Comprehensive factor evaluation model-based short-term power load prediction method

A short-term power load and evaluation model technology, applied in forecasting, instrumentation, data processing applications, etc., can solve problems such as the influence of regional differences and the decrease in the accuracy of forecasting results

Inactive Publication Date: 2018-01-19
YANSHAN UNIV
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Problems solved by technology

Most of the current short-term power load forecasting is to establish a single meteorological factor model. However, this method is greatly affected by regional differences, which greatly reduces the accuracy of the forecast results. The short-term load forecasting method can no longer fully meet the needs of the power grid

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  • Comprehensive factor evaluation model-based short-term power load prediction method
  • Comprehensive factor evaluation model-based short-term power load prediction method
  • Comprehensive factor evaluation model-based short-term power load prediction method

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Embodiment

[0101] Select the electric power load data of a certain area from April 11, 2014 to April 18, 2014, in which a load data of 768 points is sampled every 15 minutes, and then these points are decomposed by MEMD to obtain the IMF components and the residual r; According to the forecast date information, the human comfort index of the area from April 11 to April 18, 2014 was obtained through the entropy weight method; The influence of comfort on load fluctuations is used to determine the input vectors when predicting each IMF component. Finally, the predicted load data on the 8th day is compared with the real load data to verify the accuracy of the forecast model.

[0102] The forecasting process follows figure 1 The flow chart proceeds. First, the original power load data sequence is decomposed into several intrinsic mode components (IMF) and residual components (r) of different frequencies by using MEMD, and the human comfort model is established as follows: figure 2 As show...

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Abstract

The invention discloses a comprehensive factor evaluation model-based short-term power load prediction method. The method comprises the steps of firstly building a human comfort model by applying an AHP-entropy weight method; secondly decomposing an original power load data sequence into a plurality of intrinsic mode function (IMF) components and residual components with different frequencies by applying masking empirical mode decomposition (MEMD); thirdly performing correlation analysis on the IMF components capable of reflecting intrinsic characteristics of a load sequence and a human comfort index; according to the value of a correlation coefficient, determining input vectors of Elman neural network sub-models subjected to particle swarm optimization (PSO) and performing prediction; andfinally superposing predicted values of all the sub-models to obtain a final predicted result. The method remarkably improves the precision of short-term power load prediction, and has higher practicality.

Description

technical field [0001] The invention relates to the technical field of power load forecasting, in particular to a short-term power load forecasting method based on a comprehensive factor evaluation model. Background technique [0002] Power load forecasting is one of the important tasks of the power supply department, and it is the premise to ensure reliable power supply and safe operation of the power system. Accurate load forecasting can economically and rationally arrange the start and stop of generators inside the power grid, so as to improve economic and social benefits. However, the electric load does not exist alone, it is affected and restricted by many factors, so in order to improve the accuracy and stability of the forecast, it is necessary to consider the impact of different factors on the load, but if too many factors are considered, it will not only increase the complexity of the algorithm, Moreover, it is also possible to make the factors that have no effect ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06
Inventor 张淑清张航飞李盼马灿李明星吴迪
Owner YANSHAN UNIV
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