TW202248577A - Energy-saving control method and device for temperature regulation equipment capable of reducing power consumption by a building cooling and heating type prediction module and an optimal temperature setting module - Google Patents

Abstract

An energy-saving control method for temperature regulation equipment can predict the cooling and heating type of the specific building by a building cooling and heating type prediction module for an energy-saving control device according to the location of a specific building, outdoor temperature/humidity, indoor temperature/humidity and the operating status of temperature regulation equipment installed in the specific building, and determine the optimal set temperature with the best energy saving efficiency and output the optimal set temperature to the temperature regulation equipment in the specific building by an optimal temperature setting module for the energy-saving control device according to the heating and cooling type predicted by the building cooling and heating type prediction module, the current outdoor temperature/humidity of the specific building, and the normal power consumption of the temperature regulation equipment installed in the specific building so that the temperature regulation equipment operates at the optimal set temperature to reduce power consumption.

Description

Energy saving control method and device for temperature control equipment

The invention relates to a control method of temperature regulation equipment, in particular to an energy-saving control method of temperature regulation equipment capable of reducing power consumption.

In order to reduce the power consumption of indoor temperature control equipment (such as air-conditioning equipment and/or refrigeration or freezing equipment), an existing technology is based on the weather conditions and the corresponding power consumption when the temperature control equipment is set to different temperatures under the weather conditions , establish an experience value database in a controller of the temperature control equipment, for example, when the outdoor temperature is 30C/humidity 62%, and the air conditioner is set at 24C, 25C, 26C, 27C respectively, the corresponding power consumption is 45 degrees respectively , 43 degrees, 41 degrees, 42 degrees. In this way, the controller of the air-conditioning air conditioner timely inquires the experience value database according to the weather information (such as outdoor temperature 30C/humidity 60%), and knows that the set temperature with the least power consumption under this weather condition is 26C, that is, control The air conditioner operates at 26C to reduce electricity consumption.

However, in addition to the influence of the above weather conditions and set temperature on the electricity consumption of temperature control equipment, the characteristics of the building where the temperature control equipment is located, such as whether the building is prone to cooling or heating due to weather changes, will also affect the temperature. Regulate the set temperature and power consumption of the equipment. Therefore, it is necessary to take the characteristics of the building as one of the considerations when setting the temperature of the temperature control equipment, so that the temperature control equipment can reduce the power consumption more effectively while regulating the temperature. .

Therefore, the purpose of the present invention is to provide an energy-saving control method and device for temperature regulation equipment, which can comprehensively refer to the location and thermal characteristics of the building, the operating status of the temperature regulation equipment, and indoor and outdoor temperature/humidity parameters to control the temperature. The temperature control device operates at an optimal set temperature to reduce power consumption.

Therefore, the present invention provides an energy-saving control method for temperature-regulating equipment, which is used to control a temperature-regulating equipment installed in a specific building. The method is realized by an energy-saving control device for temperature-regulating equipment. The cold and heat type prediction module predicts that the specific building belongs to N (N is greater than 2 positive integer) one of the cooling and heating types; an optimal temperature setting module of the energy-saving control device is based on the type of cooling and heating predicted by the building cooling and heating type prediction module and the current situation of the specific building The outdoor temperature/humidity, the normal power consumption of the temperature control equipment in the specific building, determine an optimal set temperature with the best energy saving efficiency and output the optimal set temperature to the temperature in the specific building Regulating the device so that the temperature regulating device operates at the optimum set temperature.

In some embodiments of the present invention, the building cooling and heating pattern prediction module is a computer device using at least one building location, outdoor temperature/humidity historical data, indoor temperature/humidity historical data and equipment. Train a neural network model on the operation history data of the temperature control equipment in the at least one building, so as to learn and establish the correlation between each of the buildings and the N kinds of cold and hot types, so that the building after the training is completed The cold and heat type prediction module can predict the location of the specific building according to the location of the specific building, the current outdoor temperature/humidity, the current indoor temperature/humidity and the operating status of the temperature control equipment installed in the specific building. This hot and cold type.

In some implementations of the present invention, the optimal temperature setting module is a computer device that uses the collected historical data of the cold and heat type of at least one building, outdoor temperature/humidity data and information set in the at least one building. training a neural network model based on the historical data of electricity consumption of the temperature control equipment in the building, so that according to the historical data of the cold and heat type of the at least one building, the outdoor temperature/humidity data and the temperature set in the at least one building With the historical data of power consumption of the control equipment, learn and find out the temperature setting of the temperature control equipment can save the most energy (power saving) under various conditions of cold and heat, various outdoor temperature/humidity, and various power consumption conditions.

In some embodiments of the present invention, the energy-saving control device is a cloud server, which communicates with the temperature control device through a network.

In some embodiments of the present invention, the energy-saving control device is set in the temperature regulation device, and communicates with the temperature regulation device through wired or wireless means.

The effect of the present invention lies in: using the building cooling and heating pattern prediction module to predict the specific building according to the location of the specific building, outdoor temperature/humidity, indoor temperature/humidity and the operating status of the temperature control equipment in the specific building The heat and cold type of the object, and then the optimal temperature setting module is based on the heat and cold type predicted by the building's heat and cold type prediction module and the current outdoor temperature/humidity of the specific building, the temperature in the specific building The usual power consumption of temperature control equipment, controlling the temperature control equipment to operate at an optimal set temperature, can more accurately estimate the power consumption corresponding to the set temperature, so that the temperature control equipment can control the temperature more accurately and be more effective to reduce power consumption.

Before the present invention is described in detail, it should be noted that in the following description, similar elements are denoted by the same numerals.

Referring to Fig. 1, it is the main flow of the energy-saving control method of the temperature regulation equipment of the present invention, which is used to control the temperature regulation equipment 11 (at least one or more temperature control equipment), the specific building 1 can be a general residence, office building or store, etc., and the temperature control equipment 11 can be cold/heating air-conditioning equipment and/or refrigeration or freezing equipment; and the present embodiment is composed of an energy-saving control device 2, the energy-saving control device 2 can be a cloud or a remote server, and can communicate with the temperature control device 11 through a network (such as the Internet or the Internet of Things); the energy-saving control device 2 can also be a device At the near end, for example, a controller with computing power, a microcontroller or a microcomputer, etc., is provided in the temperature regulating device 11 .

The energy-saving control device 2 mainly includes a building cooling and heating type prediction module 21 and an optimal temperature setting module 22, and these two modules can be loaded and executed by a processor in the energy-saving control device 2 A software program or a firmware that can be programmed or embedded in a processor of the energy-saving control device 2, but not limited thereto.

And for the convenience of understanding this embodiment, the following description will be made by taking the temperature control device 11 as a cooling/heating air-conditioning device and the energy saving control device 2 as a cloud server as an example.

Firstly, when the temperature regulating device 11 is in operation, the energy-saving control device 2 will control the temperature regulating device 11 to operate in a timely manner (such as but not limited to every 1 hour, 2 hours, morning, afternoon, etc.) Therefore, when determining the optimal temperature setting, the energy-saving control device 2 will first obtain the location of the specific building 1, the current outdoor temperature/humidity, the current indoor temperature/humidity, and the specific building temperature. The operating status of the temperature control equipment 11 in the object 1 (such as but not limited to the current set temperature) and other information, and the above-mentioned information can be provided by the temperature control equipment 11 and/or the indoor temperature of the specific building 1 /humidity meter, outdoor temperature/humidity meter are provided to the energy-saving control device 2 through the temperature regulation equipment 11.

Then, as shown in step S1 of FIG. 1 , the building cooling and heating type prediction module 21 according to the location of the specific building 1 , the current outdoor temperature/humidity, the current indoor temperature/humidity and the operating status of the temperature regulation device 11, Predict which of the N (N is a positive integer greater than 1) cooling and heating types the specific building 11 belongs to. For example, the cooling and heating types can be divided into, but not limited to, such as the most cold and hot type, the easy cold and hot type, the difficult cold and hot type, the easy cold and low heat type, the easy heat but not cold type, the least cold and hot type, etc.; The state prediction module 21 will predict that the specific building 11 belongs to one of these cold and hot types, such as the easy-to-cool and heat-type, and provide the predicted cold-heat type (easy-to-cool and heat-type) to the optimal temperature setting model Group 22.

And in this embodiment, the building cooling and heating pattern prediction module 21 can be a computer device (not shown) using the location and outdoor temperature/humidity history of multiple (or at least one) buildings collected in advance. data, indoor temperature/humidity historical data and the operating history data of the temperature control equipment located in each of the buildings to train a neural network model (such as a deep neural network), so that learning and establishing each of the buildings and the N types The correlation between hot and cold types (such as the above-mentioned most easy to cold and hot, easy to cold and hot, not easy to cold and hot, easy to cold but not hot, easy to heat but not cold, and least cold and hot); The building cooling and heating type prediction module 21 can be based on the location of the specific building 1, the current outdoor temperature/humidity, the current indoor temperature/humidity and the operating status of the temperature control equipment 11 installed in the specific building 1, Predict the type of cold and heat that the specific building 1 belongs to.

Next, as shown in step S2 of FIG. 1 , the optimal temperature setting module 22 is based on the type of cooling and heating predicted by the building cooling and heating type prediction module 21 (easy to cool and heat) and the current temperature of the specific building 1. The outdoor temperature/humidity, the normal power consumption of the temperature control equipment 11 in the specific building 1, determine an optimal setting temperature that can make the temperature control equipment 11 have the best energy-saving efficiency, and output the optimal setting The temperature should be given to the temperature control device 11, so that the temperature control device 11 operates at the optimal set temperature to reduce power consumption. Wherein, the normal power consumption may refer to the average or median of the historical power consumption of the temperature regulating device 11 , such as the daily or monthly normal power consumption.

And in this embodiment, the optimum temperature setting module 22 can be a computer device (not shown) that utilizes the historical data of the cooling and heating patterns of multiple (or at least one) buildings collected in advance, the outdoor temperature /Humidity history data and the electricity consumption history data of the temperature control equipment installed in each building (such as electricity consumption every hour, every two hours, half a day, whole day, etc.) to train a neural network model ( For example, a deep neural network), so that according to the historical data of the cold and hot type of each building, the historical data of outdoor temperature/humidity, and the historical data of electricity consumption of the temperature control equipment in each of the buildings, learn and find out in various Under the conditions of hot and cold types, various outdoor temperatures/humidities, and various power consumption conditions, what temperature can be set by the temperature control device to save energy (power saving); thereby, the optimal temperature setting module 22 after training can be based on the input The cold and heat type, the current outdoor temperature/humidity of the specific building, and the normal power consumption of the temperature-regulating device 11 are used to calculate a power consumption of the temperature-regulating device 11 under the condition of the least power consumption (the most energy-saving). Optimum set temperature.

For example, the optimum temperature setting module 22 finds that if the set temperature is 25°C, the temperature regulation The power consumption of the device 11 will be 50 degrees per day. If the set temperature is 26C, the power consumption of the temperature control device 11 will be 40 degrees per day. If the set temperature is 27C, the power consumption of the temperature control device 11 will be 40 degrees per day. The temperature will be 44 degrees, and the optimal temperature setting module 22 will select 26C as the optimal temperature setting.

To sum up, the above embodiment uses the building cooling and heating type prediction module 21 to regulate the operating status of the equipment 11 according to the location of the specific building 1, the outdoor temperature/humidity, the indoor temperature/humidity, and the temperature in the specific building 1. , to predict a type of cooling and heating that the specific building 1 belongs to, and then the optimal temperature setting module 22 is based on the type of cooling and heating predicted by the building cooling and heating type prediction module and the current outdoor temperature of the specific building 1 /humidity, the usual power consumption of the temperature-regulating equipment 11 in the specific building 1, determine an optimal set temperature and control the temperature-regulating equipment 11 to operate at the optimal set temperature, and the location and temperature of the building As one of the considerations for controlling the set temperature of the temperature control equipment, it can more accurately estimate the power consumption corresponding to the set temperature, so that the temperature control equipment can control the temperature more accurately and reduce power consumption more effectively. Really reach effect and purpose of the present invention.

But what is described above is only an embodiment of the present invention, and should not limit the scope of the present invention. All simple equivalent changes and modifications made according to the patent scope of the present invention and the content of the patent specification are still within the scope of the present invention. Within the scope covered by the patent of the present invention.

1: specific building 11: Temperature control equipment 2: Energy saving control device 21: Building cooling and heating type prediction module 22: Optimal temperature setting module S1~S2: steps

Other features and effects of the present invention will be clearly shown in the implementation manner with reference to the drawings, wherein: Fig. 1 is the main process flow of an embodiment of the energy-saving control method of the temperature control equipment of the present invention; and FIG. 2 is a schematic block diagram of modules included in an embodiment of the energy-saving control device of the temperature regulating device of the present invention.

S1~S2: steps

Claims (10)

An energy-saving control method for temperature-regulating equipment, used to control a temperature-regulating equipment installed in a specific building, the method comprising: (A) A building cooling and heating type prediction module of an energy-saving control device is based on the location of the specific building, outdoor temperature/humidity, indoor temperature/humidity and the operating status of the temperature regulation equipment in the specific building, It is predicted that the specific building belongs to one of N (N is a positive integer greater than 2) cold and hot types; and (B) An optimal temperature setting module of the energy-saving control device is based on the heat and cold pattern predicted by the building heat and cold pattern prediction module, as well as the current outdoor temperature/humidity of the specific building, and the internal temperature of the specific building. The normal power consumption of the temperature-regulating equipment, determine an optimal set temperature with the best energy-saving efficiency and output the optimal set temperature to the temperature-regulating equipment in the specific building, so that the temperature-regulating equipment operates at the Optimum set temperature. The energy-saving control method of temperature regulation equipment as described in claim item 1, wherein the building cooling and heating type prediction module is a computer device using at least one building location, outdoor temperature/humidity historical data, indoor temperature/humidity data collected by a computer device. The humidity history data and the operation history data of the temperature control equipment installed in the at least one building train a neural network model to learn and establish the correlation between each of the buildings and the N kinds of cold and heat types, so that the training The completed building cooling and heating pattern prediction module can predict the specific building's location, current outdoor temperature/humidity, current indoor temperature/humidity, and the operating status of the temperature control equipment installed in the specific building. A specific building belongs to one of the N cold and hot types. The energy-saving control method for temperature-regulating equipment as described in Claim 1, wherein the optimal temperature setting module is a computer device using the historical data of the cold and heat type of at least one building, outdoor temperature/humidity data, and equipment. Training a neural network model based on the electricity consumption history data of the temperature control equipment in the at least one building, so that according to the historical data of the cold and heat type of the at least one building, outdoor temperature/humidity data and The historical data of electricity consumption of the temperature control equipment in the building can be used to learn and find out what temperature the temperature control equipment can set to save the most energy under various conditions of cold and heat, various outdoor temperature/humidity, and various power consumption conditions. The energy-saving control method for temperature-regulating equipment as described in Claim 1, wherein the energy-saving control device is a cloud server, which communicates with the temperature-regulating equipment through a network. The energy-saving control method of temperature-regulating equipment as described in Claim 1, wherein the energy-saving control device is set in the temperature-regulating equipment, and communicates with the temperature-regulating equipment through wired or wireless means. An energy-saving control device for temperature-regulating equipment, used to control a temperature-regulating equipment installed in a specific building, comprising: A building cooling and heating type prediction module, which predicts the specific building according to the location of the specific building, the current outdoor temperature/humidity, the current indoor temperature/humidity and the operating status of the temperature control equipment in the specific building belongs to one of the N types of hot and cold patterns; and An optimal temperature setting module, which is based on the heat and cold pattern predicted by the building cold and heat pattern prediction module, the current outdoor temperature/humidity of the specific building, and a setting of the temperature control equipment in the specific building. Determine an optimal set temperature with the best energy saving efficiency in general power consumption, and output the optimal set temperature to the temperature control device in the specific building, so that the temperature control device operates at the optimal set temperature. The energy-saving control device for temperature regulation equipment as described in claim 6, wherein the building cooling and heating type prediction module is collected by a computer device using at least one building location, outdoor temperature/humidity historical data, indoor temperature/humidity The humidity history data and the operation history data of the temperature control equipment installed in the at least one building train a neural network model to learn and establish the correlation between each of the buildings and the N kinds of cold and heat types, so that the training The completed building cooling and heating pattern prediction module can predict the specific building's location, current outdoor temperature/humidity, current indoor temperature/humidity, and the operating status of the temperature control equipment installed in the specific building. A specific building belongs to one of the N cold and hot types. The energy-saving control device for temperature regulation equipment as described in claim 6, wherein the optimal temperature setting module is collected by a computer device using the historical data of the cold and heat type of at least one building, outdoor temperature/humidity data and equipment Training a neural network model based on the electricity consumption history data of the temperature control equipment in the at least one building, so that according to the historical data of the cold and heat type of the at least one building, outdoor temperature/humidity data and The historical data of electricity consumption of the temperature control equipment in the building can be used to learn and find out what temperature the temperature control equipment can set to save the most energy under various conditions of cold and heat, various outdoor temperature/humidity, and various power consumption conditions. The energy-saving control device for temperature-regulating equipment as described in claim 6, wherein the energy-saving control device is a cloud server that communicates with the temperature-regulating equipment through the network. The energy-saving control device for temperature-regulating equipment as described in Claim 6, wherein the energy-saving control device is set in the temperature-regulating equipment, and communicates with the temperature-regulating equipment through wired or wireless means.

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