Medium-short term load prediction method and device based on manifold learning

Abstract

The invention provides a medium-short term load prediction method and device based on manifold learning, and belongs to the technical field of load prediction, and the method comprises the steps: carrying out nonlinear dimensionality reduction of a historical load data set by a local linear embedding method to acquire a low-dimensional manifold sequence; inputting the low-dimensional manifold sequence into the trained long-term and short-term memory neural network model to obtain a prediction sequence; and reconstructing the prediction sequence by adopting a manifold learning reconstruction method to obtain a load prediction value. The manifold learning method is adopted to reduce the dimension of the load data, and the manifold learning method can better mine the nonlinear characteristicsof the load. Meanwhile, the low-dimensional manifold sequence obtained after dimension reduction is predicted by adopting a deep learning method, so that the time sequence rule of the low-dimensionalmanifold sequence is better mined, and the load prediction precision is improved.

Description

technical field [0001] The invention belongs to the technical field of load forecasting, and more specifically relates to a method and device for short-term and medium-term load forecasting based on manifold learning. Background technique [0002] Short-term load curve forecasting (hours to days in advance) and medium-term load curve forecasting (days to months in advance) are of great significance to the optimal planning and operation of power systems, as well as power trading. [0003] The change of power load mainly depends on the regularity of people's production and life, and is affected by many external factors. External factors include weather factors (such as temperature), economic factors (such as electricity prices), seasonal factors, and historical electricity consumption. Under the influence of the above factors, the power load has complex characteristics such as periodicity, uncertainty, multi-dimensional nonlinearity, etc., making accurate load forecasting ver...

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Problems solved by technology

[0005] In view of the defects of the prior art, the purpose of the present invention is to provide a short-term and medium-term load forecasting method and device based on manifold learning, aiming to solve the problem of low forecasting accuracy of the linear dimensionality reduction method used in the existing load forecasting method

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