A method for forecasting daily peak load of electric power

A load forecasting and daily peak technology, applied in forecasting, instruments, computing models, etc., can solve the problems of strong volatility, high noise in the load sequence, long training time, etc., and achieve the effect of complete decomposition.

Inactive Publication Date: 2019-01-18
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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AI Technical Summary

Problems solved by technology

However, the BP neural network algorithm has disadvantages such as slow convergence speed, long training time, and easy to fall into local optimal solution.
However, the daily peak load forecast is easily disturbed by external factors and has strong volatility, and the load sequence contains a lot of noise, which brings great difficulties to the forecasting work.

Method used

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  • A method for forecasting daily peak load of electric power
  • A method for forecasting daily peak load of electric power
  • A method for forecasting daily peak load of electric power

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Embodiment Construction

[0052] The embodiments are described in detail below with reference to the accompanying drawings.

[0053] 1. Full Aggregated Empirical Mode Decomposition of Adaptive White Noise

[0054] 1.1EMD

[0055] Empirical Mode Decomposition is the core algorithm of Hilbert-Huang Transform (HHT). Function (Intrinsic Mode Function, referred to as IMF), it should meet the following two conditions:

[0056] (1) The number of extreme points and zero-crossing points of the signal are equal or differ by at most one;

[0057] (2) The average value of the upper and lower envelopes of the signal is zero.

[0058] For a given signal X(t), it can be expressed as:

[0059]

[0060] where, imf i (t) is the eigenmode function component containing local feature signals of different time scales, r n (t) is the residual signal.

[0061] The specific steps of the EMD algorithm are as follows:

[0062] (1) Determine all the local extreme points of X(t), and use the cubic spline function to fit...

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Abstract

The invention belongs to the technical field of electric power load forecasting, in particular to a method for forecasting daily peak load of electric power, which comprises the following steps: collecting sample data including historical daily peak load, daily maximum temperature, daily minimum temperature, daily average temperature, daily average relative humidity, daily maximum wind speed and date type; the original sequence of daily peak load is decomposed into a finite number of eigenmode functions of local characteristic signals with different time scales by using the complete convergentempirical mode decomposition of adaptive white noise, and the adaptive white noise smoothing impulse interference is added in each decomposition to obtain a plurality of IMF components. By introducing the population dynamic evolutionary operator and nonlinear convergence factor, the grey wolf optimization algorithm is improved, the regularization parameters and the radial basis function parameters of support vector machine are optimized, and the optimized support vector machine prediction model is established. Forecasting models are used to get the final daily peak load forecasting results.

Description

technical field [0001] The invention belongs to the technical field of electric power load prediction, and in particular relates to a method for predicting the daily peak load of electric power. Background technique [0002] The development of modern society is inseparable from the supply of electricity. With the rapid development of the power industry, the power system has higher and higher requirements for the accuracy of power load forecasting. Daily peak load forecasting is an important part of power load forecasting, and the accuracy of its forecasting has a significant impact on the formulation of power generation plans, grid power dispatch, grid operation, and power supply reliability of the power system. Therefore, it is an important research work to construct a suitable model to realize the accurate prediction of daily peak load. [0003] The core issue of load forecasting is the forecasting method and model. With the rapid development of science and technology, l...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/00
CPCG06N3/006G06Q10/04G06Q50/06
Inventor 牛东晓戴舒羽康辉浦迪
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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