TWI746087B - Air conditioning system control method - Google Patents

Air conditioning system control method Download PDF

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TWI746087B
TWI746087B TW109125241A TW109125241A TWI746087B TW I746087 B TWI746087 B TW I746087B TW 109125241 A TW109125241 A TW 109125241A TW 109125241 A TW109125241 A TW 109125241A TW I746087 B TWI746087 B TW I746087B
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air
temperature
indoor temperature
conditioning
air conditioner
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TW202204832A (en
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黃敬群
趙梓吟
王榮進
羅時麒
鍾振武
梁健政
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內政部建築研究所
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一種空調系統控制方法,用以控制該空調系統的空調設定溫度,由一空調控制裝置來實施,包含以下步驟:(A)根據多個空調可能設定溫度及至少一室內溫度感測值,利用一用以預測一預設時間後的室內溫度的室內溫度預測模型,獲得多個分別對應該等空調可能設定溫度且在一預測時間的預測室內溫度組;(B)根據一包括多個期望室內溫度的期望室內溫度資料及該預測時間,從該等期望室內溫度中,獲得一目標期望室內溫度;及(C) 根據多個相關於該等預測室內溫度組分別與該目標期望室內溫度的誤差值,從該等空調可能設定溫度獲得一目標空調設定溫度。An air conditioning system control method is used to control the air conditioning set temperature of the air conditioning system. It is implemented by an air conditioning control device and includes the following steps: (A) According to the possible set temperature of multiple air conditioners and at least one indoor temperature sensing value, use one The indoor temperature prediction model used to predict the indoor temperature after a preset time, to obtain a plurality of predicted indoor temperature groups corresponding to the possible set temperatures of the air conditioners and a predicted time; (B) according to a set of expected indoor temperatures Obtain a target expected indoor temperature from the expected indoor temperature data and the predicted time; and (C) according to a plurality of error values related to the predicted indoor temperature groups and the target expected indoor temperature , Obtain a target air conditioner set temperature from the possible set temperatures of the air conditioners.

Description

空調系統控制方法Air conditioning system control method

本發明是有關於一種控制方法,特別是指一種空調系統控制方法。The present invention relates to a control method, in particular to a control method of an air conditioning system.

隨著近年來科技的蓬勃發展,追求舒適的生活環境也受到人們的逐漸重視,畢竟多數人絕大部分的時間皆處於室內空間。而得力於空調系統的進步與發達,使得室內環境的調節與控制更加便利,在享有冬暖夏涼與優良空氣品質之同時,更能大大提升工作效率與維持健康的生理機能。With the rapid development of science and technology in recent years, the pursuit of a comfortable living environment has also been gradually valued by people. After all, most people spend most of their time indoors. Thanks to the advancement and development of air-conditioning systems, the adjustment and control of the indoor environment are more convenient. While enjoying warm winter and cool summer and excellent air quality, it can greatly improve work efficiency and maintain healthy physiological functions.

傳統上,空調系統之控制乃預測平均表決(Predicted Mean Vote, PMV)等室內舒適性指標,並配合節能策略等訂定控制的目標,控制人員再依據經驗累積和傳承配合訂定控制的目標進行控制。然而,控制成效因人而異,造成現有空調系統之控制,無法時時刻刻達到目標室內溫度,導致無法即時反應空間內活動人員的感受問題。Traditionally, the control of the air-conditioning system is based on indoor comfort indicators such as Predicted Mean Vote (PMV), and the control goals are set in conjunction with energy-saving strategies. The control personnel then set the control goals based on the accumulation of experience and inheritance. control. However, the control effect varies from person to person, resulting in the control of the existing air-conditioning system, unable to reach the target indoor temperature all the time, resulting in the inability to instantly respond to the feelings of the people moving in the space.

因此,本發明的目的,即在提供一種能自動控制空調系統的空調系統控制方法。Therefore, the object of the present invention is to provide an air conditioning system control method that can automatically control the air conditioning system.

於是,本發明空調系統控制方法,用以控制該空調系統的空調設定溫度,由一空調控制裝置來實施,該空調控制裝置電連接該空調系統及至少一設置於一室內空間的室內溫度感測器,該至少一室內溫度感測器用以感測室內溫度,以產生至少一對應該至少一室內溫度感測器的室內溫度感測值,該空調控制裝置儲存多個空調可能設定溫度及一期望室內溫度資料,該期望室內溫度資料包括多個第一時段及多個分別對應該等第一時段的期望室內溫度,包含一步驟(A)、一步驟(B),及一步驟(C)。Therefore, the air-conditioning system control method of the present invention is used to control the air-conditioning set temperature of the air-conditioning system, which is implemented by an air-conditioning control device that is electrically connected to the air-conditioning system and at least one indoor temperature sensor set in an indoor space The at least one indoor temperature sensor is used to sense the indoor temperature to generate at least one pair of indoor temperature sensing values corresponding to the at least one indoor temperature sensor. The indoor temperature data includes a plurality of first time periods and a plurality of desired indoor temperatures corresponding to the first time periods, including a step (A), a step (B), and a step (C).

該步驟(A)中,該空調控制裝置根據該等空調可能設定溫度及該至少一室內溫度感測值,利用一用以預測一預設時間後的室內溫度的室內溫度預測模型,獲得多個分別對應該等空調可能設定溫度且在一預測時間的預測室內溫度組,其中該預測時間為一當前時間加上該預設時間,每一預測室內溫度組包括至少一分別對應該至少一室內溫度感測值的預測室內溫度。In the step (A), the air-conditioning control device uses an indoor temperature prediction model for predicting the indoor temperature after a preset time according to the possible set temperatures of the air-conditioners and the at least one indoor temperature sensing value to obtain a plurality of The predicted indoor temperature groups corresponding to the possible set temperatures of the air conditioners and a predicted time, wherein the predicted time is a current time plus the preset time, and each predicted indoor temperature group includes at least one corresponding to at least one indoor temperature The predicted indoor temperature of the sensed value.

該步驟(B)中,該空調控制裝置根據該期望室內溫度資料及該預測時間,從該等期望室內溫度中,獲得一目標期望室內溫度,其中該預測時間位於該目標期望室內溫度對應的第一時段In the step (B), the air-conditioning control device obtains a target expected indoor temperature from the expected indoor temperatures according to the expected indoor temperature data and the predicted time, wherein the predicted time is located at the second corresponding to the target expected indoor temperature. A period of time

該步驟(C)中,該空調控制裝置根據多個相關於該等預測室內溫度組分別與該目標期望室內溫度的誤差值,從該等空調可能設定溫度獲得一目標空調設定溫度。In the step (C), the air-conditioning control device obtains a target air-conditioning set temperature from the possible set temperatures of the air-conditioners according to a plurality of error values related to the predicted indoor temperature groups and the target desired indoor temperature.

本發明的功效在於:藉由該空調控制裝置預測出該等空調可能設定溫度導致該預設時間後的該等預測室內溫度組,並根據該等預測室內溫度組與該目標期望室內溫度的誤差值,從該等空調可能設定溫度獲得該目標空調設定溫度,以達到每一時段的目標期望室內溫度,並即時反應空間內活動人員的感受。The effect of the present invention is that the air-conditioning control device predicts that the air conditioners may set the temperature to cause the predicted indoor temperature groups after the preset time, and based on the error between the predicted indoor temperature groups and the target expected indoor temperature Value, the target air-conditioning setting temperature is obtained from the possible setting temperatures of the air-conditioners, so as to reach the target desired indoor temperature in each time period, and to reflect the feelings of the active personnel in the space in real time.

在本發明被詳細描述之前,應當注意在以下的說明內容中,類似的元件是以相同的編號來表示。Before the present invention is described in detail, it should be noted that in the following description, similar elements are denoted by the same numbers.

參閱圖1,說明用以實施本發明空調系統控制方法的一實施例之一空調系統11及一電連接該空調系統11的空調控制裝置12。Referring to FIG. 1, an air-conditioning system 11 and an air-conditioning control device 12 electrically connected to the air-conditioning system 11 are described to implement an embodiment of the air-conditioning system control method of the present invention.

該空調系統11包括一空氣調節箱(Air Handling Unit, AHU)111、多個變風量系統(Variable Air Volume System, VAV)112、多個小型冷風機(Fan Coil Unit, FCU)113,及一外氣空調箱(Make-Up Air Unit, MAU)114。值得注意的是,在本實施例中,該空調系統11例如包括5台變風量系統112及5台小型冷風機113,且該等設置於靠窗區域,在其他實施方式中,變風量系統112及小型冷風機113的數量亦可分別為1,但不以此為限。The air conditioning system 11 includes an Air Handling Unit (AHU) 111, a plurality of Variable Air Volume System (VAV) 112, a plurality of small air coolers (Fan Coil Unit, FCU) 113, and an external Make-Up Air Unit (MAU) 114. It is worth noting that in this embodiment, the air conditioning system 11 includes, for example, 5 variable air volume systems 112 and 5 small air coolers 113, and these are arranged in the window area. In other embodiments, the variable air volume system 112 The number of the air cooler 113 and the small air cooler 113 can also be 1, but is not limited to this.

該空調控制裝置12還電連接多個設置於一室內空間的室內溫度感測器13、多個設置於該空調系統11的空調感測器14、多個插座電功耗感測器15,及一晴雨感測器16。值得注意的是,在本實施例中,例如有5個室內溫度感測器13,28個空調感測器14,2個插座電功耗感測器15,在其他實施方式室內溫度感測器13及插座電功耗感測器15數量亦可分別為1,但不以此為限。The air-conditioning control device 12 is also electrically connected to a plurality of indoor temperature sensors 13 arranged in an indoor space, a plurality of air-conditioning sensors 14 arranged in the air-conditioning system 11, a plurality of socket power consumption sensors 15, and A sunny rain sensor 16. It is worth noting that, in this embodiment, for example, there are 5 indoor temperature sensors 13, 28 air-conditioning sensors 14, and 2 socket power consumption sensors 15. In other embodiments, indoor temperature sensors The number of 13 and the socket power consumption sensor 15 can also be 1, but is not limited to this.

該等室內溫度感測器13用以感測室內溫度,以產生多個對應該等室內溫度感測器13的室內溫度感測值。值得注意的是,在本實施例中,該等室內溫度感測器13分別設置於該等變風量系統112,用以分別感測該等變風量系統112的回風溫度,以作為在該室內空間中5個不同位置的室內溫度,但不以此為限。The indoor temperature sensors 13 are used for sensing indoor temperature to generate a plurality of indoor temperature sensing values corresponding to the indoor temperature sensors 13. It is worth noting that, in this embodiment, the indoor temperature sensors 13 are respectively disposed in the variable air volume systems 112 to sense the return air temperature of the variable air volume systems 112 as the indoor temperature Indoor temperature at 5 different locations in the space, but not limited to this.

該等空調感測器14用以感測該空調系統11的狀態,以產生多筆空調系統狀態感測資料。值得注意的是,在本實施例中,共有28筆空調系統狀態感測資料分別對應28個空調感測器14,且28個空調感測器14中有1個設置於該空氣調節箱111,用以感測該空氣調節箱111封管中的壓力大小;5個分別設置於該等變風量系統112,用以分別感測該等變風量系統112是否進入節能模式;5個分別設置於該等變風量系統112,用以分別感測該等變風量系統112之風量阻尼開度;5個分別設置於該等小型冷風機113,用以分別感測該等小型冷風機113是否啟動;5個分別設置於該等小型冷風機113,用以分別感測該等小型冷風機113之冰水閥開度;5個分別設置於該等小型冷風機113,用以分別感測該等小型冷風機113的回風溫度;1個設置於該外氣空調箱114,用以感測該外氣空調箱114的輸出氣體的溫度;1個設置於該外氣空調箱114,用以感測該外氣空調箱114的吸入外部氣體的溫度,但不以此為限。The air-conditioning sensors 14 are used to sense the state of the air-conditioning system 11 to generate multiple pieces of air-conditioning system state sensing data. It is worth noting that in this embodiment, there are 28 air-conditioning system state sensing data corresponding to 28 air-conditioning sensors 14, and one of the 28 air-conditioning sensors 14 is set in the air-conditioning box 111. It is used to sense the pressure in the sealed pipe of the air conditioning box 111; 5 are respectively set in the variable air volume systems 112 to respectively sense whether the variable air volume systems 112 enter the energy-saving mode; 5 are respectively set in the variable air volume systems 112 The constant variable air volume system 112 is used to respectively sense the opening of the air volume damping of the variable air volume systems 112; five are respectively arranged on the small air coolers 113 to respectively sense whether the small air coolers 113 are activated; 5 One is respectively installed on the small air coolers 113 to sense the opening of the ice water valve of the small air coolers 113; 5 are respectively installed on the small air coolers 113 to sense the small cold air The return air temperature of the machine 113; one is installed in the outside air conditioning box 114 to sense the temperature of the output gas of the outside air conditioning box 114; one is installed in the outside air conditioning box 114 to sense the The temperature of the inhaled outside air of the outside air conditioning box 114, but not limited to this.

該等插座電功耗感測器15用以感測該室內空間的插座的供電量,以產生多筆分別對應該等插座電功耗感測器15的插座功耗資料。The socket power consumption sensors 15 are used for sensing the power supply of the sockets in the indoor space to generate a plurality of socket power consumption data corresponding to the socket power consumption sensors 15 respectively.

該晴雨感測器16用以感測是否下雨,以產生一指示是否下雨的晴雨感測資料。The sunny and rainy sensor 16 is used to sense whether it is raining or not, so as to generate sunny and rainy sensing data indicating whether it is raining.

該空調控制裝置12儲存多筆訓練資料、一期望室內溫度資料、多個合成空調設定溫度、一第一可控係數、一第二可控係數、一門檻值、一第一可調參數、一第二可調參數、多個空調可能設定溫度,及一專家建議空調設定溫度資料。The air conditioning control device 12 stores multiple training data, a desired indoor temperature data, multiple synthetic air conditioning setting temperatures, a first controllable coefficient, a second controllable coefficient, a threshold value, a first adjustable parameter, and a The second adjustable parameter, the possible setting temperature of multiple air conditioners, and an expert recommends the setting temperature data of the air conditioner.

每一訓練資料包括一歷史空調設定溫度、多個第一歷史室內溫度、多筆相關於該空調系統11的歷史空調系統狀態感測資料、多筆相關於該室內空間的插座的供電量的歷史插座功耗資料、一歷史晴雨感測資料,及多個分別對應該等第一歷史室內溫度的第二歷史室內溫度,該等第二歷史室內溫度在該等第一歷史室內溫度產生之後經過一預設時間產生。值得注意的是,在本實施例中,該預設時間例如為18分鐘,在其他實施方式中,第一歷史室內溫度、歷史插座功耗資料,及第二歷史室內溫度的數量可分別為1,此外,在其他實施方式中,每一訓練資料可僅包括歷史空調設定溫度、第一歷史室內溫度,及第二歷史室內溫度,但不以此為限。Each training data includes a historical air-conditioning set temperature, a plurality of first historical indoor temperatures, a plurality of historical air-conditioning system state sensing data related to the air-conditioning system 11, and a plurality of historical air-conditioning system state sensing data related to the indoor space. Socket power consumption data, a historical sunny and rainy sensor data, and a plurality of second historical indoor temperatures corresponding to the first historical indoor temperatures, and the second historical indoor temperatures have passed through a period after the first historical indoor temperatures are generated The preset time is generated. It is worth noting that, in this embodiment, the preset time is, for example, 18 minutes. In other embodiments, the number of the first historical indoor temperature, historical socket power consumption data, and the second historical indoor temperature may be 1 respectively. In addition, in other embodiments, each training data may only include the historical air conditioner setting temperature, the first historical indoor temperature, and the second historical indoor temperature, but it is not limited to this.

該期望室內溫度資料包括多個第一時段及多個分別對應該等第一時段的期望室內溫度。The expected indoor temperature data includes a plurality of first time periods and a plurality of expected indoor temperatures respectively corresponding to the first time periods.

該第一可控係數與該第二可控係數大於0且小於等於1,且該第一可控係數小於該第二可控係數。The first controllable coefficient and the second controllable coefficient are greater than 0 and less than or equal to 1, and the first controllable coefficient is less than the second controllable coefficient.

該第一可調參數大於零且小於1,該第二可調參數為任意正數。The first adjustable parameter is greater than zero and less than 1, and the second adjustable parameter is any positive number.

該等空調可能設定溫度例如為攝氏20~27度,但不以此為限。The air conditioner may be set to a temperature of 20 to 27 degrees Celsius, for example, but it is not limited to this.

該專家建議空調設定溫度資料包括多個第二時段及多個分別對應該等第二時段的建議空調設定溫度。The expert recommends that the air conditioner setting temperature data includes a plurality of second time periods and a plurality of recommended air conditioner setting temperatures respectively corresponding to the second time periods.

參閱圖1、2,本發明空調系統控制方法的該實施例包含一建模程序及一控制程序,以下說明該建模程序。Referring to FIGS. 1 and 2, the embodiment of the control method of the air conditioning system of the present invention includes a modeling program and a control program. The modeling program is described below.

在步驟21中,對於每一訓練資料,該空調控制裝置12根據該訓練資料、該等合成空調設定溫度、該第一可控係數,及該第二可控係數,進行數據合成,以獲得多筆分別對應該等合成空調設定溫度的合成訓練資料。In step 21, for each training data, the air-conditioning control device 12 performs data synthesis based on the training data, the synthetic air-conditioning setting temperatures, the first controllable coefficient, and the second controllable coefficient to obtain multiple The pens respectively correspond to the synthetic training data of the synthetic air conditioner setting temperature.

搭配參閱圖3,步驟21包括子步驟211~213,以下說明步驟21包括的子步驟。With reference to FIG. 3, step 21 includes sub-steps 211 to 213, and the sub-steps included in step 21 are described below.

在步驟211中,對於每一訓練資料,且對於每一減去該訓練資料的歷史空調設定溫度大於零的合成空調設定溫度,該空調控制裝置12根據該第一可控係數、該合成空調設定溫度,及該訓練資料的該歷史空調設定溫度與該等第二歷史室內溫度,獲得多個分別對應該等第二歷史室內溫度的合成第二歷史室內溫度。In step 211, for each training data, and for each synthetic air-conditioning setting temperature of which the historical air-conditioning setting temperature minus the training data is greater than zero, the air-conditioning control device 12 is based on the first controllable coefficient and the synthetic air-conditioning setting The temperature, the historical air-conditioning setting temperature and the second historical indoor temperatures of the training data are used to obtain a plurality of synthetic second historical indoor temperatures corresponding to the second historical indoor temperatures.

在步驟212中,對於每一訓練資料,且對於每一減去該訓練資料的歷史空調設定溫度小於零的合成空調設定溫度,該空調控制裝置12根據該第二可控係數、該合成空調設定溫度,及該訓練資料的該歷史空調設定溫度與該等第二歷史室內溫度,獲得多個分別對應該等第二歷史室內溫度的合成第二歷史室內溫度。In step 212, for each training data, and for each synthetic air-conditioning setting temperature of which the historical air-conditioning setting temperature minus the training data is less than zero, the air-conditioning control device 12 according to the second controllable coefficient and the synthetic air-conditioning setting The temperature, the historical air-conditioning setting temperature and the second historical indoor temperatures of the training data are used to obtain a plurality of synthetic second historical indoor temperatures corresponding to the second historical indoor temperatures.

值得注意的是,在本實施例中,步驟211或212的合成第二歷史室內溫度

Figure 02_image001
以下式表示:
Figure 02_image003
, 其中,
Figure 02_image005
為該等第二歷史室內溫度,
Figure 02_image007
為該第一可控係數,
Figure 02_image009
Figure 02_image011
為該合成空調設定溫度,
Figure 02_image013
為該歷史空調設定溫度,
Figure 02_image015
為該第二可控係數,
Figure 02_image017
。 It is worth noting that in this embodiment, the synthesis of the second historical indoor temperature in step 211 or 212
Figure 02_image001
The following formula represents:
Figure 02_image003
, in,
Figure 02_image005
Is the second historical indoor temperature,
Figure 02_image007
Is the first controllable coefficient,
Figure 02_image009
,
Figure 02_image011
Set the temperature for the synthetic air conditioner,
Figure 02_image013
Set the temperature for this historical air conditioner,
Figure 02_image015
Is the second controllable coefficient,
Figure 02_image017
.

在步驟213中,對於每一訓練資料,且對於每一合成空調設定溫度,該空調控制裝置12產生一合成訓練資料,該合成訓練資料包括該合成空調設定溫度、該訓練資料的該等第一歷史室內溫度、該訓練資料的該等空調系統狀態感測資料、該訓練資料的該等歷史插座功耗資料、該訓練資料的該歷史晴雨感測資料,及步驟211或212獲得的合成第二歷史室內溫度。In step 213, for each training data and for each synthetic air conditioner setting temperature, the air conditioner control device 12 generates a synthetic training data. The synthetic training data includes the synthetic air conditioner setting temperature and the first training data. The historical indoor temperature, the air conditioning system state sensing data of the training data, the historical socket power consumption data of the training data, the historical sunny and rainy sensing data of the training data, and the synthesized second data obtained in step 211 or 212 Historical indoor temperature.

要特別注意的是,在本實施例中,該第一可控係數例如為0.6,用以模擬主動冷卻降溫較快的特性,該第二可控係數例如為0.4,用以模擬被動加熱升溫較慢的特性,但不以此為限。It should be particularly noted that, in this embodiment, the first controllable coefficient is, for example, 0.6, to simulate the characteristics of faster cooling of active cooling, and the second controllable coefficient is, for example, 0.4 to simulate the characteristics of passive heating. Slow characteristics, but not limited to this.

在步驟22中,對於每一訓練資料及合成訓練資料,該空調控制裝置12將該訓練資料或合成訓練資料的該等空調系統狀態感測資料、該等歷史插座功耗資料,及該歷史晴雨感測資料進行主成分分析(Principal Components Analysis,PCA),以獲得多筆訓練分析資料。值得注意的是,在本實施例中,該空調控制裝置12將28筆空調系統狀態感測資料、2筆歷史插座功耗資料,及1筆歷史晴雨感測資料進行主成分分析,重新去蕪存菁,消除冗餘部分,以獲得13筆分析資料,但不以此為限。In step 22, for each training data and synthetic training data, the air-conditioning control device 12 uses the training data or the synthetic training data to sense the state of the air-conditioning system, the historical socket power consumption data, and the historical sunny or rainy weather. Principal Components Analysis (PCA) is performed on the sensing data to obtain multiple training analysis data. It is worth noting that, in this embodiment, the air-conditioning control device 12 performs principal component analysis on 28 air-conditioning system state sensing data, 2 historical socket power consumption data, and 1 historical sunny and rainy sensing data, and re-debugging Save the Jing, eliminate redundant parts to obtain 13 analysis data, but not limited to this.

在步驟23中,該空調控制裝置12根據該等訓練資料的歷史空調設定溫度、第一歷史室內溫度、第二歷史室內溫度,及所對應的訓練分析資料,與該等合成訓練資料的合成空調設定溫度、第一歷史室內溫度、合成第二歷史室內溫度,及所對應的訓練分析資料,利用一神經網路(Neural Network, NN)演算法建立一用以預測該預設時間後的室內溫度的室內溫度預測模型。In step 23, the air-conditioning control device 12 uses the historical air-conditioning setting temperature, the first historical indoor temperature, and the second historical indoor temperature of the training data, and the corresponding training analysis data, and the synthetic air conditioning of the synthetic training data Set the temperature, the first historical indoor temperature, synthesize the second historical indoor temperature, and the corresponding training analysis data, and use a Neural Network (NN) algorithm to create a predictive indoor temperature after the preset time Indoor temperature prediction model.

要再注意的是,在本實施例中,該等訓練資料樣本數不足,造成該等訓練資料數據偏差,所訓練出來的模型並不準確,因此需要進行步驟21,以利用數據合成的方式解決數據偏差的問題,但在其他訓練資料樣本數足夠的實施方式中,可不進行步驟21直接進行步驟22、23,此外,在其他實施方式中亦可不進行步驟22的主成分分析,在步驟23中,直接根據該等訓練資料及該等合成訓練資料,或是僅根據該等訓練資料,建立該室內溫度預測模型,不以此為限。It should be noted again that in this embodiment, the number of training data samples is insufficient, which causes the training data to be biased, and the trained model is not accurate. Therefore, step 21 is required to solve the problem by data synthesis. The problem of data deviation, but in other implementations where the number of training data samples is sufficient, steps 22 and 23 can be directly performed without step 21. In addition, in other embodiments, the principal component analysis of step 22 can also be omitted, and in step 23 , It is not limited to establish the indoor temperature prediction model directly based on the training data and the synthetic training data, or only based on the training data.

參閱圖1、4,以下說明該控制程序。Referring to Figures 1 and 4, the control procedure is described below.

在步驟31中,該空調控制裝置12將該等空調系統狀態感測資料、該等插座功耗資料,及該晴雨感測資料進行主成分分析,以獲得多筆分析資料。In step 31, the air-conditioning control device 12 performs principal component analysis on the air-conditioning system state sensing data, the socket power consumption data, and the sunny and rainy sensing data to obtain multiple pieces of analysis data.

在步驟32中,對於每一空調可能設定溫度,該空調控制裝置12根據該空調可能設定溫度、該等室內溫度感測值,及該等分析資料,利用該室內溫度預測模型,獲得一對應該空調可能設定溫度且在一預測時間的預測室內溫度組。該預測時間為一當前時間加上該預設時間,每一預測室內溫度組包括多個分別對應該等室內溫度感測值的預測室內溫度。In step 32, for each possible setting temperature of the air conditioner, the air conditioning control device 12 uses the indoor temperature prediction model to obtain a corresponding response based on the possible setting temperature of the air conditioner, the indoor temperature sensing values, and the analysis data. The air conditioner may set the temperature and predict the indoor temperature group at a predicted time. The predicted time is a current time plus the preset time, and each predicted indoor temperature group includes a plurality of predicted indoor temperatures corresponding to the indoor temperature sensing values.

在其他實施方式中,步驟32可僅根據該空調可能設定溫度及該等室內溫度感測值,獲得該預測室內溫度組,且不需要進行步驟31,直接進行步驟32,但不以此為限。In other embodiments, step 32 can obtain the predicted indoor temperature group only according to the possible set temperature of the air conditioner and the indoor temperature sensing values, and step 31 is not required, and step 32 is performed directly, but it is not limited to this. .

在步驟33中,該空調控制裝置12根據該期望室內溫度資料及該預測時間,從該等期望室內溫度中,獲得一目標期望室內溫度,其中該預測時間位於該目標期望室內溫度對應的第一時段。舉例來說,該等第一時段為1天中的24個小時時段,若該預測時間為09:15,則該目標期望室內溫度為09:00~09:59的第一時段對應的期望室內溫度。In step 33, the air-conditioning control device 12 obtains a target expected indoor temperature from the expected indoor temperatures according to the expected indoor temperature data and the predicted time, where the predicted time is located at the first corresponding to the target expected indoor temperature. Time period. For example, the first time period is a 24-hour time period in a day. If the predicted time is 09:15, then the target expected indoor temperature corresponds to the first time period when the target expected indoor temperature is 09:00~09:59. temperature.

在步驟34中,該空調控制裝置12根據多個相關於該等預測室內溫度組分別與該目標期望室內溫度的誤差值,從該等空調可能設定溫度獲得一目標空調設定溫度。In step 34, the air-conditioning control device 12 obtains a target air-conditioning setting temperature from the possible setting temperatures of the air-conditioning based on a plurality of error values related to the predicted indoor temperature groups and the target expected indoor temperature.

搭配參閱圖5,步驟34包括子步驟341~346,以下說明步驟34包括的子步驟。Referring to FIG. 5 in conjunction, step 34 includes sub-steps 341 to 346. The sub-steps included in step 34 are described below.

在步驟341中,對於每一空調可能設定溫度,該空調控制裝置12根據該空調可能設定溫度對應的預測室內溫度組與該目標期望室內溫度獲得一誤差值,該誤差值

Figure 02_image019
以下式表示:
Figure 02_image021
, 其中,
Figure 02_image023
為該空調可能設定溫度對應的預測室內溫度組,
Figure 02_image025
為該室內溫度預測模型,
Figure 02_image027
為該等室內溫度感測值,
Figure 02_image029
為該等分析資料,
Figure 02_image031
為該等空調系統狀態感測資料、該等插座功耗資料,及該晴雨感測資料,
Figure 02_image033
為該空調可能設定溫度,
Figure 02_image035
為該目標期望室內溫度,
Figure 02_image037
為該預設時間,
Figure 02_image039
為該預測時間,
Figure 02_image041
為範數函數。要特別注意是,在可僅根據該空調可能設定溫度及該等室內溫度感測值,獲得該預測室內溫度組的實施方式中,該空調可能設定溫度對應的預測室內溫度組以
Figure 02_image043
表示。 In step 341, for each possible set temperature of the air conditioner, the air conditioner control device 12 obtains an error value according to the predicted indoor temperature group corresponding to the possible set temperature of the air conditioner and the target expected indoor temperature, the error value
Figure 02_image019
The following formula represents:
Figure 02_image021
, in,
Figure 02_image023
The predicted indoor temperature group corresponding to the possible set temperature of the air conditioner,
Figure 02_image025
Is the indoor temperature prediction model,
Figure 02_image027
Are the indoor temperature sensing values,
Figure 02_image029
For such analysis data,
Figure 02_image031
For the air-conditioning system state sensing data, the socket power consumption data, and the sunny or rainy sensing data,
Figure 02_image033
May set the temperature for this air conditioner,
Figure 02_image035
The desired room temperature for this goal,
Figure 02_image037
For the preset time,
Figure 02_image039
For the forecast time,
Figure 02_image041
Is the norm function. It should be particularly noted that in the embodiment in which the predicted indoor temperature group can be obtained only based on the possible set temperature of the air conditioner and the indoor temperature sensing values, the predicted indoor temperature set corresponding to the possible set temperature of the air conditioner is
Figure 02_image043
Express.

在步驟342中,該空調控制裝置12獲得一對應該等誤差值中之一最小誤差值的第一候選空調可能設定溫度。In step 342, the air-conditioning control device 12 obtains a possible set temperature of the first candidate air-conditioner that should have the smallest error value among the equal error values.

在步驟343中,該空調控制裝置12獲得一對應該等誤差值中之一第二小誤差值的第二候選空調可能設定溫度。In step 343, the air-conditioning control device 12 obtains a pair of second candidate air-conditioning possible set temperatures corresponding to the second smallest error value of one of the equal error values.

在步驟344中,該空調控制裝置12判定是否該第二候選空調可能設定溫度大於該第一候選空調可能設定溫度,且該第二小誤差值小於該門檻值。當判定出該第二候選空調可能設定溫度大於該第一候選空調可能設定溫度,且該第二小誤差值小於該門檻值時,流程進行步驟345;而當判定出該第二候選空調可能設定溫度不大於該第一候選空調可能設定溫度,或該第二小誤差值不小於該門檻值時,流程進行步驟346。In step 344, the air-conditioning control device 12 determines whether the possible set temperature of the second candidate air conditioner is greater than the possible set temperature of the first candidate air conditioner, and the second small error value is less than the threshold value. When it is determined that the possible set temperature of the second candidate air conditioner is greater than the possible set temperature of the first candidate air conditioner, and the second small error value is less than the threshold value, the process proceeds to step 345; and when it is determined that the second candidate air conditioner may be set When the temperature is not greater than the possible set temperature of the first candidate air conditioner, or the second small error value is not less than the threshold value, the process proceeds to step 346.

在步驟345中,該空調控制裝置12決定該第二候選空調可能設定溫度為該目標空調設定溫度。In step 345, the air-conditioning control device 12 determines that the second candidate possible air-conditioner set temperature is the target air-conditioner set temperature.

在步驟346中,該空調控制裝置12決定該第一候選空調可能設定溫度為該目標空調設定溫度。In step 346, the air conditioner control device 12 determines that the first candidate air conditioner possible set temperature is the target air conditioner set temperature.

值得注意的是,在本實施例中,若該第二候選空調可能設定溫度大於該第一候選空調可能設定溫度,且該第二小誤差值尚在可接受範圍(即小於該門檻值),會決定該第二候選空調可能設定溫度為該目標空調設定溫度,以節省能源,但在其他實施例中,可以僅進行步驟341、342後,直接決定該第一候選空調可能設定溫度為該目標空調設定溫度,不以此為限。It is worth noting that, in this embodiment, if the possible set temperature of the second candidate air conditioner is greater than the possible set temperature of the first candidate air conditioner, and the second small error value is still within an acceptable range (that is, less than the threshold value), The second candidate air conditioner may be determined to be the target air conditioner set temperature to save energy. However, in other embodiments, only steps 341 and 342 may be performed, and the first candidate air conditioner may be directly determined to be the target air conditioner. The setting temperature of the air conditioner is not limited to this.

在步驟35中,該空調控制裝置12根據一在一前一時間設定的前一空調設定溫度、多筆在該前一時間獲得的前一分析資料、多個分別由該等室內溫度感測器13在該前一時間產生的前一室內溫度感測值、該等室內溫度感測值、該第一可調參數、該第二可調參數,及一在該前一時間的前一信心度,獲得一當前信心度,該前一時間為該當前時間扣除該預設時間,該前一信心度及該當前信心度大於零且小於1,該當前信心度

Figure 02_image045
以下式表示:
Figure 02_image047
, 其中,
Figure 02_image049
為該第一可調參數,
Figure 02_image051
為該第二可調參數,
Figure 02_image025
為該室內溫度預測模型,
Figure 02_image053
為該等前一室內溫度感測值,
Figure 02_image055
為該等前一分析資料,
Figure 02_image057
為該前一空調設定溫度,
Figure 02_image059
為該至少一室內溫度感測值,
Figure 02_image061
為前一時間,
Figure 02_image041
為範數函數,
Figure 02_image063
為該前一信心度,且
Figure 02_image065
。 In step 35, the air-conditioning control device 12 is based on a previous air-conditioning setting temperature set at a previous time, a plurality of previous analysis data obtained at the previous time, and a plurality of indoor temperature sensors. 13 The previous indoor temperature sensing value generated at the previous time, the indoor temperature sensing values, the first adjustable parameter, the second adjustable parameter, and a previous confidence level at the previous time , Obtain a current confidence level, the previous time is the current time minus the preset time, the previous confidence level and the current confidence level are greater than zero and less than 1, the current confidence level
Figure 02_image045
The following formula represents:
Figure 02_image047
, in,
Figure 02_image049
Is the first adjustable parameter,
Figure 02_image051
Is the second adjustable parameter,
Figure 02_image025
Is the indoor temperature prediction model,
Figure 02_image053
Are the previous indoor temperature sensing values,
Figure 02_image055
For the previous analysis data,
Figure 02_image057
Set the temperature for the previous air conditioner,
Figure 02_image059
Is the at least one indoor temperature sensing value,
Figure 02_image061
For the previous time,
Figure 02_image041
Is the norm function,
Figure 02_image063
Is the previous confidence level, and
Figure 02_image065
.

在步驟36中,該空調控制裝置12根據該專家建議空調設定溫度資料及該前一時間,從該等建議空調設定溫度中,獲得一目標建議空調設定溫度,其中該前一時間位於該目標建議空調設定溫度對應的第二時段。In step 36, the air-conditioning control device 12 obtains a target recommended air-conditioning setting temperature from the recommended air-conditioning setting temperature according to the expert-recommended air-conditioning setting temperature data and the previous time, wherein the previous time is located in the target recommended air-conditioning setting temperature. The second time period corresponding to the set temperature of the air conditioner.

在步驟37中,該空調控制裝置12根據該當前信心度、該目標建議空調設定溫度,及該目標空調設定溫度,獲得一最佳空調設定溫度,該最佳空調設定溫度

Figure 02_image067
以下式表示:
Figure 02_image069
, 其中,
Figure 02_image071
為該目標空調設定溫度,
Figure 02_image073
為該目標建議空調設定溫度。 In step 37, the air-conditioning control device 12 obtains an optimal air-conditioning setting temperature based on the current confidence level, the target recommended air-conditioning setting temperature, and the target air-conditioning setting temperature.
Figure 02_image067
The following formula represents:
Figure 02_image069
, in,
Figure 02_image071
Set the temperature for the target air conditioner,
Figure 02_image073
The air conditioner setting temperature is recommended for this goal.

在步驟37中,該空調控制裝置12根據該最佳空調設定溫度設定該空調系統11。In step 37, the air-conditioning control device 12 sets the air-conditioning system 11 according to the optimal air-conditioning set temperature.

要再特別注意的是,在本實施例中,因為初始階段模擬模型使用了合成資料訓練,準確率上還不完善,因此,進行步驟35,計算該室內溫度預測模型的信心度,並依照該當前信心度、該目標空調設定溫度與,及目標建議空調設定溫度,以調整出該最佳空調設定溫度,且當該室內溫度預測模型準確度不可信時可加大該目標建議空調設定溫度的佔比,至少提供使用者的基本舒適度,在其他實施方式中,亦可不進行步驟35~37,直接以該目標空調設定溫度進行控制,不以此為限。It should be particularly noted that in this embodiment, because the initial stage simulation model uses synthetic data training, the accuracy is not perfect. Therefore, proceed to step 35 to calculate the confidence level of the indoor temperature prediction model, and follow the Current confidence, the target air-conditioning setting temperature and the target recommended air-conditioning setting temperature to adjust the optimal air-conditioning setting temperature, and when the accuracy of the indoor temperature prediction model is not credible, the target recommended air-conditioning setting temperature can be increased The proportion at least provides the user's basic comfort level. In other embodiments, steps 35 to 37 may not be performed, and the target air conditioner setting temperature may be directly used for control, which is not limited to this.

綜上所述,本發明空調系統控制方法,藉由該空調控制裝置12將該等空調系統狀態感測資料、該等插座功耗資料,及該晴雨感測資料進行主成分分析,以獲得該等分析資料,再根據該等空調可能設定溫度、該等分析資料,及該等室內溫度感測值,利用該室內溫度預測模型,獲得該等預測室內溫度組,再根據該等預測室內溫度組與該目標期望室內溫度的誤差值,從該等空調可能設定溫度獲得該目標空調設定溫度,並在計算出該當前信心度後,根據該當前信心度、該目標空調設定溫度,及該目標建議空調設定溫度,獲得該最佳空調設定溫度,最後根據該最佳空調設定溫度設定該空調系統11,以達到每一時段的目標期望室內溫度,並即時反應空間內活動人員的感受,故確實能達成本發明的目的。In summary, the air-conditioning system control method of the present invention uses the air-conditioning control device 12 to perform principal component analysis on the air-conditioning system state sensing data, the socket power consumption data, and the sunny and rainy sensing data to obtain the According to the possible set temperature of the air conditioners, the analysis data, and the indoor temperature sensing values, the indoor temperature prediction model is used to obtain the predicted indoor temperature groups, and then based on the predicted indoor temperature groups. The error value of the target expected indoor temperature is obtained from the possible setting temperatures of the air conditioners, and the current confidence level is calculated based on the current confidence level, the target air conditioner setting temperature, and the target recommendation The air-conditioning set temperature is obtained, and the air-conditioning system 11 is finally set according to the optimal air-conditioning set temperature to achieve the target desired indoor temperature in each time period, and it can reflect the feelings of the active personnel in the space in real time. To achieve the purpose of the invention.

惟以上所述者,僅為本發明的實施例而已,當不能以此限定本發明實施的範圍,凡是依本發明申請專利範圍及專利說明書內容所作的簡單的等效變化與修飾,皆仍屬本發明專利涵蓋的範圍內。However, the above are only examples of the present invention. When the scope of implementation of the present invention cannot be limited by this, all simple equivalent changes and modifications made in accordance with the scope of the patent application of the present invention and the content of the patent specification still belong to Within the scope covered by the patent of the present invention.

11:空調系統11: Air conditioning system

111:空氣調節箱111: Air conditioning box

112:變風量系統112: Variable air volume system

113:小型冷風機113: small air cooler

114:外氣空調箱114: outside air conditioning box

12:空調控制裝置12: Air conditioning control device

13:室內溫度感測器13: Indoor temperature sensor

14:空調感測器14: Air conditioner sensor

15:插座電功耗感測器15: Socket power consumption sensor

16:晴雨感測器16: Sunny rain sensor

21~23:步驟21~23: Steps

211~213:步驟211~213: Steps

31~37:步驟31~37: Steps

341~346:步驟341~346: Steps

本發明的其他的特徵及功效,將於參照圖式的實施方式中清楚地呈現,其中: 圖1是一方塊圖,說明用來實施本發明空調系統控制方法的一實施例的一空調控制裝置的連接關係; 圖2是一流程圖,說明該實施例的一建模程序; 圖3是一流程圖,輔助說明圖2的一步驟22之子步驟; 圖4是一流程圖,說明該實施例的一控制程序;及 圖5是一流程圖,輔助說明圖4的一步驟34之子步驟。 Other features and effects of the present invention will be clearly presented in the embodiments with reference to the drawings, in which: 1 is a block diagram illustrating the connection relationship of an air conditioning control device used to implement an embodiment of the air conditioning system control method of the present invention; Figure 2 is a flowchart illustrating a modeling procedure of this embodiment; FIG. 3 is a flowchart to assist in explaining the sub-steps of step 22 in FIG. 2; Figure 4 is a flowchart illustrating a control procedure of this embodiment; and FIG. 5 is a flowchart to assist in explaining the sub-steps of a step 34 in FIG. 4.

31~37:步驟 31~37: Steps

Claims (8)

一種空調系統控制方法,用以控制該空調系統的空調設定溫度,由一空調控制裝置來實施,該空調控制裝置電連接該空調系統、至少一設置於一室內空間的室內溫度感測器、多個設置於該空調系統的空調感測器、至少一插座電功耗感測器,及一晴雨感測器,該至少一室內溫度感測器用以感測室內溫度,以產生至少一對應該至少一室內溫度感測器的室內溫度感測值,該空調控制裝置儲存多個空調可能設定溫度及一期望室內溫度資料,該期望室內溫度資料包括多個第一時段及多個分別對應該等第一時段的期望室內溫度,該等空調感測器用以感測該空調系統的狀態,以產生多筆空調系統狀態感測資料,該至少一插座電功耗感測器用以感測該室內空間的插座的供電量,以產生至少一分別對應該至少一插座電功耗感測器的插座功耗資料,該晴雨感測器用以感測是否下雨,以產生一指示是否下雨的晴雨感測資料,該方法包含以下步驟:(A)根據該等空調可能設定溫度、該至少一室內溫度感測值,及該等空調系統狀態感測資料、該至少一插座功耗資料,及該晴雨感測資料,利用一用以預測一預設時間後的室內溫度的室內溫度預測模型,獲得多個分別對應該等空調可能設定溫度且在一預測時間的預測室內溫度組,其中該預測時間為一當前時間加上該預設時間,每一預測室內溫度組包括至少一分別對應該至少一室內溫度感測值的預測室內溫度,步驟(A)包括以下子步驟: (A-1)將該等空調系統狀態感測資料、該至少一插座功耗資料,及該晴雨感測資料進行主成分分析,以獲得多筆分析資料,及(A-2)對於每一空調可能設定溫度,根據該空調可能設定溫度、該至少一室內溫度感測值,及該等分析資料,利用該室內溫度預測模型,獲得一對應該空調可能設定溫度且在該預測時間的預測室內溫度組;(B)根據該期望室內溫度資料及該預測時間,從該等期望室內溫度中,獲得一目標期望室內溫度,其中該預測時間位於該目標期望室內溫度對應的第一時段;及(C)根據多個相關於該等預測室內溫度組分別與該目標期望室內溫度的誤差值,從該等空調可能設定溫度獲得一目標空調設定溫度。 An air conditioning system control method for controlling the air conditioning set temperature of the air conditioning system is implemented by an air conditioning control device, the air conditioning control device is electrically connected to the air conditioning system, at least one indoor temperature sensor arranged in an indoor space, and more An air-conditioning sensor, at least one socket power consumption sensor, and a rain or rain sensor are provided in the air-conditioning system. The at least one indoor temperature sensor is used to sense the indoor temperature to generate at least one pair of at least The indoor temperature sensing value of an indoor temperature sensor, the air-conditioning control device stores a plurality of possible setting temperatures of the air conditioner and a desired indoor temperature data, and the desired indoor temperature data includes a plurality of first time periods and a plurality of corresponding first A period of expected indoor temperature, the air-conditioning sensors are used to sense the state of the air-conditioning system to generate multiple pieces of air-conditioning system state sensing data, and the at least one socket power consumption sensor is used to sense the indoor space The power supply of the socket to generate at least one socket power consumption data corresponding to the at least one socket power consumption sensor respectively, and the sunny/rain sensor is used to sense whether it is raining to generate a sunny/rainy sensor indicating whether it is raining Data, the method includes the following steps: (A) according to the possible set temperature of the air conditioners, the at least one indoor temperature sensing value, and the air conditioning system state sensing data, the at least one socket power consumption data, and the sunny/rainy sensor Obtain a plurality of predicted indoor temperature groups corresponding to the possible set temperatures of the air conditioners and a predicted time by using an indoor temperature prediction model for predicting the indoor temperature after a preset time, wherein the predicted time is one The current time plus the preset time, each predicted indoor temperature group includes at least one predicted indoor temperature corresponding to at least one indoor temperature sensing value, step (A) includes the following sub-steps: (A-1) Perform principal component analysis on the air conditioning system state sensing data, the at least one socket power consumption data, and the sunny or rainy sensing data to obtain multiple pieces of analysis data, and (A-2) for each The possible setting temperature of the air conditioner, according to the possible setting temperature of the air conditioner, the at least one indoor temperature sensing value, and the analysis data, the indoor temperature prediction model is used to obtain a prediction room corresponding to the possible setting temperature of the air conditioner and at the predicted time Temperature group; (B) according to the expected indoor temperature data and the predicted time, obtain a target expected indoor temperature from the expected indoor temperatures, wherein the predicted time is in the first time period corresponding to the target expected indoor temperature; and ( C) According to a plurality of error values related to the predicted indoor temperature groups and the target expected indoor temperature, a target air-conditioning set temperature is obtained from the possible set temperatures of the air-conditioning. 如請求項1所述的空調系統控制方法,該空調控制裝置還儲存多筆訓練資料,每一訓練資料包括一歷史空調設定溫度、至少一第一歷史室內溫度,及至少一分別對應該至少一第一歷史室內溫度的第二歷史室內溫度,該至少一第二歷史室內溫度在該至少一第一歷史室內溫度產生之後經過該預設時間產生,在步驟(A)之前還包含以下步驟:(D)根據該等訓練資料,利用一神經網路演算法建立該室內溫度預測模型。 According to the air-conditioning system control method of claim 1, the air-conditioning control device further stores a plurality of training data, each training data includes a historical air-conditioning set temperature, at least one first historical indoor temperature, and at least one corresponding to at least one The second historical indoor temperature of the first historical indoor temperature, the at least one second historical indoor temperature is generated after the at least one first historical indoor temperature is generated after the preset time, before step (A), the following steps are further included: ( D) Based on the training data, a neural network algorithm is used to establish the indoor temperature prediction model. 如請求項2所述的空調系統控制方法,該空調控制裝置還儲存多個合成空調設定溫度、一第一可控係數,及一第二可控係數,該第一可控係數與該第二可控係數大於0且小 於等於1,且該第一可控係數小於該第二可控係數,其中,步驟(D)包括以下子步驟:(D-1)對於每一訓練資料,根據該訓練資料、該等合成空調設定溫度、該第一可控係數,及該第二可控係數,進行數據合成,以獲得多筆分別對應該等合成空調設定溫度的合成訓練資料;及(D-2)根據該等訓練資料及該等合成訓練資料,利用該神經網路演算法建立該室內溫度預測模型。 According to the air-conditioning system control method of claim 2, the air-conditioning control device further stores a plurality of synthetic air-conditioning set temperatures, a first controllable coefficient, and a second controllable coefficient, the first controllable coefficient and the second controllable coefficient Controllable coefficient is greater than 0 and small Is equal to 1, and the first controllable coefficient is less than the second controllable coefficient, wherein step (D) includes the following sub-steps: (D-1) for each training data, according to the training data, the synthetic air conditioners Set the temperature, the first controllable coefficient, and the second controllable coefficient, and perform data synthesis to obtain a plurality of synthetic training data corresponding to the synthetic air conditioner setting temperature; and (D-2) according to the training data And the synthetic training data, using the neural network algorithm to establish the indoor temperature prediction model. 如請求項3所述的空調系統控制方法,其中,步驟(D-1)包括以下子步驟:(D-1-1)對於每一訓練資料,且對於每一減去該訓練資料的歷史空調設定溫度大於零的合成空調設定溫度,根據該第一可控係數、該合成空調設定溫度,及該訓練資料的該歷史空調設定溫度與該至少一第二歷史室內溫度,獲得至少一分別對應該至少一第二歷史室內溫度的合成第二歷史室內溫度;(D-1-2)對於每一訓練資料,且對於每一減去該訓練資料的歷史空調設定溫度小於零的合成空調設定溫度,根據該第二可控係數、該合成空調設定溫度,及該訓練資料的該歷史空調設定溫度與該至少一第二歷史室內溫度,獲得至少一分別對應該至少一第二歷史室內溫度的合成第二歷史室內溫度;及(D-1-3)對於每一訓練資料,且對於每一合成空調設定溫度,產生一包括該合成空調設定溫度、該訓練資料的 該至少一第一歷史室內溫度,及步驟(D-1-1)或(D-1-2)獲得的合成第二歷史室內溫度的合成訓練資料。 The air conditioning system control method according to claim 3, wherein step (D-1) includes the following sub-steps: (D-1-1) for each training data, and for each historical air conditioning minus the training data Set a synthetic air conditioner set temperature with a set temperature greater than zero, and obtain at least one corresponding respectively according to the first controllable coefficient, the synthetic air conditioner set temperature, and the historical air conditioner set temperature and the at least one second historical indoor temperature of the training data A synthetic second historical indoor temperature of at least one second historical indoor temperature; (D-1-2) For each training data, and for each synthetic air-conditioning setting temperature of which the historical air-conditioning setting temperature minus the training data is less than zero, According to the second controllable coefficient, the synthetic air conditioner setting temperature, and the historical air conditioner setting temperature of the training data and the at least one second historical indoor temperature, at least one synthetic first corresponding to at least one second historical indoor temperature is obtained. 2. Historical indoor temperature; and (D-1-3) For each training data and for each synthetic air-conditioning set temperature, generate a set temperature that includes the synthetic air-conditioning set temperature and the training data The at least one first historical indoor temperature, and the synthetic training data obtained in step (D-1-1) or (D-1-2) to synthesize the second historical indoor temperature. 如請求項1所述的空調系統控制方法,該空調控制裝置還儲存一門檻值,其中,步驟(C)包括以下子步驟:(C-1)對於每一空調可能設定溫度,根據該空調可能設定溫度對應的預測室內溫度組與該目標期望室內溫度獲得一誤差值;(C-2)獲得一對應該等誤差值中之一最小誤差值的第一候選空調可能設定溫度;(C-3)獲得一對應該等誤差值中之一第二小誤差值的第二候選空調可能設定溫度;(C-4)判定是否該第二候選空調可能設定溫度大於該第一候選空調可能設定溫度,且該第二小誤差值小於該門檻值;(C-5)當判定出該第二候選空調可能設定溫度大於該第一候選空調可能設定溫度,且該第二小誤差值小於該門檻值時,決定該第二候選空調可能設定溫度為該目標空調設定溫度;及(C-6)當判定出該第二候選空調可能設定溫度不大於該第一候選空調可能設定溫度,或該第二小誤差值不小於該門檻值時,決定該第一候選空調可能設定溫度為該目標空調設定溫度。 According to the air-conditioning system control method of claim 1, the air-conditioning control device also stores a threshold value, wherein step (C) includes the following sub-steps: (C-1) The temperature may be set for each air-conditioner, according to the air-conditioning possibility Obtain an error value between the predicted indoor temperature group corresponding to the set temperature and the target expected indoor temperature; (C-2) Obtain the possible set temperature of the first candidate air conditioner that should have the smallest error value among the equal error values; (C-3) ) Obtain a pair of possible second candidate air conditioner set temperatures that should be equal to one of the second smallest error values; (C-4) determine whether the second candidate air conditioner may set temperature is greater than the first candidate air conditioner's possible set temperature, And the second small error value is less than the threshold value; (C-5) When it is determined that the possible setting temperature of the second candidate air conditioner is greater than the possible setting temperature of the first candidate air conditioner, and the second small error value is less than the threshold value , Determine the possible setting temperature of the second candidate air conditioner as the target air conditioner setting temperature; and (C-6) When it is determined that the possible setting temperature of the second candidate air conditioner is not greater than the possible setting temperature of the first candidate air conditioner, or the second lower When the error value is not less than the threshold value, it is determined that the possible set temperature of the first candidate air conditioner is the target air conditioner set temperature. 如請求項5所述的空調系統控制方法,其中,在步驟(C-1)中,該誤差值
Figure 109125241-A0305-02-0021-1
以下式表示:
Figure 109125241-A0305-02-0022-2
其中,F(s t ,a t )為該空調可能設定溫度對應的預測室內溫度組,F(.)為該室內溫度預測模型,s t 為該至少一室內溫度感測值,a t 為該空調可能設定溫度,
Figure 109125241-A0305-02-0022-3
為該目標期望室內溫度,t+△T為該預測時間,∥.∥為範數函數。
The air conditioning system control method according to claim 5, wherein, in step (C-1), the error value
Figure 109125241-A0305-02-0021-1
The following formula represents:
Figure 109125241-A0305-02-0022-2
Wherein, F (s t, a t ) may be set as the air temperature corresponding to the indoor temperature prediction group, F (.) For the indoor temperature prediction model, s t room temperature for at least a sensing value, a t for The air conditioner may set the temperature,
Figure 109125241-A0305-02-0022-3
Is the desired indoor temperature for the target, t + △ T is the predicted time, and ∥.∥ is the norm function.
如請求項1所述的空調系統控制方法,該空調控制裝置還儲存一第一可調參數、一第二可調參數,及一專家建議空調設定溫度資料,該第一可調參數大於零且小於1,該第二可調參數為任意正數,該專家建議空調設定溫度資料包括多個第二時段及多個分別對應該等第二時段的建議空調設定溫度,在步驟(C)之後還包含以下步驟:(E)根據一在一前一時間設定的前一空調設定溫度、至少一分別由該至少一室內溫度感測器在該前一時間產生的前一室內溫度感測值、該至少一室內溫度感測值、該第一可調參數、該第二可調參數,及一在該前一時間的前一信心度,獲得一當前信心度,其中該前一時間為該當前時間扣除該預設時間,該前一信心度及該當前信心度大於零且小於1;(F)根據該專家建議空調設定溫度資料及該前一時間,從該等建議空調設定溫度中,獲得一目標建議空調設定溫度,其中該前一時間位於該目標建議空調設定溫度對應的第二時段;及(G)根據該當前信心度、該目標建議空調設定溫度,及該目標空調設定溫度,獲得一最佳空調設定溫度。 According to the air-conditioning system control method of claim 1, the air-conditioning control device further stores a first adjustable parameter, a second adjustable parameter, and an expert recommended air-conditioning setting temperature data, the first adjustable parameter is greater than zero and Less than 1, the second adjustable parameter is any positive number. The expert recommends that the air-conditioning setting temperature data includes a plurality of second time periods and a plurality of recommended air-conditioning setting temperatures corresponding to the second time periods. After step (C), it also includes The following steps: (E) According to a previous air conditioning set temperature set at a previous time, at least one previous indoor temperature sensing value generated by the at least one indoor temperature sensor at the previous time, the at least An indoor temperature sensing value, the first adjustable parameter, the second adjustable parameter, and a previous confidence level at the previous time to obtain a current confidence level, where the previous time is the current time minus The preset time, the previous confidence level and the current confidence level are greater than zero and less than 1; (F) According to the expert’s recommended air conditioning setting temperature data and the previous time, obtain a target from the recommended air conditioning setting temperatures Recommended air-conditioning setting temperature, where the previous time is in the second time period corresponding to the target recommended air-conditioning setting temperature; and (G) According to the current confidence, the target recommended air-conditioning setting temperature, and the target air-conditioning setting temperature, obtain a maximum Optimal air conditioning setting temperature. 如請求項7所述的空調系統控制方法,其中,在步驟(E)中,該當前信心度α t 以下式表示:
Figure 109125241-A0305-02-0023-4
其中,β為該第一可調參數,σ為該第二可調參數,F(.)為該室內溫度預測模型,s t-△T 為該至少一前一室內溫度感測值,a t-△T 為該前一空調設定溫度,s t 為該至少一室內溫度感測值,t-△T為前一時間,∥.∥為範數函數,α t-△T 為該前一信心度,且α 0=0.5。在步驟(G)中,該最佳空調設定溫度
Figure 109125241-A0305-02-0023-8
以下式表示:
Figure 109125241-A0305-02-0023-5
其中,
Figure 109125241-A0305-02-0023-6
為該目標空調設定溫度,
Figure 109125241-A0305-02-0023-7
為該目標建議空調設定溫度。
The air conditioning system control method according to claim 7, wherein, in step (E), the current confidence level α t is expressed by the following formula:
Figure 109125241-A0305-02-0023-4
Where β is the first adjustable parameter, σ is the second adjustable parameter, F (.) is the indoor temperature prediction model, s tT is the at least one previous indoor temperature sensing value, a t -△ T is the previous air conditioner setting temperature, s t is the at least one indoor temperature sensing value, t -△ T is the previous time, ∥.∥ is the norm function, α t -△ T is the previous confidence Degree, and α 0 =0.5. In step (G), the optimal air conditioner set temperature
Figure 109125241-A0305-02-0023-8
The following formula represents:
Figure 109125241-A0305-02-0023-5
in,
Figure 109125241-A0305-02-0023-6
Set the temperature for the target air conditioner,
Figure 109125241-A0305-02-0023-7
The air conditioner setting temperature is recommended for this goal.
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