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公开(公告)号：US20230037193A1
公开(公告)日：2023-02-02
申请号：US17774735
申请日：2021-08-03
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Jun ZHAO , Tianyu WANG , Wei WANG
IPC: H02J3/00 , H02J3/38 , H02J3/46 , G05B19/042
Abstract: The present invention belongs to the technical field of information, particularly relates to the theories such as time series interval prediction, extreme learning machine modeling and Gaussian approximation solution, and is a wind power output interval prediction method. First, interval prediction of wind power output influencing factors is realized by time series analysis and normal exponential smoothing so as to consider an input noise factor. Then an extreme learning machine prediction model is established with an interval result as an input, output distribution is calculated based on iterative expectation and a conditional variance law, and thus an interval prediction result of wind power output is obtained. The method has advantages in interval prediction performance and calculation efficiency and can provide guidance for production, scheduling and safe operation of a power system.
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公开(公告)号：US20220318714A1
公开(公告)日：2022-10-06
申请号：US17297939
申请日：2020-07-28
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Jun ZHAO , Feng JIN , Guanghui YANG , Wei WANG
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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Patent Agency Ranking
3.发明申请公开(公告)号：US20200342150A1
公开(公告)日：2020-10-29
申请号：US16928672
申请日：2020-07-14
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Jun ZHAO , Yang LIU , Fan ZHOU , Zhongyang HAN , Linqing WANG , Wei WANG
Abstract: The present invention provides a method for an optimal scheduling decision of an air compressor group based on a simulation technology, which belongs to the technical field of information. The present invention uses expert experience to construct an air compressor energy consumption model sample set, and applies a least squares algorithm to learn relevant parameters of an air compressor energy consumption model; uses maximum energy conversion efficiency and minimum economic cost based on an equivalent electricity as target functions, and applies the simulation technology and a depth first tree search algorithm to solve a multi-target optimal scheduling model of the air compressor group; and finally uses a fuzzy logic theory to describe the preferences of decision makers, and introduces the decision maker preference information into interactive decision making, thereby assisting production staff to formulate safe, economical, efficient and environmentally friendly operation schemes to achieve an operation mode of maximum resource utilization of the air compressor group. The method also has wide application value in different industrial fields.
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公开(公告)号：US20220382263A1
公开(公告)日：2022-12-01
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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5.发明申请公开(公告)号：US20200285982A1
公开(公告)日：2020-09-10
申请号：US16500052
申请日：2018-09-12
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Zhongyang HAN , Jun ZHAO , Wei WANG , Linqing WANG
Abstract: The present invention belongs to the field of information technology, involving the techniques of fuzzy modeling, reinforcement learning, parallel computing, etc. It is a method combining granular computing and reinforcement learning for construction of long-term prediction interval and determination of its structure. Adopting real industrial data, the present invention constructs multi-layer structure for assigning information granularity in unequal length and establishes corresponding optimization model at first. Then considering the importance of the structure on prediction accuracy, Monte-Carlo method is deployed to learn the structural parameters. Based on the optimal multi-layer granular computing structure along with implementing parallel computing strategy, the long-term prediction intervals of gaseous generation and consumption are finally obtained. The proposed method exhibits superiority on accuracy and computing efficiency which satisfies the demand of real-world application. It can be also generalized to apply on other energy systems in steel industry.
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公开(公告)号：US20240411964A1
公开(公告)日：2024-12-12
申请号：US18699786
申请日：2021-10-26
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Tianyu WANG , Jun ZHAO , Wei WANG
IPC: G06F30/27
Abstract: A long-term scheduling method for an industrial byproduct gas system comprises the following steps: dividing information granularity according to the fluctuation features of energy data to form semantic representation of a data sample; with granular data features as input, constructing a deep contrastive network structure through expert scheduling experience data, and constructing knowledge representation under different scheduling states in modes of qualitative and quantitative learning; establishing a fully connected output layer to fit expert scheduling amount, to obtain an initial scheduling policy based on experience knowledge; constructing an actor-critic architecture to calculate a compensation policy that considers long-term scheduling performance.
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公开(公告)号：US20200219027A1
公开(公告)日：2020-07-09
申请号：US16624780
申请日：2018-06-15
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Abstract: A knowledge transfer-based modeling method for blast furnace gas scheduling systems, firstly, building energy body models of all stages of energy generation, transmission, consumption, storage and conversion based on pipe network structures of gas systems, and extracting common structure features of different gas systems based on the energy models; secondly, designing a data distribution feature-based membership function transfer method, learning mapping relations between data of the different gas systems according to distribution features of the data, and then transferring membership functions; thirdly, proposing a feature-based fuzzy rule transfer method, mapping rule structures of different systems to adjacent low-dimensional features, and realizing rule transfer in a rule reconstruction mode; and finally, designing a scheduling data-based knowledge transfer adjustment strategy, inputting actual scheduling data of blast furnace gas systems into the models, and adjusting corresponding rule parameters by taking a minimum deviation of an output scheduling scheme as a goal.
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