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
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

These methods have a large amount of sampli

Method used

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  • 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...

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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

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06
Inventor 李文升刘远龙龚文杰陈琛
Owner STATE GRID SHANDONG ELECTRIC POWER
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