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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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公开(公告)号：US11526789B2
公开(公告)日：2022-12-13
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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3.发明授权公开(公告)号：US11126765B2
公开(公告)日：2021-09-21
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
IPC: G06F30/20 , G06N7/08 , G06F111/04 , G06F119/06 , G06F111/10
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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