公开(公告)号：US20200161867A1

公开(公告)日：2020-05-21

申请号：US16537610

申请日：2019-08-11

Applicant: Hefei University of Technology

Inventor： Kaile ZHOU ,  Lulu WEN ,  Shanlin YANG

IPC: H02J3/38 ,  G06N3/04 ,  G06N3/00 ,  G06N3/08 ,  G06Q10/06 ,  G06Q50/06

Abstract: The present invention provides a method, system and storage medium for load dispatch optimization for residential microgrid. The method includes collecting environmental data and time data of residential microgrid in preset future time period; obtaining power load data of residential microgrid in future time period by inputting environmental data and time data into pre-trained load forecasting model; obtaining photovoltaic output power data of residential microgrid in future time period by inputting environmental data and time data into pre-trained photovoltaic output power forecasting model; determining objective function and corresponding constraint condition of residential microgrid in future time period, where optimization objective of objective function is to minimize total cost of residential microgrid; obtaining load dispatch scheme of residential microgrid in future time period by solving objective function with particle swarm algorithm. The invention can provide load dispatch scheme suitable for current microgrid and reduce operating cost of residential microgrid.

公开(公告)号：US20190265768A1

公开(公告)日：2019-08-29

申请号：US16284064

申请日：2019-02-25

Applicant: Hefei University of Technology

Inventor： Kaile ZHOU ,  Zhifeng GUO ,  Shanlin YANG ,  Pengtao LI ,  Lulu WEN ,  Xinhui LU

IPC: G06F1/28 ,  G06N20/00 ,  G06N7/00

Abstract: The disclosure provides a method, a system and a storage medium for predicting power load probability density based on deep learning. The method comprises: S101, 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; S102, determining a deep learning model for predicting power load; S103, 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; S104, performing kernel density estimation and obtaining a probability density curve of the power load of the user in the third time interval.

公开(公告)号：US20180294819A1

公开(公告)日：2018-10-11

申请号：US15947857

申请日：2018-04-08

Applicant: Hefei University of Technology

Inventor： Kaile ZHOU ,  Lulu WEN ,  Shanlin YANG ,  Xinhui LU ,  Zhen SHAO ,  Li SUN

IPC: H03M7/30 ,  H04L29/06 ,  G01D4/00 ,  G01R22/06

CPC classification number: H03M7/3084 ,  G01D4/002 ,  G01R22/063 ,  H03M7/3059 ,  H04L69/04 ,  Y02B90/24 ,  Y04S20/30

Abstract: The present invention relates to a method and a system for compressing data from a smart meter. The method comprises: LZ-encoding electricity load data collected by the smart meter whenever the smart meter collects the electricity load data; storing the LZ-encoded electricity load data in a temporary database through a smart grid communication channel; reading the electricity load data from the temporary database every preset second duration, wherein the read electricity load data is electricity load data stored in the temporary database within the second duration before a corresponding reading time point; and LZ-decoding the read electricity load data, SAX-compressing the LZ-decoded electricity load data, and storing the SAX-compressed electricity load data in a data center. The present invention has high compression rate, reduces the transmission burden for communication lines and storage burden for the data center, and improves the efficiency of smart electricity data analysis and mining.