Adaptive Variable Weight Combination Load Forecasting Method and Device
A combined forecasting and load forecasting technology, applied in forecasting, data processing applications, instruments, etc., can solve problems such as low accuracy, and achieve the effects of high accuracy, adaptability and flexibility
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Embodiment 1
[0063] An adaptive variable weight combination load forecasting method, refer to figure 1 , the method includes:
[0064] S102. Day-ahead load forecasting modeling based on multiple forecasting methods to obtain multiple single day-ahead load forecasting models.
[0065] Among them, the single day-ahead load forecasting model includes the average growth rate forecasting model, the linear quadratic moving average model, the cubic exponential smoothing forecasting model and the gray system theory forecasting model.
[0066] In the embodiment of the present invention, the following four forecasting methods are selected as the basis of combined forecasting, mainly because the method is simple and easy to implement, historical data is easy to obtain, and it has the advantages of high forecasting accuracy. After the verification of the actual electricity consumption data, the errors of the four prediction methods alone can be controlled within 20%.
[0067] (1) Average growth rate...
Embodiment 2
[0171] An adaptive variable weight combination load forecasting device, refer to figure 2 , the device consists of:
[0172] The first modeling module 11 is used for day-ahead load forecasting modeling based on multiple forecasting methods to obtain multiple single day-ahead load forecasting models.
[0173] Wherein, the single day-ahead load forecasting model includes an average growth rate forecasting model, a linear quadratic moving average model, a cubic exponential smoothing forecasting model, and a gray system theory forecasting model.
[0174] The second modeling module 12 is configured to establish a day-ahead load combination forecast model based on multiple single day-ahead load forecast models.
[0175] A weight updating module 13, configured to update the weight of the day-ahead load combination forecasting model based on reinforcement learning.
[0176] Further, the weight update module is specifically used to: take the weight coefficient of the single day-ahea...
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