Power system load prediction method and system based on wavelet noise reduction and EMD-ARIMA

A power load and wavelet noise reduction technology, which is applied in the direction of load forecasting, forecasting, and information technology support systems in AC networks, can solve problems such as lack of optimization in forecasting methods, and achieve the effect of reducing interference and improving accuracy

A power load and wavelet noise reduction technology, which is applied in the direction of load forecasting, forecasting, and information technology support systems in AC networks, can solve problems such as lack of optimization in forecasting methods, and achieve the effect of reducing interference and improving accuracy

CN111985361AInactive Publication Date: 2020-11-24WUHAN UNIV
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  • Power system load prediction method and system based on wavelet noise reduction and EMD-ARIMA
  • Power system load prediction method and system based on wavelet noise reduction and EMD-ARIMA
  • Power system load prediction method and system based on wavelet noise reduction and EMD-ARIMA

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Embodiment Construction

[0045] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not constitute a conflict with each other.

[0046] The invention proposes a power system load prediction method based on wavelet noise reduction and EMD-ARIMA. And the daily load data forecasting in a certain area is taken as a specific example for illustration, but the present invention is not only applicable to the load forecasting in this area, but can also be extended to other forecasting fields.

[0047] Firstly, the original load d...

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Abstract

The invention discloses a power system load prediction method and system based on wavelet noise reduction and EMD-ARIMA, and belongs to the field of power system load prediction. The method comprisesthe following steps: firstly, acquiring original load data of a power system, and then performing noise reduction processing on the load data by utilizing wavelet analysis; further processing the denoised data by using an empirical mode decomposition (EMD) method to obtain different load components; and finally, constructing a corresponding differential autoregressive moving average ARIMA model for different load components. And meanwhile, the ARIMA model is optimized by utilizing a red pool information criterion and a Bayesian information criterion, and finally, load components predicted by different ARIMA models are reconstructed to obtain a final prediction result, so that the accuracy of load prediction is effectively improved.

Description

technical field [0001] The invention belongs to the field of power system load forecasting, and more specifically relates to a power system load forecasting method and system based on wavelet noise reduction and EMD-ARIMA. Background technique [0002] Power system short-term load forecasting is one of the core parts of smart grid integrated intelligent energy management system. An accurate short-term load forecasting model facilitates rational planning of ongoing grid operations with efficient resource management. The randomness, non-stationarity and nonlinearity of the load curve make the accurate modeling of short-term load forecasting a rather challenging task. [0003] At present, there are many related studies on power system load forecasting, including differential autoregressive moving average (Autoregressive Integrated Moving Average Model, ARIMA) model, Kalman filter forecasting, Markov forecasting, support vector machine (Support Vector Machine, SVM), Artificial...

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Application Information

Patent Timeline
24 Nov 2020
Publication
CN111985361A
IPC
G06K9/00; G06K9/62; G06Q10/04; G06Q50/06
CPC
G06Q10/04; G06Q50/06; G06F2218/20; G06F2218/04; G06F18/29; G06F17/18; G06F30/367; G06F2119/10
Inventors
何怡刚; 吴晓欣