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公开(公告)号：US11487273B1
公开(公告)日：2022-11-01
申请号：US17569671
申请日：2022-01-06
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
IPC: G05B19/418
Abstract: A distributed industrial energy operation optimization platform which is capable of automatically constructing intelligent models and algorithms, is divided into three parts: a modeling terminal, a background service and a human-computer interface. The models like data pre-processing, energy generation-consumption-storage trend forecasting and optimal scheduling decision models are encapsulated in the modeling terminal as different visualization modules facing with multiple categories production scenarios, by dragging which the complex functional models can be realized conveniently. The background service is capable of automatically constructing the training samples and the production plans/manufacturing signals series according to the device model requirements of each edge side, interacts with the trained intelligent models through corresponding interfaces, and the computing results are saved in the specified relational database. The computing results are displayed through a friendly customer human-computer interface, and the real-time state of current working condition can also be adjusted.
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公开(公告)号：US11755976B2
公开(公告)日：2023-09-12
申请号：US17297939
申请日：2020-07-28
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Jun Zhao , Feng Jin , Guanghui Yang , Wei Wang
IPC: G06Q10/06 , G06Q10/0637 , G06Q10/0631 , G06Q50/04
CPC classification number: G06Q10/06375 , G06Q10/06313 , G06Q50/04
Abstract: The present disclosure discloses a method for predicting oxygen load in iron and steel enterprises based on production plan, which relates to influencing factor extraction, neural network modeling and similar sequence matching technologies. The method uses the actual industrial operation data to first extract the relevant data such as the production plan and production performance of converter steel-making, analyze the influencing factors, and extract the main influencing variables of oxygen consumption. Then, the neural network prediction model of oxygen consumption of a single converter is established, the mean square error is taken as the evaluation index, and the predicting result of time granularity of a converter in the blowing stage is given. Finally, in combination with the information of smelting time and smelting duration of each device in the converter production plan, the prediction value of oxygen load in a planned time period is given.
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公开(公告)号：US11070056B1
公开(公告)日：2021-07-20
申请号：US17197831
申请日：2021-03-10
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Feng Jin , Jun Zhao , Xingxing Gao , Linqing Wang , Wei Wang
Abstract: The present disclosure belongs to the technical field of information, provides a short-term interval prediction method for photovoltaic power output, and is a short-term interval prediction method for photovoltaic power output based on a combination of a multi-objective optimization algorithm and a least square support vector machine. The present disclosure firstly proposes a similar day classification method considering both numerical value and pattern similarity to enhance the regularity of samples, then constructs an adaptive proportional interval estimation model based on dual-LSSVM model, and optimizes model parameters by using NSGA-II algorithms to realize the interval prediction of photovoltaic power output. Results obtained by the method have high accuracy, and computation efficiency meets actual application requirements. The method can also be popularized and applied in the fields of grid connection and scheduling of renewable energy sources.
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