Method for construction of long-term prediction intervals and its structural learning for gaseous system in steel industry

Patent metadata

Field	Value
Publication number	US-11526789-B2
Application number	US-201816500052-A
Country	US
Kind code	B2
Filing date	Sep 12, 2018
Priority date	Sep 12, 2018
Publication date	Dec 13, 2022
Grant date	Dec 13, 2022

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.

First claim

The invention claimed is:

1. A method for construction of long-term prediction intervals and its structural learning approach for generation and consumption of an industrial energy system, the method comprising steps of:
step 1: data pre-processing
collecting data of generation and consumption units of the industrial energy system from real-time relational database, and implement essential noise elimination, filtering and imputation;
step 2: Fuzzy C-Means (FCM)
dividing the data into segments with equal length, i.e., Z={z 1 , z 2 , . . . , Z N }, where Z i ∈ n , n denotes the number of data points in each segment, and N is the number of segments; implementing FCM clustering algorithm so as to obtain the prototype matrix V={v 1 , v 2 , . . . , v c } and the corresponding fuzzy membership grades U={u 1 , u 2 , . . . , u N }, where V i ∈ n , u i ∈ c , C denotes the dimension of the prototype matrix;
step 3: establishment of the multi-layer granular computing model
assigning information granularity α i,j and β i from bottom to top on the prototype matrix V={v 1 , v 2 , . . . , v c }, where i=1, 2, . . . , m; j=1, 2, . . . , n i and n 1 ≠n 2 ≠ . . . ≠n m ; as such, the numeric prototypes are successfully extended into the intervals; in order to optimize the above parameters, this method defines coverage cov and specificity spec, as follows:

cov

=
.

1
T

⁢

∑

i
=
1

T

λ
i

(
1
)

spec

=
.

range
-

1
T

⁢

∑

i
=
1

T

❘
"\[LeftBracketingBar]"

z
_

i

-

z
_

i

❘
"\[RightBracketingBar]"

(
2
)

where T denotes the number of data points in a sample; λ i is a marker variable, which will be tagged as 1 if the constructed prediction interval covers the data point, otherwise it will be tagged as 0; range denotes the difference between the maximum and minimum value of the data points; z i and z i respectively represent the upper and lower bounds of the constructed prediction intervals;
maximizing the cov and spec; wherein, the cov should be greater than or equal to the prescribed confidence level (1−ρ)×100%, where ρ∈[0,1] denotes the level of significance; considering Eq. (1) as the constraints, meaning that the cov should be greater than or equal to the objective confidenece interval; the direction for optimizing the information granularities is opposite with the one for assignment; the optimization models are established as follows:
(1) 2 nd layer

max
⁢

range

(
2
)

-

1
m

⁢

∑

i
=
1

m

❘
"\[LeftBracketingBar]"

z
_

i

(
2
)

-

z
_

i

(
2
)

❘
"\[RightBracketingBar]"

⁢

s
.

Assignees

• Univ Dalian Tech

Inventors

• Han Zhongyang
• Zhao Jun
• Wang Wei
• Wang Linqing

Classifications

• G06N5/048 Primary

  Fuzzy inferencing · CPC title

• G06Q10/04 Primary

  Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202) · CPC title

• G06N3/043

  based on fuzzy logic, fuzzy membership or fuzzy inference, e.g. adaptive neuro-fuzzy inference systems [ANFIS] · CPC title

• G06F2218/00

  Aspects of pattern recognition specially adapted for signal processing · CPC title

• G06F18/295

  Markov models or related models, e.g. semi-Markov models; Markov random fields; Networks embedding Markov models · CPC title