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公开(公告)号：US11409347B2
公开(公告)日：2022-08-09
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
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.
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Patent Agency Ranking
公开(公告)号：US10211851B2
公开(公告)日：2019-02-19
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
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.
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公开(公告)号：US12065054B2
公开(公告)日：2024-08-20
申请号：US17490145
申请日：2021-09-30
Applicant: Hefei University of Technology
Inventor： Kaile Zhou , Dingding Hu , Lanlan Li , Xinhui Lu , Zhineng Fei
Abstract: The invention provided an electric vehicle charging scheduling method, apparatus and system based on cloud-edge collaboration, a storage medium and an electronic device. In the present invention, a charging request of an electric vehicle user is accepted and processed by an edge computing unit, and a target charging station for a to-be-charged electric vehicle is determined with a minimum traveling cost as a target, so that a data transmission distance is reduced, and the electric vehicle user is timely assisted in selecting the target charging station and completing a charging appointment. After the charging appointment is made, charging data is uploaded to a charging optimization scheduling model pre-trained by a cloud platform for obtaining an electric vehicle charging scheduling strategy, so that powerful cloud platform computing abilities and rapid response advantages of the edge computing unit are fully utilized, the problem of network congestion is avoided, and timeliness is improved.
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公开(公告)号：US11721994B2
公开(公告)日：2023-08-08
申请号：US17230952
申请日：2021-04-14
Applicant: Hefei University of Technology
Inventor： Kaile Zhou , Zenghui Zhang , Shanlin Yang , Jianling Jiao , Xinhui Lu
IPC: H02J7/00 , G01R31/392 , G01R31/382
CPC classification number: H02J7/0048 , G01R31/382 , G01R31/392 , H02J7/005
Abstract: Provided are a method and a system for optimizing charging and discharging behaviors of a BESS based on a SOH, relating to charging and discharging optimization. The number of cycles of the battery pack and corresponding DODs are obtained based on the curve of the SOC of the battery pack. Then, the SOH of the battery pack is obtained. A charging index sequence and a discharging index sequence of battery packs are obtained based on the SOH, the SOC and a charging and discharging state of the battery pack. The optimal number of the charging and discharging battery packs and optimal DODs are determined. Charging and discharging tasks are carried out according to the charging and discharging index sequences of the battery packs based on the optimal number of the charging and discharging battery packs and the optimal DODs.
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5.发明授权公开(公告)号：US11581740B2
公开(公告)日：2023-02-14
申请号：US16537610
申请日：2019-08-11
Applicant: Hefei University of Technology
Inventor： Kaile Zhou , Lulu Wen , Shanlin Yang
IPC: G06N3/00 , G06N3/04 , G06N3/08 , G06Q10/06 , H02J3/38 , G06N3/006 , G06N3/084 , G06Q10/0631 , G06Q50/06 , H02J3/00
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.
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公开(公告)号：US10026134B2
公开(公告)日：2018-07-17
申请号：US15597598
申请日：2017-05-17
Applicant: Hefei University of Technology
Inventor： Kaile Zhou , Xinhui Lu , Shanlin Yang , Li Sun , Chi Zhang , Zhen Shao
Abstract: A charging and discharging scheduling method for electric vehicles in microgrid under time-of-use price includes: determining the system structure of the microgrid and the characters of each unit; establishing the optimal scheduling objective function of the microgrid considering the depreciation cost of the electric vehicle (EV) battery under time-of-use price; determining the constraints of each distributed generator and EV battery, and forming an optimal scheduling model of the microgrid together with the optimal scheduling objective function of the microgrid; determining the amount, starting and ending time, starting and ending charge state, and other basic calculating data of the EV accessing the microgrid under time-of-use price; determining the charge and discharge power of the EV when accessing the grid, by solving the optimal scheduling model of the microgrid with a particle swarm optimization algorithm.
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