Multi-factor electrical load prediction method based on deep learning

Abstract

The invention discloses a multi-factor electrical load prediction method based on deep learning, and the method comprises the steps: firstly completing the acquisition and storage of data, including electrical load data and environmental influence data; preprocessing and standardizing the data based on abnormal data detection, autoregression interpolation and sequence data normalization of a k-proximity algorithm and an improved DBSCAN algorithm; then, propsing an improved CNN-LSTM electrical load prediction model, and firstly using a CNN feature extraction module to learn local features of input data; inputting the input data into an LSTM sequence learning model, and extracting sequence feature information of the input data; meanwhile, introducing a self-attention mechanism into the LSTM for learning features of a hidden layer of the LSTM, and extracting key features by distributing different attention weights, so that the final prediction precision is improved; and finally, predicting the electrical load. According to the invention, digital upgrading of a power grid can be promoted, personalized requirements of users are met, and industry correlation analysis, power generation dispatching, power consumption trend prediction, work and production resumption guidance and the like are realized.

Description

technical field [0001] The present invention relates to the intersecting field of artificial intelligence and electric load forecasting, and more specifically, a multi-factor user electric load forecasting method based on deep learning. Background technique [0002] In recent years, with the rapid and high-quality development of the national economy and the continuous improvement of people's living standards, the demand for electricity is also increasing. The country has also accelerated the deployment of power projects to ensure sufficient power supply. However, due to the backward power generation link of related power generation projects, decision makers in the power sector cannot accurately grasp the load changes of the power grid, and there will be problems such as decision-making mistakes, and they cannot maintain a reasonable power supply and demand relationship, resulting in a lot of waste of power resources. Predicting the user's electricity load has the following ...

AI Technical Summary

Problems solved by technology

Summarize the current similar research and technical findings: Most of the existing power grid load forecasting methods are biased towards traditional algorithms, such as regression analysis, time series methods, etc. These methods have defects and cannot consider complex factors such as meteorological data. Influence, the prediction result and the actual value are very different; or the method of using machine learning is only for a single data source, such as the current power load and historical power load. Although the short-term prediction effect is improved compared with the traditional method, it is often The load is greatly affected by environmental factors, such as weather, temperature, humidity, holidays, etc., which makes the prediction accuracy of the previous method not good, and the deviation of the prediction results is large

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