29-08-2019 дата публикации
Номер: US20190265768A1
Автор:
Kaile ZHOU,
Zhifeng GUO,
Shanlin YANG,
Pengtao LI,
Lulu WEN,
Xinhui LU,
ZHOU KAILE,
GUO ZHIFENG,
YANG SHANLIN,
LI PENGTAO,
WEN LULU,
LU XINHUI,
ZHOU, Kaile,
GUO, Zhifeng,
YANG, Shanlin,
LI, Pengtao,
WEN, Lulu,
LU, Xinhui
Принадлежит:
The disclosure provides a method, a system and a storage medium for predicting power load probability density based on deep learning. The method comprises: S, collecting power load data of a user, meteorological data and air quality data in a preset historical time period, and dividing the collected data into a training set and a test set; S, determining a deep learning model for predicting power load; S, inputting the test set into the deep learning model for predicting power load, and obtaining power load prediction data of the user at different quantile points in a third time interval; S, performing kernel density estimation and obtaining a probability density curve of the power load of the user in the third time interval. 1. A method for predicting power load probability density based on deep learning , the method is executed by a computer , and the method comprising:{'b': '101', 'S, collecting power load data of a user, meteorological data and air quality data in a preset historical time period, and dividing the collected data into a training set and a test set, wherein the historical time period comprises a first time interval and a second time interval which is later than the first time interval, and the training set is data in the first time interval, the test set is data in the second time interval;'}{'b': '102', 'S, determining a deep learning model for predicting power load according to the training set and the test set;'}{'b': '103', 'S, inputting the test set into the deep learning model for predicting power load, and obtaining power load prediction data of the user at different quantile points in a third time interval, wherein the third time interval is a preset time interval in a future time period;'}{'b': '104', 'S, performing kernel density estimation according to power load prediction data of the user at different quantile points in the third time interval, and obtaining a probability density curve of the power load of the user in the third time ...
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