Short-term power load prediction method
A short-term power load and forecasting method technology, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve problems such as correction of few results and impact on prediction accuracy
Inactive Publication Date: 2016-08-03
HAINAN POWER GRID CO LTD ELECTRIC POWER RES INST +1
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[0002] Short-term power load forecasting is easily affected by many uncertain factors, such as historical load data, temperature, relative humidity, wind speed, etc.; channel noise and other factors will produce sawtooth fluctuations in historical load data, affecting the accuracy of forecasting; There are corrections to the predicted results, so a multivariate gray forecasting method based on the wavelet transform method to process data and correct the predicted results through the Markov method is needed
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[0050] The embodiment of the present invention provides a short-term power load forecasting method, specifically a gray Markov short-term power load forecasting method based on wavelet denoising. Gray Markov is a combined forecasting method of gray forecasting and Markov method, such as figure 1 As shown, the specific process of the embodiment of the present invention may include the following steps:
[0051] Step 101, select the load signal of 48 points (sampling once every 30min) of m similar dates before the forecast date;
[0052] Here, dates of the same type include: working days, weekends and major holidays. Among them, major festivals include Spring Festival, Mid-Autumn Festival and so on. In practical applications, m is an integer not less than 1, and it has been verified through multiple experiments that when m is set to 6 or 7, the prediction accuracy is relatively high.
[0053] Here, the short-term load generally refers to the sampling time of 1h, 24 o'clock or 3...
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The invention discloses a short-term power load prediction method, which comprises the following steps: performing noise processing on acquired original load data by adopting a wavelet threshold processing method; establishing a multi-variable grey dynamic model by adopting the processed original load data and acquired weather characteristic data as input, wherein the output of the grey dynamic model is a load data sequence; forming an error sequence by adopting the load data sequence and a sequence of the original load data, dividing an error into multiple states by adopting a Markov method, extracting a corrected error value from each state interval, predicting an error state of a prediction date by virtue of a state transition matrix, and adding a grey predicted load value and a Markov corrected value to obtain a final predicted value. According to the method, the short-term power load prediction accuracy is greatly improved.
Description
technical field [0001] The invention relates to the technical field of short-term power load forecasting, in particular to a short-term power load forecasting method. Background technique [0002] Short-term power load forecasting is easily affected by many uncertain factors, such as historical load data, temperature, relative humidity, wind speed, etc.; channel noise and other factors will produce sawtooth fluctuations in historical load data, affecting the accuracy of forecasting; There are corrections to the predicted results, so a multivariate gray forecasting method that processes data based on the wavelet transform method and corrects the predicted results through the Markov method is needed. Contents of the invention [0003] In order to solve existing technical problems, an embodiment of the present invention provides a short-term power forecasting method. [0004] In order to achieve the above object, the technical solution of the embodiment of the present invent...
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IPC IPC(8): G06F19/00G06Q50/06
CPCG06Q50/06G16Z99/00
Inventor 蔡渊卢继哲方连航刘红岩梁钰牛玉广胡阳
Owner HAINAN POWER GRID CO LTD ELECTRIC POWER RES INST