A short-term load forecasting method for distribution network based on multi-mode fusion

Abstract

The invention discloses a short-term load forecasting method of a distribution network based on multi-mode fusion, the main steps of which are as follows: 1) collecting historical load time series data X of a power network. 2) Perform STL time series decomposition on historical load time series data X. 3) Get the trend item sequence X trend LSTM neural network model with N structures, residual item sequence X remainder LSTM neural network model with N structures and integrated prediction model. 4) Obtain the period item prediction result O s . 5) Obtain forecast samples. 6) Input the forecast sample into the forecast model to obtain the trend item forecast result O t and the prediction result O of the remaining items r . 7) Integrating the prediction results of periodic items O s , Trend item prediction result O t and the prediction result O of the remaining items r , and use the integrated prediction to obtain the final prediction result. The present invention helps to improve the prediction accuracy of the model while improving the robustness and generalization ability of the load prediction model.

Description

technical field [0001] The invention relates to the field of power system load forecasting, in particular to a short-term load forecasting method for distribution networks based on multi-mode fusion. Background technique [0002] Accurate load forecasting is of great significance for operation and maintenance personnel to master the safe and stable operation of the distribution network system. The existence of prediction error directly increases the additional cost of power system operation, which is not conducive to the improvement of economy. In the past few decades, many scholars at home and abroad have proposed many algorithm models for the problem of short-term load forecasting to improve its accuracy. However, because the load sequence is greatly affected by external factors, and the sequence change is nonlinear, random and uncertain, it is very difficult to improve its accuracy. [0003] At present, load forecasting technology has gradually shifted from traditional ...

AI Technical Summary

Problems solved by technology

Although the hybrid model can effectively improve the accuracy of short-term load forecasting, the forecasting part of the model is single, which is greatly affected by parameter adjustment, and is not adaptable to multi-type characteristic data.

Images