JPH0926147A - Method for controlling power in heat accumulative type heating system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To eliminate a useless consumption of electrical power and to enable an economical floor heating system to be operated by a method wherein a variation of an electrical energization time which is different under various conditions is calculated by a numerical value analyzing method so as to determine an electrical energization starting time. SOLUTION: Measured values of T and θi are inputted into an estimating equation T=Ζaiθi+Bi... (1) indicating a relation between an electrical energization time T and a temperature element θi before starting an electrical energization (where, (i) is a serial No. of a temperature measuring position element, a temperature element 9i is a temperature before starting an electrical energization of the temperature measuring position element (i), (ai) is a coefficient of θi, (bi) is a constant item, θi is selected from a group including a heat accumulative material temperature θ1 , a floor temperature θ2 , an indoor temperature θ3 and a surrounding air temperature θ4 . The floor temperature θ2 is an essential temperature element θi) and then the coefficient ai and the constant item (bi) in the equation are calculated by multivariate analysis so as to obtain a multiple recurrence formula. Then, an actual measured value of θi is inputted to the obtained multiple recurrence formula so as to obtain an electrical energization time T and then an electrical energization is started by the time T before an electrical energization completion line (a floor heating start time under a heat radiation of a latent heat accumulation material.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a heat storage type heating system control method.

[0002]

2. Description of the Related Art A latent heat storage type floor heating system stores electric power in an underfloor latent heat storage material by electric heat before the start of use of a room, especially in the middle of the night, and heats the floor when using the stored heat. In this type of floor heating, electricity is stored and heat is stored during a cheap midnight power supply time.When the heat storage material reaches a predetermined temperature due to electricity supply, the temperature sensor etc. shuts off the electricity and when the temperature drops to a predetermined temperature. Generally, the heat storage power is turned on and off from the start of energization to the end of energization such that energization is restarted.
In some cases, depending on the season and climatic conditions, if it is expected that the energization time (hereinafter sometimes referred to as heat storage time) will be significantly shorter than the midnight power supply time,
An ON-OFF operation for a predetermined time is also performed by a timer or the like so that the floor heating performance can be ensured by empirically predicting the heat storage time.

It is also common practice to control the heat storage energy in the room by controlling the floor heating area by increasing or decreasing the heating heater circuit instead of controlling the heat storage time. However, in order to further improve the control performance, some past ambient temperature data and floor heating temperature rising characteristics are stored in a memory as described in JP-A-63-62012. A method has been proposed in which the temperature of outside air when heating is being measured, the temperature rise characteristic is determined by a mode approximated from the stored data in the past, and the power-on time is determined.

The heating period in winter is usually five months from November to March, but the operating method of the floor heating system should be different between the severe cold season of January to February and the mild warming season before and after that. When designing and constructing a floor heating system, consider the structure of the building, the outside temperature in winter, etc., calculate the heat load amount to ensure the heating effect even in the severe cold season, and design the heating power source and heat storage material. There is.

When the heating power source is turned on during the midnight power supply time, it is sufficient for the heating heat load in the severe cold season, but the heat storage becomes excessive during the mild warm season, and the midnight power is not supplied. Before the end, the heat storage material reaches a predetermined temperature, and thereafter the power is turned on and off by the temperature sensor, and the power is turned on wastefully.

[0006] Further, a method of predicting the heat storage time depending on the season, climatic conditions and heating conditions and performing ON-OFF operation with a timer is extremely sensuous, and it is difficult to maintain actual heating conditions comfortably. is there. With the method of adjusting the floor heating area by increasing / decreasing the heating heater circuit, it is difficult to obtain comfortable heating because some rooms are heated and some are not.

Therefore, a method described in, for example, Japanese Patent Application Laid-Open No. 63-62012 has been proposed.
Empirically, such a method of storing the typical modes of the outside air temperature and the heating characteristic of the floor heating in the memory and determining the power-on time requires a considerably complicated measuring device and a long time. It seems to be.

[0008]

In the present invention, a change in the required energization time (heat storage time) that varies depending on the season and climatic conditions is obtained by a numerical analysis method, and the energization start time according to the season and climatic conditions is determined. Therefore, it is intended to propose a control method that enables economical operation of a floor heating system while saving unnecessary power consumption.

[0009]

The present invention comprises the following inventions. [1] Prediction formula (1) showing the relationship between the energization time T and the temperature element θi before the start of energization

(3) T = Σai θi + bi ─────────── (1) (where i is the serial number of the temperature measurement position element, temperature element θi is the temperature before the energization of temperature measurement position element i, ai is the coefficient of θi, bi is a constant term, θi is selected from the group including heat storage material temperature θ ₁ , floor temperature θ ₂ , indoor temperature θ ₃ and outside air temperature θ ₄ , and floor temperature θ ₂ is an essential temperature element θi. The measured values of T and .theta.i are input to the equation (1) and the coefficient ai and the constant term bi of the equation (1) are obtained by multivariate analysis to obtain a multiple regression equation, which is then obtained. The measured value of θi is input to the multiple regression equation, the energization time T is obtained, and the energization is controlled to start T hours before the energization end time (the time when floor heating is started by the heat dissipation of the latent heat storage material). A method for controlling the heat input of a heat storage type heating system, characterized in that

[2] A simple prediction formula (2) obtained as a relationship between the floor surface temperature θ ₂ of the heat storage type heating system before the start of energization and the energization time T

(4) T = a ₂ θ ₂ + b _2- (2) (where, θ ₂ is the bed temperature before the start of energization, a ₂ is the coefficient of θ ₂ , and b ₂ is a constant term). , T and θ ₂ are input, and the coefficient a of the equation (1) is calculated by multivariate analysis.
_A multiple regression equation is obtained by obtaining ₂ and the constant term b ₂ , and then the obtained multiple regression equation is input with the measured value of θ ₂ .
The heat quantity of the heat storage type heating system is characterized in that the energization time T is obtained and the energization is controlled to start energization T hours before the energization end time (the time when floor heating is started by heat dissipation of the latent heat storage material). Control method of input.

[3] The method for controlling heat input of the heat storage type heating system according to the above item [1] or [2], wherein the heat storage type heating system is a heat storage type floor heating system.

A prediction equation showing the relationship between the energization time T and the temperature element θi of the temperature measuring element i such as the heat storage material temperature, the floor temperature, the room temperature and the outside air temperature before the start of the energization is expressed as follows. .

[Equation 5] T = Σai θi + bi ─── (1) θi: Temperature before energization of temperature measurement position element i ai: Coefficient of θi bi: Constant term

The measured values of T and θi are input, and the coefficient a i and the constant term b of the prediction formula (1) for T are calculated by multivariate analysis.
The multiple regression equation can be obtained by obtaining i. Here, if the prediction accuracy of the multiple regression equation with respect to the temperature element θi is sufficient, the prediction equation (1) becomes a unique equation for predicting the energization time T in the building in which the floor heating facility is installed, and it depends on the season, climatic conditions, etc. Regardless, if the temperature element θi before the start of energization at that point in time is input to the obtained multiple regression equation, the required energization time T at that point in time can be predicted.

Therefore, the four temperature measurement position elements i
Before the start of energization in θ, that is, the heat storage material temperature θ ₁ , the floor temperature θ ₂ , the indoor temperature θ ₃ and the outside air temperature θ ₄
For each of the above, the multiple regression equations obtained individually or in combination and the correlation coefficient indicating the prediction accuracy were obtained.By using the bed temperature θ ₂ as the temperature element θi, it is possible to obtain a multiple regression expression with sufficiently high prediction accuracy. They have found the present invention and completed the present invention. Hereinafter, the present invention will be described specifically with reference to examples.

[0015]

【Example】

Example 1 Regarding a latent heat storage type electric floor heating constructed in a reinforced concrete three-story school building of a certain junior high school, a heat storage material temperature θ ₁ , a floor temperature θ ₂ , an indoor temperature θ ₃ and an outside air temperature θ ₄ are set as a thermocouple temperature. The measurement was performed continuously for 24 hours. From the measured data, 45 for the temperature element θi and the energization time T before the energization starts
A specimen was prepared. Those data are shown in Tables 1 and 2.

[0016]

[Table 1] ─────────────────────────────────── Date θ ₁ θ ₂ θ ₃ θ ₄ T (℃) (℃) (℃) (℃) (min) ──────────────────────────────────── 12 1 14.97 13.29 3.56 13.12 280 12 2 22.19 17.16 6.67 15.87 200 12 3 22.21 16.48 2.59 15.22 240 12 4 22.94 16.45 -1.02 15.43 230 12 5 22.47 16.08 1.61 15.24 240 12 7 16.76 13.93 2.32 14.01 250 12 8 24.12 18.73 12.16 17.5 170 1 27 22.54 16.52 0.31 15.3 210 1 28 21.93 16.22 2.39 15.37 230 1 29 22.14 15.23 -3.0 14.18 220 2 3 13.77 10.48 -3.37 10.67 360 2 4 20.77 14.63 -1.82 13.7 240 2 5 22.3 15.8 0.4 14.86 220 2 6 23.8 16.58- 0.76 15.85 220 2 8 17.85 14.78 3.48 14.68 250 2 9 23.32 16.34 -5.06 14.99 220 2 10 21.51 15.39 -1.54 14.61 230 2 12 17.19 13.76 -0.17 13.67 260 2 15 14.73 12.04 -2.44 11.78 390 2 16 22.57 16.45 -0.41 15.48 220 2 17 24.6 18.53 6.72 17.58 170 2 18 24.07 18.75 4.17 17.63 1 70 ───────────────────────────────────

[0017]

[Table 2] ─────────────────────────────────── Month / Day θ ₁ θ ₂ θ ₃ θ ₄ T ─────────────────────────────────── 2 19 24.33 18.66 2.21 17.29 170 2 20 24.19 18.8 -1.81 17.39 170 2 22 17.02 15.16 3.27 14.61 240 2 23 24.66 20.16 3.22 18.04 140 2 24 22.86 18.47 0.15 16.67 180 2 25 23.95 19.33 -1.17 17.43 160 2 26 24.25 19.51 -1.79 17.64 160 2 27 24.06 19.51 -0.36 17.79 160 3 1 18.94 16.95 4.41 16.38 220 3 2 23.66 19.41 0.41 17.4 150 3 3 23.25 18.28 -2.3 16.33 170 3 4 23.82 18.94 0.06 17.15 160 3 5 24.16 19.18 0.4 17.42 150 3 6 24.9 19.92 1.39 18.15 140 3 8 18.5 16.34 3.69 15.34 220 3 9 22.65 18.04 1.85 16.25 180 3 10 23.16 18.39 1.07 16.7 170 3 11 23.44 18.84 1.42 17.02 160 3 12 23.41 18.42 1.04 16.52 170 3 15 15.34 13.61 -0.94 12.8 290 3 16 21.22 16.54 2.93 14.47 210 3 17 19.94 15.27 -1.62 13.5 220 3 18 21.08 16.06 -1.48 13.91 210 ──── ──────────────────────────────

Using these data, each temperature element θi
For each of the heat storage material temperature θ ₁ , the floor temperature θ ₂ , the indoor temperature θ ₃ and the outside air temperature θ ₄ , the multiple regression equation of the prediction equation (2) and the correlation coefficient r with the actual measurement value T are obtained individually or in combination. It was Table 3 shows the results.

[0019]

[Table 3] ─────────────────────────────────── No. Multiple regression equation Correlation coefficient: r ─ ────────────────────────────────── 1 T = −22.622 θ ₂ ＋590.09 0.9560 2 T = −15.043 _{θ 1 +534.11 0.8719 3 T = -27.653θ} 3 +641.30 0.9241 4 T = -1.5891θ 1 -20.678θ 2 +591.68 0.9568 5 T = -26.346θ 2 + 4.8299θ 3 +577.33 0.9567 6 T = −22.615 θ ₂ −0.019934 θ ₄ +590.00 0.9560 7 T = −5.1356 θ ₁ −20.006 θ ₃ +632.72 0.9366 8 T = −1.4549 θ ₁ −24.127 θ ₂ +4.2605 θ ₃ +580.28 0.9574 ──── ───────────────────────────────

As can be seen from Table 3, it was confirmed that the correlation coefficient r was 0.9560 and a high prediction accuracy was obtained even when the bed temperature was used alone. Even in the combination of the floor temperature and a temperature element other than the floor temperature, almost no improvement in the prediction accuracy is recognized as compared with the case of the floor temperature alone, and therefore the floor temperature θ ₂ before energization is the only temperature element. Regarding the prediction formula (1), it was found that the energization time T can be predicted with high accuracy by the following multiple regression formula (3) obtained by performing multivariate analysis.

[0021]

[Equation 6] T = −22.622 θ ₂ +590.09 ─── (3)

Example 2 In Example 1, it was found that there is a high correlation between the bed temperature θ ₂ before energization and the energization time T. Then, it was found that the minimum energization time required for heat storage can be predicted by the linear regression equation of θ ₂ and T. Therefore, the energization time is predicted by using this linear regression equation and the energization time to the heater is controlled (hereinafter, this may be referred to as predictive control).
In order to verify the power consumption reduction effect and the heating effect in the case of using, the latent heat storage heat storage type electric floor heating system of the reinforced concrete construction laboratory auditorium of a certain chemical company was used during the period from March 22 to 27, 1995. The experimental operation was performed by the above predictive control method. As a comparative experiment, 17 from March 13 of the same year
Uncontrolled operation was also performed during the day. The results of comparing the power consumption and the heating effect (transition of the indoor temperature) are shown in Table 4 and Table 5.
It was shown to.

[0023]

[Table 4] Results of predictive control operation ─────────────────────────────────── Month day Floor temperature Power consumption Room temperature on the next day [℃] [℃] [kwh] 8:00 11:00 14:00 17:00 ────────────────────────── ────────── 3 22 24.9 69.3 − − 23.3 22.2 23 22.4 96.2 23.3 22.8 22.4 21.5 24 22.0 101.4 23.1 22.1 21.4 20.4 25 20.8 115.1 21.7 21.0 20.2 19.2 26 19.8 126.3 21.2 20.9 20.3 20.0 27 20.2 121.1 21.3 20.6 20.1 20.0 Average 21.7 104.9 22.1 21.5 21.3 20.6 ────────────────────────────────────

[0024]

[Table 5] Non-operating results ─────────────────────────────────── Month floor temperature Power consumption Room temperature on the next day [℃] [℃] [kwh] 8:00 11:00 14:00 17:00 ────────────────────────── ────────── 3 13 16.5 250.2 21.8 21.1 21.3 20.9 14 21.3 236.1 23.1 22.2 21.6 21.6 15 22.2 228.0 23.6 22.8 − 23.1 16 23.5 183.4 23.9 23.2 22.6 22.1 17 23.3 178.5 23.9 22.8 22.1 21.2 Average 21.4 215.2 23.3 22.4 21.9 21.8 ───────────────────────────────────

As can be seen from Tables 4 and 5, when the predictive control was performed, the power consumption was reduced by 51.3% on average. Regarding the heating effect, the room temperature was kept around 21 ° C in each time zone, indicating that comfortable heating was achieved.

The energization time prediction formula (linear regression formula) used in the predictive control operation was prepared based on the measured values of the energization time T and the floor temperature θ ₂ in the non-control operation. The prediction formula is described below.

[0027]

[Equation 7] T = -19.0θ ₂ +641 ─── (4) Residual average: 7.6 minutes Correlation coefficient: 0.988

FIG. 1 shows the correlation between the actually measured value (pure heat storage) and the calculated value obtained by the above equation (4) for the energization time T in the non-controlled operation.

[0029]

By measuring the floor temperature θ ₂ of the heat storage type heating system before the start of energization, it is possible to accurately predict the energization time T required for the heating input heat amount of the target building. By incorporating this predictive control method into a heat storage type heating system and operating it, power consumption was reduced and economical and comfortable heating became possible.

[Brief description of drawings]

FIG. 1 shows a correlation between an actually measured value and a calculated value of an energization time T.

Claims (3)

[Claims] 1. A prediction formula (1) showing the relationship between the energization time T and the temperature element .theta.i before the energization is started. (1) T = .SIGMA.ai .theta.i + bi --- (1) (where i is the temperature measurement position. The element serial number, the temperature element θi is the temperature of the temperature measurement position element i before the start of energization, ai is the coefficient of θi, bi is a constant term, and θi is the heat storage material temperature θ ₁ , the floor temperature θ ₂ , the indoor temperature θ ₃ and the outside air temperature θ ₄ and the floor temperature θ ₂ is an indispensable temperature element θi.) The measured values of T and θi are input to the equation ( The multiple regression equation is obtained by obtaining the coefficient ai and the constant term bi of 1), and then the measured value of θi is input to the obtained multiple regression equation to obtain the energization time T and the energization end time (latent heat). Specially, it is controlled to start energization T hours before the time when floor heating starts due to heat dissipation from the heat storage material). The method of heat-up of the heat storage heating system to. 2. A simple prediction equation (2) obtained as a relationship between the floor temperature θ ₂ of the heat storage type heating system before the start of energization and the energization time T (2) T = a ₂ θ ₂ + b ₂ ─── (2) (where θ ₂ is the bed temperature before the start of energization, a ₂ is the coefficient of θ ₂ and b ₂ is a constant term), enter the measured values of T and θ ₂ . , The coefficient a ₂ of the equation (2) is calculated by multivariate analysis.
And a constant term b ₂ are obtained to obtain a multiple regression equation,
Next, input the measured value of θ ₂ to the obtained multiple regression equation, calculate the energization time T, and energize it T time before the energization end time (the time when floor heating is started by the heat dissipation of the latent heat storage material). The method for controlling the heat input of the heat storage type heating system is characterized by being controlled so as to start. 3. The method for controlling heat input to the heat storage type heating system according to claim 1, wherein the heat storage type heating system is a heat storage type floor heating system.