Electric boiler heat compensation and storage cost optimization method and device based on electric power big data

Abstract

The invention provides an electric boiler heat compensation and storage cost optimization method and device based on electric power big data. The method comprises the following steps: acquiring weather forecast data, equipment operation data and electric power acquisition data, and preprocessing the acquired data; extracting cost features and operation efficiency features, performing dimension reduction processing on the extracted features, and dividing the features into training set data and test set data; establishing a BP neural network model, inputting the training set data into the BP neural network model for training, and taking the trained neural network model as a heat compensation and storage strategy model; and inputting the test set data into the heat compensation and storage strategy model, and outputting the energy consumption cost. In this way, the operation strategy and the energy-saving and consumption-reducing effect of the heat exchange station can be fitted, the fitting efficiency is obviously improved, and the energy consumption efficiency of the heat exchange station is improved.

Description

technical field [0001] The present invention generally relates to the field of electric power big data, and more specifically relates to a method and device for optimizing the cost of electric boiler supplementary heat storage based on electric power big data. Background technique [0002] Centralized heating is the main way of heating in winter in the northern region. Due to the continuous expansion of heating area, the loss in the heat network transmission process continues to increase, resulting in the general problem of insufficient heating capacity of heat exchange stations at the end of heating. In order to solve this problem, many heat exchange stations have added electric energy supplementary heating devices, which use electric energy to supplement heating inside the heat exchange station, so as to improve the problem of insufficient heat supply capacity of the heat exchange station at the end of the heat supply. For example, the flow rate is automatically adjusted a...

AI Technical Summary

Problems solved by technology

[0003] However, due to the complexity of the system and the large number of devices that need to be controlled, all manual control is ineffective, and there is a lack of algorithm models to assist, resulting in low operating efficiency of the supplementary heating system, and excessive flow rate will cause heat loss

And since it is necessary to sense the drop in return water temperature and then adjust it, there is a lag in the adjustment time, and it is impossible to predict the adjustment strategy based on real-time weather conditions

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