Power load forecasting method in multi-energy coupling mode

Patent metadata

Field	Value
Publication number	US-11205895-B2
Application number	US-202016913580-A
Country	US
Kind code	B2
Filing date	Jun 26, 2020
Priority date	Dec 27, 2019
Publication date	Dec 21, 2021
Grant date	Dec 21, 2021

Abstract

The present invention relates to a load forecasting method based on a multi-energy coupling scene, which comprises the following steps: step 1, establishing a multilevel indicator system of key influencing factors of load demand in a multi-energy mode; step 2, obtaining key influencing factors influencing the total load demand and the total supply of the multi-energy coupling load; step 3, normalizing the data of the key influencing factors extracted in step 2, and initializing population characteristic parameters of adaptive firework algorithm (AFWA); step 4, forecasting the regional total power demand and regional coupling power supply respectively by adopting LSSVM optimized by the AFWA algorithm; and step 5, forecasting the net power load demand on the regional coupling power supply. The present invention improves the calculation efficiency and model stability and also ensures the forecasting accuracy.

First claim

We claim:

1. A load forecasting method based on a multi-energy coupling scene, comprising the following steps:
Step 1, establishing a multilevel indicator system of key influencing factors of load demand in a multi-energy coupling mode, and collecting corresponding data sets according to key influencing factors in the indicator system;
Step 2, extracting key influencing factors most correlated with a total load demand and a total supply of multi-energy coupling load in the indicator system established in step 1 respectively by using a maximum and minimum redundancy method, removing surplus influencing factors with redundancy information, and finally obtaining the key influencing factors influencing the total load demand and the total supply of the multi-energy coupling load;
Step 3, normalizing the data of the key influencing factors extracted in step 2, and initializing population characteristic parameters of an AFWA adaptive firework algorithm;
Step 4, based on the key influencing factor data normalized in step 3 and initialized population characteristic parameters of the AFWA adaptive firework algorithm, forecasting regional total power demand and regional coupling power supply respectively by using LSSVM optimized by AFWA algorithm; and
Step 5, based on forecast results of the regional total power demand and the regional coupling power supply in step 4, forecasting a net power load demand of the regional coupling power supply.

2. The load forecasting method based on the multi-energy coupling scene according to claim 1 , comprising the following steps:
the multilevel indicator system of the key influencing factors of the load demand in the multi-energy coupling mode in step 1 comprises factors influencing the regional total load demand and factors influencing the multi-energy coupling supply;
wherein the factors influencing the regional total load demand comprises economic development, industry structure and demand coupling benefit;
(1) economic development comprises: three-level influencing factor indicators of gross regional product and price index;
(2) industry structure comprises: three-level influencing factor indicators of contribution of regional primary industry, contribution of regional secondary industry, contribution of regional tertiary industry;
(3) demand coupling benefit comprises three-level influencing factor indicators of investment in energy-saving systems;
wherein the factors influencing the multi-energy coupling supply comprise: renewable energy power supply, combined supply of cooling, heating and electricity, other energy power supply and energy supply economy;
(1) the renewable energy power supply comprises: three-level influencing factor indicators of wind power generating capacity, solar power capacity, hydroelectric power generating capacity and other renewable energy power generating capacity;
(2) the combined supply of cooling, heating and electricity comprises: three-level influencing factor indicators of combined supply power;
(3) other energy power supply comprises: three-level influencing factor indicators of garbage power generating capacity;
(4) energy supply economy comprises: three-level influencing factor indicators of unit price of main energy and investment in energy base stations.

3. The load forecasting method based on the multi-energy coupling scene according to claim 1 , wherein the step 2 specifically comprises:
(1) obtaining correlation between each influencing factor and load value by calculating mutual information of each influencing factor:

I
⁡

(

x
i

,
y

)

=

∫

∫

p
⁡

(

x
i

,
y

)

⁢
log
⁢

p
⁡

(

x
i

,
y

)

p
⁡

(

x
i

)

⁢

p
⁡

(
y
)

⁢

dx
i

⁢
dy

(
1
)

wherein I(x i , y) is positive correlation between x i and y, x i is the i th influencing factor, y is the load value, p(x i , y), p(x i ) and p(y) represent combined probability density of x i and y, probability density of x i and probability density of y;
(2) by calculating the maximum correlation of each influencing factor, selecting a set of m key influencing factors with maximum correlation with the load value:

max
⁢

⁢

D
⁡

(

S
,
y

)

Assignees

• Univ North China Electric Power

Inventors

• Liu Dunnan
• Wang Lingxiang
• Jia Heping
• Qin Guangyu
• Liu Mingguang

Classifications

• H02J2103/30

  Simulating, planning, modelling, reliability check or computer assisted design [CAD] of electric power networks · CPC title

• G06Q50/06

  Energy or water supply · CPC title

• G05B19/042

  using digital processors (G05B19/05 takes precedence) · CPC title

• H02J3/003 Primary

  Load forecast, e.g. methods or systems for forecasting future load demand · CPC title

• G05B2219/2639

  Energy management, use maximum of cheap power, keep peak load low · CPC title