Long-term and short-term memory network model power load prediction method suitable for high and cold regions
A long-short-term memory and network model technology, which is applied in the field of urban power load forecasting, can solve problems such as poor forecasting accuracy, and achieve the effect of enhancing integrity and accurate forecasting results
Inactive Publication Date: 2021-04-16
STATE GRID HEILONGJIANG ELECTRIC POWER CO LTD ELECTRIC POWER RES INST +1
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[0004] The purpose of the present invention is to solve the problem that the existing power load forecasting method fails to consider the time correlation of each influencing factor, resulting in poor forecasting accuracy. The present invention provides a long-term short-term memory network suitable for alpine regions Model Electric Load Forecasting Method
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[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.
[0026] It should be noted that, in the case of no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other.
[0027] For details, see figure 1 Describe this embodiment, the long short-term memory network model power load prediction method suitable for alpine regions described in this embodiment, it is characterized in that, the prediction method includes the following process:
[0028] Step 1. Use the d...
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The invention discloses a long-term and short-term memory network model power load prediction method suitable for high and cold regions, and belongs to the field of urban power load prediction. The problem that the prediction precision is still poor due to the fact that the existing power load prediction method does not consider the relevance of all influence factors in time is solved. The prediction method comprises the following steps: step 1, constructing a sample set with time correlation by utilizing data in a preset time period T in a historical database; and step 2, training a long-term and short-term memory network model by using the constructed sample set with time correlation to obtain a trained long-term and short-term memory network model; and predicting the power load output by the power grid by using the trained long-term and short-term memory network model so as to predict the power load of the power grid. The method is mainly used for predicting the power load of the power grid.
Description
technical field [0001] The invention belongs to the field of prediction of urban electric load. Background technique [0002] In modern smart grids, power dispatching needs to grasp the characteristics and future trends of regional power loads; accurate and fast regional power load forecasting is a necessary factor for the stable and reliable operation of power grids. With the continuous improvement of modern artificial intelligence algorithms and the increasingly urgent requirements of users for power grid power quality and power supply quality, it is imperative to propose load forecasting methods suitable for different regional characteristics. [0003] At present, there are mainly two types of short-term power load forecasting methods in alpine regions. One is the time series statistical method, which uses the relationship between power load and weather factors to establish a multiple linear regression model, which has the advantages of simple model and fast calculation s...
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IPC IPC(8): G06Q10/04G06Q50/06G06N3/04G06N3/08
CPCY04S10/50
Inventor 武国良祖光鑫孙东阳王国良秦立志
Owner STATE GRID HEILONGJIANG ELECTRIC POWER CO LTD ELECTRIC POWER RES INST