Power load prediction method based on deep neural network

Abstract

The invention discloses a power load prediction method based on a deep neural network. The method comprises the steps of collecting historical load data; processing the historical load data by using aplurality of parallel convolutional neural network CNN components to obtain sequence data; introducing a parallel structure into a deep neural network model DNN; processing the sequence data througha recurrent neural network to obtain a hidden corresponding state list; and bringing other types of features into a prediction model to serve as input of the DNN part of the model, and carrying out load prediction. Different types of factors possibly influencing load consumption are modeled by using different types of neural network components; the plurality of convolutional neural network (CNN) components are used for learning rich feature representation from a historical load sequence, and then a recurrent neural component based on an LSTM is used for modeling variability and dynamics in historical loading such that the purpose of predicting the power load per hour one day ahead is achieved.

Description

technical field [0001] The invention relates to the field of power load forecasting, in particular to a power load forecasting method based on a deep neural network. Background technique [0002] As electricity plays an increasingly important role in economic development and industrial production as well as in the daily life of ordinary people, the future power grid is expected to provide unprecedented flexibility in the production, distribution and management of energy, which is increasingly required The ability to accurately perform short-term, small-scale electrical load and generation forecasting. One of the main characteristics of electricity is that once it is generated, it is difficult to store. In addition, the short-term power load demand varies greatly. It is affected by many factors. Accurate power load forecasting can reduce power waste, increase revenue, and maintain stable operation of the power grid system. Therefore, accurate Power load forecasting is critic...

AI Technical Summary

Problems solved by technology

[0012] The present invention mainly solves the technical problem that the original forecasting method does not collect complete data and does not consider holiday factors, and provides a power load forecasting method based on a deep neural network. factors and learn rich feature representations from historical loading sequences using multiple convolutional neural network (CNN) components, and then use LSTM-based recurrent neural components to model variability and dynamics in historical loading to achieve The purpose of hourly electric load forecasting one day in advance

Images