Short-term load prediction method based on GBDT (gradient boosting decision tree)

Abstract

An embodiment of the invention discloses a short-term load prediction method based on a GBDT (gradient boosting decision tree). The method comprises the following steps: acquiring historical load dataof N days before the prediction-waiting day, and forming an original data set A0; screening a data set B for constructing training samples from the original data set A0; constructing total sample set(X, Y) required for constructing a GBDT prediction model by the data set B; training and constructing a whole-day GBDT prediction model by the total sample set (X, Y), and prediction whole-day load vector of the prediction-waiting day according to the whole-day GBDT prediction model; segmenting the total sample set (X, Y) into 24 sample subsets by the hour, training and constructing hour GBDT prediction models respectively, and predicting 24-hour load vectors of the prediction-waiting day according to the hour GBDT prediction models; predicting the final load vector of the prediction-waitingday according to combination of the whole-day load vector and the 24-hour load vectors. The short-term load prediction precision is improved by sufficiently mining characteristics in the historical load data and constructing the different GBDT models.

Description

technical field [0001] The invention relates to the technical field of power system load forecasting, in particular to a short-term load forecasting method based on a gradient boosting decision tree. Background technique [0002] Load forecasting is to determine the load data at a specific time in the future based on many factors such as system operating characteristics, capacity increase decision-making, natural conditions and social influences, under the condition of meeting certain accuracy requirements, where load refers to the power demand (power) Or electricity consumption, load forecasting is an important content in power system economic dispatch. Accurate load forecasting can economically and rationally arrange the start and stop of power grid internal generator sets, maintain the safety and stability of power grid operation, reduce unnecessary rotating reserve capacity, reasonably arrange unit maintenance plans, ensure the normal production and life of the society, ...

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Problems solved by technology

[0004] In the embodiment of the present invention, a short-term load forecasting method based on a gradient boosting decision tree is provided to solve the problem of low load forecasting accuracy in the prior art

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