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Power load forecasting optimization method based on continuous power spectrum analysis

A power spectrum analysis, power load technology, applied in the field of power system, can solve problems affecting model learning and generalization ability

Inactive Publication Date: 2017-10-27
NANJING UNIV OF INFORMATION SCI & TECH
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Problems solved by technology

The power load is affected by human production and life with obvious regularity, but there is a lot of randomness in this regularity, which affects the learning and generalization ability of the model

Method used

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  • Power load forecasting optimization method based on continuous power spectrum analysis
  • Power load forecasting optimization method based on continuous power spectrum analysis
  • Power load forecasting optimization method based on continuous power spectrum analysis

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Embodiment 2

[0145] According to the steps S1-S5 in the first embodiment, the hour-level original power load time series collected by a power grid is obtained. For details, see figure 2 , since the purpose of the example of the present invention is hourly-level short-term forecasting, it can be used directly without making any adjustments to the original power load data, that is, p'(i)=p(i), i=1,2,... , N. In this embodiment, the first 1680 points of p'(i) are taken as training data, and the subsequent 50 points are predicted, and the effectiveness of the algorithm is checked with the relative percentage error MAPE as an index, namely:

[0146]

[0147] Among them, Y(i) and p'(i) are the power load prediction value and sampling value respectively, and l is the prediction step size.

[0148] image 3 Shown is the continuous power spectrum analysis result of the anomaly sequence P of the average power load time series, and it is found that the power load sequence of the power grid has ...

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Abstract

The invention discloses a power load forecasting optimization method based on continuous power spectrum analysis. The continuous power spectrum analysis method is used to extract the hidden significant periodic sequence in the power load time series and separate to obtain the residual sequence. The optimization method is based on particle swarm optimization. The BP neural network predicts the significant periodic sequence and obtains the prediction results of each significant periodic sequence; the RBF neural network optimized by the particle swarm optimization algorithm is used to predict the first-order difference sequence of the residual sequence, and then the residual sequence is obtained by differential inverse operation Finally, the average value of the average power load time series is added to the prediction results of each significant period sequence and the prediction result of the residual sequence to obtain the final prediction result. Aiming at the periodic characteristics of electric load data, the present invention establishes a forecasting model, which can greatly improve the accuracy of short-term electric load forecasting.

Description

technical field [0001] The invention belongs to the technical field of electric power systems, and in particular relates to an electric load forecasting optimization method based on continuous power spectrum analysis. Background technique [0002] The power system load refers to the sum of the power consumption of all electrical equipment in the system, also known as the comprehensive power load of the power system. The comprehensive power load plus the loss in the power grid and the factory power of the power plant is the total power that all generators in the system should generate, also known as the power system power generation load. Power load is an important factor affecting the safe and stable operation of the system. Power load forecasting refers to the analysis and research on the historical records of power load, comprehensive consideration of various factors that affect power load changes, such as social development planning, economic conditions, meteorological c...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/00G06N3/08
CPCG06Q10/04G06N3/006G06N3/084G06Q50/06
Inventor 杜杰彭丽霞王雷
Owner NANJING UNIV OF INFORMATION SCI & TECH
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