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The so-called Self-HVAC means that the HVAC algorithm that you learn by yourself is actually an algorithm that allows the HVAC system to have self-learning ability and can independently control it. Let’s talk about some specific key parts below.

First, data collection module

If you want this algorithm to learn by itself, you must first have sufficient data. We have to collect parameters such as indoor temperature and humidity, outdoor temperature and humidity, light, as well as current and pressure during the operation of the equipment. Various sensors need to be installed to collect data in a comprehensive way. For example, temperature sensors must be very accurate.

You may have questions here, are the accuracy of data varies greatly? In fact, if the temperature sensor accuracy is not enough, the monitored data has errors. If the algorithm learns to control it based on this error data, then the control will definitely be inaccurate.

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Second, model selection and construction section

You need to choose a model that is very suitable for this self-learning scenario, such as the neural network algorithm model, which can process various types of parameters, analyze the correlation between each other, and optimize the system control scheme. Just use a large amount of historical data to train this model repeatedly to make it as accurate as possible to complete the optimization function.

Some people may ask again, what are the differences in the choice of different models? If the algorithm model data that is not good or unsuitable is not easy to process and finally there are problems such or that when applied, it cannot achieve the precise control effect of self-learning.

Third, intelligent adjustment method

After data training, the algorithm model is stable and mature, it can control it according to the working conditions itself, but this algorithm will learn and adjust it according to the current operating data and various situations to make adjustments according to the current operating data and various situations. It can better adjust the air conditioner's compression mechanism cooling adjustment range. If the fixed parameters set may not be useful in certain periods of time, it cannot meet the current real-time energy-saving and temperature control goals, but this algorithm for self-learning can be used.

Some people are confused about where the difference between the efficiency of fixed parameter adjustment and self-adjustment can really go. Many times, some special situations or changes in working conditions as the environment changes over time, fixed parameters cannot be solved, but the advantages of adaptability can be clearly shown.

Please add details in the form of self-questioning

Question 1. Is it very expensive to install many sensors to collect data? In fact, the key to the cost is that some high-precision sensors of this type of equipment cost several thousand like imported high-precision temperature and humidity sensors, but the general requirements of this large number of popularization in China are not particularly high-precision sensors, which are actually very cheap, about dozens of dollars. In this way, you can choose cost-effective equipment from your own budget scale.

Question 2. What are the characteristics of the problems that arise during training? Most of the differences in equipment performance. If a hardware itself does not provide data, inaccurate learning, it will be biased, and the model will not be as good as the plan, so it will easily lead to a dead end or will be inflexible when it is trained and used.

Overall, I think this Self-HVAC has a very huge future space. It can indeed develop more and better in the future. Because energy conservation, environmental protection and improvement of user experience, it has super outstanding advantages. In the future, we can continue to develop more. Let's wait and see.