Short time electric power load prediction method and device

Abstract

The invention discloses a short time electric power load prediction method and device. The short time electric power load prediction method predicts an electric power load through integrating quantile regression and robustness extreme learning and using a hybrid prediction model established after optimizing a hybrid particle swamp algorithm (PSOGSA). The quantile regression uses multiple quantiles of history electric power data influence factors to obtain a quantile equation corresponding to a conditional distribution of electric power load prediction data of sometime in the future; stochastic disturbance of electric power data inputted in quantile regression can describe statistic distribution of prediction load values in detail without the need of making any hypothesis on distribution, which makes the whole prediction model strong in robustness; the robustness extreme learning machine is more robust in an abnormal load value; and the electric power load can be predicted by combining the two methods and using a hybrid model formed after optimizing the PSOGSA. The invention also provides a short time electric power load prediction device having the same beneficial effects.

Description

technical field [0001] The invention relates to the field of data analysis, in particular to a method and device for short-term power load forecasting. Background technique [0002] Due to the fact that electricity cannot be stored directly, in today's society, power is always generated and used at any time. However, in daily life, people's power consumption generally fluctuates, and there are peaks and valleys, but people's power consumption status can be predicted, so that the power company can provide accurate power supply. [0003] Short-term power load forecasting is of great economic significance to the operation and planning of power systems. Accurate power load forecasting helps power companies to make reasonable power generation plans and can effectively reduce the operating costs of power companies. [0004] In the prior art, the electric load is usually forecasted by using artificial neural network combined with the least square method. In the specific operation...

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Problems solved by technology

[0005] However, in the existing technology, the prediction accuracy is generally not high, which will increase the operating cost of the power company

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