Short-term load prediction based on meteorological similar day and error correction
A technology for short-term load forecasting and error correction, applied in the fields of instruments, data processing applications, resources, etc., which can solve the problems of undisclosed contacts, troubles of electric power workers, troubles of load forecasting work, etc.
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Embodiment 1
[0061] 1 Regression analysis of meteorological factors based on SPSS
[0062] 1.1 Establishment of multiple regression analysis model
[0063] In real life, people often want to perform statistical analysis on a dependent variable, but there are often more than one independent variable affecting the dependent variable. For example, k independent variables X need to be considered 1 , X 2 ,...,X k When the relationship between the dependent variable y and the dependent variable y, the principle of least squares method is used to establish a multiple linear regression model as:
[0064] y=y'+μ=b 0 +b 1 x 1 +b 2 x 2 +...+b i x i +...+b k x k (1)
[0065] It can be seen from formula (1) that the dependent variable y is composed of two parts. The first part y′ is the estimated value of the dependent variable y, which represents the part that can be determined by the variable; u is the residual, which represents the part that is not determined by the independent variabl...
Embodiment 2
[0140] In order to verify the applicability of the load forecasting principle including the simulation relative error, the load data and meteorological data of a certain area are used in the calculation example for forecasting, and MATLAB programming is used for simulation.
[0141] In this embodiment, one day in each of the four seasons of spring, summer, autumn and winter in 2014 in this region is selected as the forecast date, which are recorded as A1, A2, A3, and A4 respectively. For a certain forecast day, determine the season it belongs to, use real-time meteorological data as the basis for selecting similar days, and then establish a similar day forecast error sample set, conduct uniform sampling based on the systematic sampling method, and sort the sampling results according to the volatility analysis. Figure 4 It is the compensation error result obtained from the volatility analysis of A1~A4 and the final fitting error after sorting.
[0142] The prediction result of...
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