PMU primary frequency modulation load forecasting method based on LSTM and associative full-connected neural network

Abstract

The present invention discloses a PMU primary frequency modulation load forecasting method based on the associated fully connected neural network and LSTM, in particular comprising the following steps: selecting training data, verifying the data, establishing a joint neural network model, training the joint neural network model, and inputting the forecasting sample set into the joint neural network model trained. The method of the invention considers the correlation between the historical data of the load and the power in the ultra-short-term load forecasting, adopts the structure of the LSTMneural network and the fully connected neural network, and effectively solves the problem of long-term dependency. The invention also has the advantages of simple algorithm, short running time and high prediction accuracy, and provides a solid guarantee for the stable operation of the power network.

Description

technical field [0001] The invention belongs to the technical field of smart grid control and power forecasting methods, in particular to a PMU primary frequency modulation load forecasting method based on an associated fully connected neural network and LSTM. Background technique [0002] In recent years, the improvement of my country's distribution automation level and the advancement of intelligent distribution network construction have provided various data and technical support for the complex load forecasting of distribution networks. Accurate load forecasting can ensure the stability and security of the power system, and improve the economic and social benefits of the power grid. [0003] Scholars at home and abroad have proposed many load forecasting methods, which can be roughly divided into three categories: traditional optimization algorithms, heuristic algorithms, and artificial intelligence algorithms. Among them, the artificial intelligence algorithms related ...

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Problems solved by technology

[0004] Although the above methods have been proven to have good performance in the field of short-term load forecasting, there are still some shortcomings: ① support vector machine is difficult to handle large-scale training samples; ② wavelet transform algorithm is usually difficult to combine with artificial neural network; ③ fuzzy system Does not have self-learning ability, and fuzzy rules rely more on expert experience; ④Because the load in the urban core area is greatly affected by relevant factors, the load in different areas presents different regularities, and the robustness of the prediction method is poor; ⑤Rough analysis of load influencing factors, etc. As a result, load forecasting takes a long time, the forecasting accuracy is poor, and the practicability is not strong

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