Electrical power system short-term load forecasting method based on big data technology
A technology of short-term load forecasting and big data technology, which is applied in forecasting, data processing applications, instruments, etc., and can solve problems such as low load forecasting accuracy and complex power consumption laws
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[0050] The present invention will be further described in detail below with reference to the drawings and specific embodiments.
[0051] Such as figure 2 As shown, a short-term load forecasting method for power systems based on big data technology includes the following steps:
[0052] S1 input historical load data;
[0053] S2 uses an improved hierarchical clustering algorithm to cluster the input historical load data;
[0054] Because the trend of the load curve is closely related to the type of day and weather factors. Through the cluster analysis of the curves, the load curves with similar shape characteristics can be classified into one category.
[0055] The clustering analysis algorithm adopted by the present invention is an improved agglomerated hierarchical clustering algorithm. At the same time, the present invention normalizes the maximum value of the difference of each dimension in Euclidean distance, as shown in the following formula:
[0056] d 12 = X k ...
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