Short-term load forecasting method

Abstract

The invention discloses a short-term load forecasting method, and belongs to the technical field of power systems. Aiming at the problems that mass data have too much noise, are long in training time and are easily trapped in local minimum or over-fitting and the like in the prior art, the method comprises: performing cluster analysis on historical load data to generate a typical load curve, and digging the generality of mass historical load data to achieve the effect of screening and training data for later load forecasting, thus eliminating the noise influence of the mass data; performing strong fitting of a complex nonlinear function by deep learning to solve the problems of over-fitting and local minimum of a conventional neural network, thus realizing accurate and quick short-term load forecasting; and further constructing a forecasting model by using a distributed memory computation framework Spark, thus improving the efficiency and the instantaneity of the whole short-term load forecasting flow. Compared with the prior art, the method can realize accurate and quick short-term load forecasting.

Description

technical field [0001] The invention relates to the technical field of power systems, in particular to a short-term load forecasting method. Background technique [0002] Short-term load forecasting has always been an important operation and planning approach in power systems, and it affects many decisions in power systems, such as economic dispatch, automatic generation control, safety assessment, etc. Accurate power load forecasting can economically and reasonably arrange the start and stop of power system generating units, which plays an important role in maintaining the safety and stability of power grid operation, maintaining normal production and life in society, and effectively reducing power generation costs. [0003] In the era of big data, with the deepening of power grid intelligence, the storage scale of electricity consumption data will increase from the current GB level to TB level, or even PB level. At the same time, in order to ensure refined and accurate co...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to overcome the deficiencies of the existing technologies and provide a short-term load forecasting method that can effectively remove the noise in massive historical data and avoid over-fitting and local minimum problems that are prone to occur in conventional neural networks , so as to achieve accurate and fast short-term load forecasting

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