Weather sensitive load power estimation method based on stacked auto-encoders
A sensitive load and autoencoder technology, applied in load forecasting, instrumentation, calculation, etc. in the AC network, can solve problems such as data loss
Inactive Publication Date: 2018-11-06
STATE GRID JIANGSU ELECTRIC POWER CO ELECTRIC POWER RES INST +3
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Problems solved by technology
In practice, meteorological data, especially the change curve of meteorological factors with a sampling interval of 10 minutes, is prone to missing data
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Embodiment 1
[0087] A 220kV substation in a prefecture-level city is selected as the research object to illustrate the implementation method. The substation includes industrial, commercial, residential and traction loads, and the load types are comprehensive. The data collected are the annual load power of the station in 2015 (sampling interval of 5 minutes), as well as temperature and humidity data (sampling interval of 10 minutes).
[0088] Step 1: Prepare sample data.
[0089] Due to the incompleteness of meteorological data, a total of 70 pieces of daily meteorological sensitive load power curve data arranged in normal time order (April-October, 10 days per month) are calculated, of which 65 pieces of data are used as the belts of the fully connected layer for training the SAE model. label samples, and the other 5 as test samples. Then take 140 pieces of daily load curve data of the substation on all working days from April to October in 2015 (214 days in total, including 69 days of ...
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The invention discloses a weather sensitive load power estimation method based on stacked auto-encoders (SAE). The method comprises the steps of adding a multi-layer full-connection layer to the output end of an SAE model, and building a weather sensitive load power estimation model based on the SAE; and extracting a dimensionality reduction characteristic of a daily load curve by utilizing unsupervised learning of the SAE, and training the full-connection layer by utilizing a weather sensitive load power curve as a labeled sample, so that the mapping from the dimensionality reduction characteristic of the daily load curve to weather sensitive load power is formed in the full-connection layer. The estimation model provided by the method can directly obtain the weather sensitive load powercurve from the daily load curve, so that the method is particularly suitable for the situation that weather data is frequently lost during actual application. The SAE can extract the dimensionality reduction characteristic of the daily load curve in an unsupervised manner, and the number of input neurons of the full-connection layer is greatly reduced, so that the network parameters of the full-connection layer are greatly reduced, and the training difficulty of the model is remarkably lowered.
Description
technical field [0001] The invention belongs to the field of power system load prediction and load power model, in particular to a method for estimating weather-sensitive load power based on a stacked autoencoder. Background technique [0002] With the intensification of global warming and the continuous improvement of people's living standards, the power consumption of weather-sensitive loads dominated by air conditioners has been increasing year by year. In 2017, the power consumption of air conditioners in some areas such as Suzhou in summer led to an abnormal increase in loads in this area. Researching on the estimation of weather-sensitive load power can not only improve the accuracy of the load power model, provide regulation basis for the safe and stable operation of the power grid in summer, but also provide a basis for the evaluation of demand-side response capability, which has important research significance. [0003] A weather-sensitive load power estimation meth...
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IPC IPC(8): G06F17/50
CPCG06F30/20G06Q50/06G06Q10/04H02J3/003Y04S10/50G06N3/10G01W1/00
Inventor 赵静波鞠平陈彦翔秦川施佳君廖诗武朱鑫要王大江
Owner STATE GRID JIANGSU ELECTRIC POWER CO ELECTRIC POWER RES INST