Power load prediction method based on phase space reconstruction and data driving

Abstract

The invention discloses a power load prediction method based on phase space reconstruction and data driving, and the method comprises the steps: firstly calculating the delay time and embedded dimension of historical load data, dividing a data set into a training set and a test set, carrying out the phase space reconstruction of the training set and the test set according to the delay time and embedded dimension, and carrying out the normalization processing; secondly, comparing the prediction precision of the twelve data driving models under the same data set, wherein the obtained XGBoost isthe optimal model in the statistical learning model, tand he LSTM and the extreme learning machine are the optimal models in the neural network; carrying out model, day, week, half month and month load prediction on the three models respectively, and keeping high prediction precision; and finally, performing unequal weight combination on the XGBoost, the LSTM and the extreme learning machine by using a grey relational degree method, and constructing a combined data driving model. The method can improve the prediction precision.

Description

technical field [0001] The invention relates to the technical field of power system load forecasting, in particular to a phase space reconstruction and data-driven power load forecasting method. Background technique [0002] Accurate load forecasting can maintain the safety and stability of power grid operation, reduce unnecessary rotating reserve capacity, reasonably arrange unit maintenance plans, effectively reduce power generation costs, and improve economic and social benefits. The size of the power load is affected by many factors: weather, temperature, holidays, regional GDP, etc. However, it is often difficult to obtain accurate data on all influencing factors. Therefore, if a more accurate power load forecasting method can be implemented in the absence of multi-factor data, the cumbersome process of collecting and processing various data can be saved, and the steps of power load forecasting can be simplified. [0003] In the process of implementing the present inv...

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Problems solved by technology

[0005] The present invention proposes a power load forecasting method based on phase space reconstruction and data drive, which is used to solve or at least partially solve the technical problem of low forecasting accuracy existing in the prior art method

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