Short-time power load forecasting method based on long-range dependence FARIMA model

Abstract

The invention relates to a short-time power load forecasting method based on a long-range dependence FARIMA model. The method includes the following steps that (1) forecasting sample data are obtained according to power load data before a forecasting day; (2) the forecasting sample data are preprocessed, singular points and zero-mean-value are eliminated to obtain a power load sequence {Xt}; (3) an estimated value H of a Hurst index of the power load sequence {Xt} is calculated by means of a rescaled range analysis method; (4) whether the power load sequence meets the requirement of a long-range dependence process is judged according to the obtained estimated value H of the Hurst index, if the answer is positive, a fractional difference parameter d is calculated, and if the answer is negative, the step (1) is repeated; (5) according to the obtained fractional difference parameter d, the FARIMA model of the power load sequence {Xt} is built; (6) according to the FARIMA model, a power load value is forecasted, and an actual forecast value is obtained by carrying out inverse difference on the forecasted power load value to adjust a power scheduling scheme. Compared with the prior art, the method has the advantages of being accurate in result, high in practicality and the like.

Description

technical field [0001] The invention relates to a power load forecasting method, in particular to a short-term power load forecasting method based on a long-term correlation FARIMA model. Background technique [0002] Electric load forecasting plays an important role in power system production planning and stable operation of the power grid, unit maintenance plan, inter-regional power transmission plan, load scheduling plan, etc. The stable operation of the power system requires that the power generation can keep up with the changes in the system load. The electric energy must be able to balance the line load. If the load is not predicted in advance or the load prediction is inaccurate, a large amount of electric energy will be wasted. Therefore, accurate load prediction is not only important for system operation and Production planning plays an important role, and it also plays a key role in determining the operation mode and optimal scheduling of the power system. [0003...

AI Technical Summary

Problems solved by technology

[0003] Short-term power load forecasting provides the change trend of power load for a period of time in the future, which is the premise to ensure the security of the power grid, realize the intelligent control of the enterprise power system, operate and plan a reasonable power purchase plan, and automatic generation control (AGC). In addition to the influence of natural factors such as weather and temperature, the load itself also has random characteristics, so short-term power load forecasting is a complicated problem

At present, research on short-term load forecasting at home and abroad mainly includes trend extrapolation method, regression analysis method, gray theory forecasting method, neural network forecasting, time series forecasting, etc. The emphases of each research are different, but because there are many factors affecting load, Many forecasting methods cannot be widely used due to the limitation of forecasting accuracy and forecasting range

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