自由度,通过借自由度确定分位点,根据对比分析结果确定下一随机变量的预测数据分位点,实现居民电力负荷的预测。仿真实验证明,所提方法得出的电力负荷波动结果与实测结果相吻合,预测误差可控制在0.001~0.437 MW。说明该方法预测准确性高,可为电力决策提供有效帮助。
中文关键词:深度条件概率密度函数  四次方核函数  线性函数  自由度  负荷预测
 
Residential Power Load Forecasting Based on the Depth Conditional Probability Density Function
Abstract:Residential power load forecasting is mainly used in the power outage scheme in the power dispatching to improve power supply reliability and residential customer satisfaction. Because of the large amount of power data and the strong uncertainty factors, it is often difficult to predict the load. The existing power load forecasting methods cannot obtain the degree of freedom of power data, resulting in poor stability of load forecasting process and low accuracy of prediction results. To this end, a residential power load forecasting method based on depth conditional probability density function is proposed in this paper. By introducing the fourth power kernel function, the variable relationship between the observed value and the predicted value of residential power load data with time change is obtained. The predicted vector values conform to normal distribution by gaussian regression equation. The optimal degree of freedom of the predicted value is extracted by the cross-validation method, the quantile is determined by the degree of freedom, and the prediction data quantile of the next random variable is determined according to the comparative analysis results, so as to realize the prediction of residential power load. The simulation results show that the power load fluctuation results obtained by the proposed method are consistent with the measured values, and the error can be controlled within 0.001 MW - 0.437 MW. The experimental data suggests that this method has high prediction accuracy and can provide effective help for power decision-making.
keywords:depth conditional probability density function  quartic square kernel function  linear function  degree of freedom  load forecasting
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