Energy consumption prediction parameter optimization method of building energy supply device

Abstract

The invention relates to an energy consumption prediction parameter optimization method of a building energy supply device. The method comprises the following steps that: parameter data of an energy supply device in a building and parameter data which may have an effect on energy consumption of the device are collected; state changes and state change time of the device are collected; the energy consumption by the state changes is calculated; the above data are used to generated a data set, so as to establish a device prediction model; the model can predict the energy consumption and time used by the state changes of the device in different situations, so that a user is provided with an optimal dispatching plan and working order of the device; and minimum energy consumption, and shortest delivery time or completion time are achieved. Compared with the prior art, the method of the invention is capable of recording the energy consumption of the device, related influence factors, and state change information of the device, is capable of choosing the proper parameters influencing the device energy consumption according to different building characteristics, and is also capable of eliminating the influence of using improper parameters on the energy consumption prediction of the building energy supply device.

Description

technical field [0001] The invention relates to a method for optimizing prediction parameters, in particular to a method for optimizing energy consumption prediction parameters of building energy supply equipment. Background technique [0002] Building energy consumption is huge, especially in large shopping malls, laboratories, office buildings, etc. At present, the sub-item collection of energy consumption data of energy supply equipment in buildings is a prerequisite for understanding the energy consumption of buildings and finding energy waste points. With the implementation of national energy conservation and emission reduction policies, various regions have collected energy consumption data of many buildings, but mainly to realize the sub-item measurement of energy consumption and the statistical display of sub-item energy consumption data. At the same time, the current data collection methods mostly focus on the collection of energy consumption data itself, and less ...

AI Technical Summary

Problems solved by technology

These methods are highly specialized and have a large amount of calculation, and are not universal for different buildings, so the simulation calculation needs to be re-calculated

In most cases, some researchers will make judgments based on experience when determining the influencing factors of air-conditioning energy consumption, and consider the influencing factors too much, which makes the input data of the energy consumption prediction program huge, takes up a lot of memory, and calculates slowly

In addition, improper selection of influencing factors will also have a negative impact on the prediction results.

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