【摘要】 柔性负荷可调区间预测,将非侵入式负荷辨识和大数据挖掘相结合,大数据挖掘得到柔性负荷功率的概率特性曲线,再基于非侵入式的方法在线辨识柔性负荷实际开启时段,在柔性负荷功率的概率统计特性曲线上截取位于该段时间窗口内的曲线,最后对多户家庭进行聚合,得到柔性负荷可调区间预测实用模型。相比于传统安装检测设备的方法,非侵入式辨识更加注重用户的隐私,也节约了安装设备的成本。再通过Apriori规则量化得到用能习惯和影响因素之间的关系,最终得出居民用户柔性负荷功率的概率特性曲线。将二者结合便可以得到柔性负荷可调区间预测模型。更多还原

【Abstract】 Flexible load adjustable interval prediction combines non-intrusive load identification and big data mining,big data mining obtains the probability characteristic curve of flexible load power,and then based on non-intrusive method to identify the actual opening period of flexible load in flexible load. The probabilistic characteristic curve of power intercepts the curve located in the time windowof the period,and finally aggregates the multi-family households to obtain a flexible load-adjustable interval prediction utility model. Compared with the traditional method of installing the detection device,the non-intrusive identification pays more attention to the privacy of the user and also saves the cost of installing the device. Then,the relationship between the energy habits and the influencing factors is obtained by Apriori rule quantification,and finally the probability characteristic curve of the flexible load power of the resident users is obtained. Combining the two can obtain the flexible load adjustable interval prediction model.更多还原