Method based on air conditioner load prediction

Abstract

The invention discloses a method based on air-conditioning load prediction, which predicts the air-conditioning load by fully mining the potential value of air-conditioning big data and combining historical data with real-time heating / cooling rate dynamic matching, significantly improves the energy efficiency and operation precision of an air-conditioning system, and achieves the goal of energy saving. And on the basis of a big data driven load prediction technology, compressor power output is optimized, energy waste is avoided, the electric charge cost of a user is reduced, and expenses are saved for families and enterprises in real time. Meanwhile, energy consumption of the air conditioning system directly affects carbon emission, invalid energy supply is reduced by accurately matching loads, greenhouse gas emission can be reduced, climate change can be assisted and slowed down, and the ecological environment is protected. The air conditioner energy-saving technology gives consideration to both economic benefits and environmental benefits, saves long-term use cost for users, promotes the industry to transform to green and low carbon, and is an important direction of technology upgrading and sustainable development in the current air conditioner field.

Description

Technical Field

[0001] The present invention relates to the technical field of energy-saving of air-conditioning room load prediction, and in particular to a method based on air-conditioning load prediction. Background Art

[0002] At present, air conditioners have become an indispensable device for consumers to adjust indoor temperature in summer and winter. As a large energy consumer, the core concept of energy saving for air conditioners is to pursue higher energy utilization in the entire energy development and utilization process, while reducing air conditioning expenses for families. Therefore, reducing air conditioning energy consumption is particularly urgent.

[0003] The importance of accurately predicting air conditioning cooling / heating loads is mainly reflected in the following two aspects:

[0004] It is crucial to accurately provide the appropriate amount of heating or cooling. If too much is provided, it will not only waste energy, but also cause the room temperature to be too low or too high, making people feel uncomfortable.

[0005] If the room load conditions can be known in advance, the air conditioner can be precisely controlled according to the severity of the room load, thereby reducing overshoot of room temperature and reducing room temperature fluctuations.

[0006] The cooling load of air conditioning refers to the cooling demand that needs to be supplied to the air-conditioned area in order to maintain a suitable temperature in the area. This includes the heat that enters the room through the building envelope of the room and the heat carried by the outdoor air infiltrating into the room.

[0007] The heat load of air conditioning refers to the heat demand that needs to be supplied to the air-conditioned area in order to maintain the temperature balance of the area. This includes the solar radiation heat that enters the room through the building envelope of the room, as well as the heat emitted by the lighting equipment, human body and other equipment or appliances in the room.

[0008] The wet load of air conditioning refers to the amount of moisture that needs to be removed from the indoor air in order to maintain a certain relative humidity in the room. This includes the moisture emitted by various equipment, appliances, wet surfaces or liquid surfaces in the room, as well as the moisture emitted by various materials or beverages.

[0009] Air enthalpy plays a key role in the heat exchange process of air conditioning systems. In air conditioning systems, air circulates through condensers and evaporators to regulate indoor temperature. The refrigerant absorbs heat from the indoor air in the evaporator, increasing the air enthalpy, and then releases the heat to the condenser in the outdoor environment, reducing the air enthalpy. By controlling key components in the air conditioning system, such as the compressor, evaporator, and condenser, effective regulation of air temperature can be achieved. The design and operation of the air conditioning system must fully consider the changes in air enthalpy to ensure that the system can efficiently achieve cooling or heating functions.

[0010] Air conditioning load can be divided into static load and dynamic load:

[0011] Static load mainly includes factors such as room area, room height, wall thickness, wall material and room orientation.

[0012] Dynamic loads include changes in outdoor temperature, outdoor humidity, indoor temperature and humidity when the computer is turned on, whether the room is exposed to direct sunlight, heat sources in the room, and human activities. Summary of the invention

[0013] The present invention predicts the air conditioning load based on historical user equipment data, and dynamically calibrates the load through the heating / cooling rate after startup. Each time the air conditioner is turned on, the system calculates the air enthalpy value of the current room, and includes the outdoor temperature, outdoor humidity and the rate of room heating / cooling by 1°C into the load database. When the air conditioner is running at full power, the rate of room heating / cooling by 1°C is recorded, and then matched and searched in the historical database in combination with the current outdoor temperature, outdoor humidity, indoor temperature, indoor humidity and heating / cooling rate. If the current environmental parameters are similar to the historical environmental parameters, and the rate of heating / cooling by 1°C is also similar, it can be inferred that the current air conditioning load is similar to the load of the similar historical environment, thereby predicting the load of this air conditioning startup. Subsequently, the predicted load is sent to the electric control board, and the electric control board adjusts the compressor power according to the predicted load. When the compressor power matches the room environmental load, it can effectively avoid the waste of electric energy and achieve energy saving and power saving. In summer and winter, the air conditioner is used for a long time. Each time the air conditioner is turned on, part of the electricity is saved, which can significantly reduce the user's electricity bill expenditure. Air conditioning energy saving aims to reduce the energy consumption of air conditioning systems through a series of measures, reduce energy consumption and operating costs, and reduce the impact on the environment.

[0014] The purpose of the present invention is to match and search the load database of the user's historical air-conditioning startup, the temperature increase / decrease rate, the current environmental parameters and the temperature increase / decrease rate, so as to predict the air-conditioning load of this startup.

[0015] The present invention achieves the above-mentioned purpose through the following technical scheme: each time a user turns on the air conditioner, the current environmental parameters and the heating / cooling rate are established into a historical load database. When the user turns on the air conditioner, the current environmental parameters and the heating / cooling rate are read, and the air conditioning load of the air conditioner is predicted based on the historical load database, specifically:

[0016] A method based on air conditioning load prediction includes the following steps:

[0017] Step 1: Every time the user turns on the air conditioner, the current operating parameters are obtained in real time, including: outdoor temperature, outdoor humidity, indoor humidity, and indoor temperature, and entered into the load database;

[0018] Step 2, calculating the current enthalpy value of the indoor air according to the indoor temperature and indoor humidity;

[0019] Step 3, set the air conditioner to run at full power, and record the current rate of temperature increase / decrease by 1°C, and enter it into the load database;

[0020] Step 4, predict the load of turning on the air conditioner this time: substitute the current air enthalpy value, outdoor temperature, outdoor humidity, and heating / cooling rate into the historical load data, and search for air enthalpy value, outdoor temperature, outdoor humidity, and heating / cooling rate that are close to the history, and then read the load W, and use this load W as the predicted load for turning on the air conditioner this time.

[0021] As long as the environmental conditions are similar, the loads are also similar.

[0022] A further solution is that in step 1, each time the machine is turned on, the environmental parameters and the heating / cooling rates are automatically entered into the historical load database, and the matching samples are dynamically updated.

[0023] A further solution is that in step 2, the calculation formula is as follows:

[0024] h=1.005·t+ω·(2501+1.88·t)

[0025] h is the enthalpy of air. The specific heat capacity of dry air is 1.005 kJ / (kg·℃), the specific heat capacity of water vapor is 1.88 kJ / (kg·℃), and the latent heat of vaporization is 2501 kJ / kg at 0℃;

[0026] t is the indoor temperature in °C;

[0027] ω is the indoor relative humidity, expressed in kg water vapor / kg dry air, and can be calculated using the following formula:

[0028]

[0029] Where P v P is the partial pressure of water vapor, and P is the total pressure. P is usually calculated using the saturated water vapor pressure table or Antoine equation v .

[0030] P can be estimated by the altitude H (meters), the formula is:

[0031]

[0032] A further solution is that in step 4, the user can manually correct the predicted load value and feed the correction result back to the historical database for subsequent matching optimization.

[0033] A further solution is that in step 1, the cooling / heating mode is automatically switched according to the outdoor temperature, and the historical load database of the corresponding season is called.

[0034] A further solution is that in step 4, the reference conditions for matching determination include:

[0035] The difference in indoor air enthalpy is ≤5%;

[0036] Outdoor temperature and humidity difference ≤ ±2℃ and ±5%;

[0037] The difference in heating / cooling rate is ≤±10%.

[0038] The beneficial effects of the present invention are:

[0039] The present invention provides a method based on air conditioning load prediction, which fully explores the potential value of air conditioning big data, combines historical data with real-time heating / cooling rate to dynamically match and predict air conditioning load, significantly improves the energy efficiency and operation accuracy of the air conditioning system, and achieves energy saving goals. Based on the load prediction technology driven by big data, the compressor power output is optimized, energy waste is avoided, the user's electricity cost is reduced, and expenses are saved for families and enterprises in real time. At the same time, the energy consumption of the air conditioning system directly affects the carbon emissions. The present invention can reduce ineffective energy supply by accurately matching the load, reduce greenhouse gas emissions, help mitigate climate change, and protect the ecological environment. Air conditioning energy-saving technology takes into account both economic and environmental benefits, which not only saves users long-term use costs, but also promotes the industry's transformation to green and low-carbon. It is an important direction for technology upgrading and sustainable development in the current air conditioning field. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0041] Figure 1 This is a functional system block diagram of the present invention.

[0042] Figure 2 This is a logical flow chart of predicting air conditioning according to the present invention. DETAILED DESCRIPTION

[0043] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0044] In any embodiment, if Figure 1 As shown, a method based on air conditioning load prediction of the present invention comprises the following steps:

[0045] Step 1, each time the user turns on the air conditioner, the current operating parameters are obtained in real time, including: outdoor temperature, outdoor humidity, indoor humidity, and indoor temperature are entered into the load database; in step 1, each time the air conditioner is turned on, the current environmental parameters and the heating / cooling rate are automatically entered into the historical load database, and the matching samples are dynamically updated.

[0046] It can also automatically switch cooling / heating mode according to the outdoor temperature and call the historical load database of the corresponding season.

[0047] Step 2, calculating the current enthalpy value of the indoor air according to the indoor temperature and indoor humidity; in step 2, the calculation formula is as follows:

[0048] h=1.005·t+ω·(2501+1.88·t)

[0049] h is the enthalpy of air. The specific heat capacity of dry air is 1.005 kJ / (kg·℃), the specific heat capacity of water vapor is 1.88 kJ / (kg·℃), and the latent heat of vaporization is 2501 kJ / kg at 0℃;

[0050] t is the indoor temperature in °C;

[0051] ω is the indoor relative humidity, expressed in kg water vapor / kg dry air, and can be calculated using the following formula:

[0052]

[0053] Where P v P is the partial pressure of water vapor, and P is the total pressure. P is usually calculated using the saturated water vapor pressure table or Antoine equation v .

[0054] P can be estimated by the altitude H (meters), the formula is:

[0055]

[0056] An example of enthalpy calculation is as follows:

[0057] If the air temperature is 25°C and the specific humidity is 0.01kg / kg, the enthalpy is:

[0058] h=1.005×25+0.01×(2501+1.88×25)=25.125+0.01×2548=50.6kJ / kg

[0059] According to the above description, as long as the air enthalpy value is the same, the internal energy of the room air is the same.

[0060] Step 3, set the air conditioner to run at full power, and record the current rate of temperature increase / decrease by 1°C, and enter it into the load database;

[0061] Step 4, predict the load of this air conditioning: Substitute the current air enthalpy value, outdoor temperature, outdoor humidity, and heating / cooling rate into the historical load data, and search for air enthalpy values, outdoor temperature, outdoor humidity, and heating / cooling rates that are close to the history, and then read the load W, and use this load W as the predicted load for this air conditioning. As long as the environmental conditions are close, the load is also close.

[0062] Users can manually modify the predicted load value and feed the modified result back to the historical database for subsequent matching optimization.

[0063] The matching reference conditions may include:

[0064] The difference in indoor air enthalpy is ≤5%;

[0065] Outdoor temperature and humidity difference ≤ ±2℃ and ±5%;

[0066] The difference in heating / cooling rate is ≤±10%.

[0067] Figure 2 Flowchart describing the forecasted load:

[0068] The inherent characteristics of the room (such as area and size) are fixed. As long as the temperature and humidity are fixed, that is, the air enthalpy value of the room is determined, the static load of the room is determined.

[0069] The dynamic load changes in real time and cannot be predicted in advance. It can only be measured and learned in real time. The present invention uses a rate of increasing / decreasing the temperature by 1°C to determine the dynamic load.

[0070] Therefore, each time the user turns on the air conditioner, the current outdoor temperature and the current outdoor humidity are read and entered into the historical load database. At the same time, the indoor temperature and the indoor humidity are read and entered into the database, and the indoor temperature and the indoor humidity are substituted into the enthalpy value to calculate the current indoor air enthalpy value and enter the air enthalpy value into the load database. Then the air conditioner electronic control sets the air conditioner to run at full power, and records the rate at which the indoor temperature rises / falls by 1°C and enters it into the load database. At the same time, the current load is recorded in the load data, and the air enthalpy value, outdoor temperature, outdoor humidity and the rate of rise / fall are close to the historical ones. Then the load W is read and used as the predicted load for this air conditioner startup.

[0071] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims. It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not further explain various possible combinations. In addition, the various different embodiments of the present invention can also be combined arbitrarily, as long as they do not violate the concept of the present invention, they should also be regarded as the contents disclosed by the present invention.

Claims

1. A method based on air conditioning load prediction, characterized in that: The following steps are involved: Step 1: Every time the user turns on the air conditioner, the current operating parameters are obtained in real time, including: outdoor temperature, outdoor humidity, indoor humidity, and indoor temperature, and entered into the load database; Step 2, calculating the current enthalpy value of the indoor air according to the indoor temperature and indoor humidity; Step 3, set the air conditioner to run at full power, and record the current rate of temperature increase / decrease by 1°C, and enter it into the load database; Step 4, predict the load of turning on the air conditioner this time: substitute the current air enthalpy value, outdoor temperature, outdoor humidity, and heating / cooling rate into the historical load data, and search for air enthalpy value, outdoor temperature, outdoor humidity, and heating / cooling rate that are close to the history, and then read the load W, and use this load W as the predicted load for turning on the air conditioner this time.

2. The method based on air conditioning load prediction according to claim 1, characterized in that: In step 1, each time the machine is turned on, the environmental parameters and the heating / cooling rate are automatically entered into the historical load database, and the matching samples are dynamically updated.

3. The method based on air conditioning load prediction according to claim 1, characterized in that: In step 2, the calculation formula is as follows: h=1.005·t+ω·(2501+1.88·t) h is the enthalpy of air. The specific heat capacity of dry air is 1.005 kJ / (kg·℃), the specific heat capacity of water vapor is 1.88 kJ / (kg·℃), and the latent heat of vaporization is 2501 kJ / kg at 0℃; t is the indoor temperature in °C; ω is the indoor relative humidity, expressed in kg water vapor / kg dry air, and can be calculated using the following formula: Where P v P is the partial pressure of water vapor, and P is the total pressure. P is usually calculated using the saturated water vapor pressure table or Antoine equation v .

4. The method based on air conditioning load prediction according to claim 1, characterized in that: In step 4, the user can manually modify the predicted load value and feed the modified result back to the historical database for subsequent matching optimization.

5. The method based on air conditioning load prediction according to claim 1, characterized in that: In step 1, the cooling / heating mode is automatically switched according to the outdoor temperature, and the historical load database of the corresponding season is called.

6. The method based on air conditioning load prediction according to claim 1, characterized in that: In step 4, the matching determination reference conditions include: The difference in indoor air enthalpy is ≤5%; Outdoor temperature and humidity difference ≤ ±2℃ and ±5%; The difference in heating / cooling rate is ≤±10%.

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