Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Markov chain based method for accurately forecasting power system loads

An accurate forecasting and power system technology, applied in forecasting, instrumentation, data processing applications, etc., and can solve problems such as troublesome computing, long time, and large sampling volume.

Inactive Publication Date: 2012-06-20
STATE GRID SHANDONG ELECTRIC POWER
View PDF4 Cites 15 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

These methods have a large amount of sampling, cumbersome calculations, and a long time

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Markov chain based method for accurately forecasting power system loads
  • Markov chain based method for accurately forecasting power system loads
  • Markov chain based method for accurately forecasting power system loads

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0031] Now it is necessary to predict the load data L at time t t , predict L t The premise is that the forecast data L at time t-1 has been obtained t-1 , according to the Markov property to predict the data L t only with L t-1 relevant.

[0032] In the first step, 14 sets of historical load data are taken, and each set has one data at time t-1 and one at time t, as shown in Table 1 below:

[0033] Table 1

[0034]

[0035]

[0036] Assuming the first group of data as the data to be predicted, it can be known that L t-1 =15217, what needs to be predicted is L t , that is, the first group of data at time t.

[0037] The second step is to set the state set E={1, 2, . . . , N}. Among them, state 1 indicates that the load is a value between 0 and 100, and state 2 indicates that the load is a value between 100 and 200. From the nature of the state matrix, it can be known that the historical data and the data to be predicted can be mapped to the values ​​in the state ...

Embodiment 2

[0047] Now it is necessary to predict the load data L at time t t , predict L t The premise is that the forecast data L at time t-1 has been obtained t-1 , according to the Markov property to predict the data L t only with L t-1 relevant.

[0048] In the first step, 14 sets of historical load data are taken, and each set has one data at time t-1 and one at time t, as shown in Table 1 below:

[0049] table 3

[0050]

[0051]

[0052] Assuming the first group of data as the data to be predicted, it can be known that L t-1 =16226, what needs to be predicted is L t , that is, the first group of data at time t.

[0053] The second step is to set the state set E={1, 2, . . . , N}. Among them, state 1 indicates that the load is a value between 0 and 100, and state 2 indicates that the load is a value between 100 and 200. From the nature of the state matrix, it can be known that the historical data and the data to be predicted can be mapped to the values ​​in the state ...

Embodiment 3

[0063] Now it is necessary to predict the load data L at time t t , predict L t The premise is that the forecast data L at time t-1 has been obtained t-1 , according to the Markov property to predict the data L t only with L t-1 relevant.

[0064] In the first step, 14 sets of historical load data are taken, and each set has one data at time t-1 and one at time t, as shown in Table 1 below:

[0065] table 5

[0066]

[0067] Assuming the first group of data as the data to be predicted, it can be known that L t-1 =14125, what needs to be predicted is L t , that is, the first group of data at time t.

[0068] The second step is to set the state set E={1, 2, . . . , N}. Among them, state 1 indicates that the load is a value between 0 and 100, and state 2 indicates that the load is a value between 100 and 200. From the nature of the state matrix, it can be known that the historical data and the data to be predicted can be mapped to the values ​​in the state set. a stat...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention relates to a Markov chain based method for accurately forecasting power system loads. The method is characterized by counting various variation trends of the next moment t according to the historical data and counting the probability of the variation trends under the condition of the given value Lt-1, finally taking the trend with the highest probability as the forecast result and obtaining the final result by correcting the sum of the probability on the two sides of the forecast result. The method has the following advantages: the loads can be forecast by only a few samples; the operating speed is high; the operation time is short; and the accurate probability forecast result can be obtained.

Description

technical field [0001] The invention relates to a load forecasting method, in particular to a method for short-term load forecasting of an electric power system. Background technique [0002] Load forecasting is to determine the load data at a specific time in the future based on many factors such as system operating characteristics, capacity increase decision-making, natural conditions and social influences, under the condition of meeting certain accuracy requirements, where load refers to the power demand (power) Or power consumption; load forecasting is an important content in the economic dispatch of power systems and an important module of the energy management system (EMS). Power system loads can generally be divided into urban civil loads, commercial loads, rural loads, industrial loads, and other loads. Different types of loads have different characteristics and rules. Urban civil load mainly comes from the electricity load of household electrical appliances of urba...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06Q10/04G06Q50/06
Inventor 李文升刘远龙龚文杰陈琛
Owner STATE GRID SHANDONG ELECTRIC POWER
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com