Visualization method for energy load prediction

Abstract

The invention belongs to the field of energy, and particularly relates to a visualization method for energy load prediction. The visualization method comprises the following steps: forming an energy load prediction model based on a classification and regression tree model and gradient lifting; inputting energy load historical data parameters and real-time parameters the formed energy load prediction model based on the classification and regression tree model and the gradient lifting; and performing visualization processing on a prediction path and a prediction result of the energy load prediction model based on the classification and regression tree model and the gradient lifting. According to the method, the energy load prediction process can be visually displayed.

Description

Technical field [0001] The present invention belongs to the field of energy, particularly relates to a method for the visualization of energy load prediction. Background technique [0002] For the energy sector, load forecasting is an important part of automation technology, accurate predictions can provide the basis for supply planning system, optimal scheduling, reduce energy consumption, improve operational safety and efficiency, so the correct prediction model, and enhance the prediction accuracy of great significance. [0003] With the conventional method to enhance the accuracy of the prediction, which also enhance the complexity of the model can be further reduced in explanation, so that the black box is a state prediction method between input and output. Unknown persons related to the prediction process, leading to predict when to question the results, you can not deduce the rationality of the results, the final results of the model can not be fully trusted. Thus, the mod...

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Problems solved by technology

[0004] Traditional energy load forecasting model building methods generally use time series methods, regression analysis methods, and expert systems. With the rise of artificial intelligence technology, some researchers have improved the forecasting accuracy through complex model-based methods such as neural networks and support vector machines; However, the above-mentioned visualization technologies for energy load forecasting focus on the visualization of the platform and some data, and do not visualize the entire forecasting process.

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