DE19600694A1 - Room air-conditioning, heating control system - Google Patents

Abstract

The heating valve K is regulated with a motor and controls the level of heating. The system has a single chip micro processor A and operates to data in a ROM memory. The processor has a timer module B ,a RAM memory C and an A/D memory E,F,G. The A/D converters receives signals from a hotter/colder switch H. A temperature sensor I and a threshold regulator S signals are fed as input to the processor A. The D/A converter generates an output to control the operation of the motor. The operating condition is based upon a software algorithm that uses fuzzy logic or a neural network to interpret conditions.

Description

The present invention relates to a climate control system, in special a heating control system.

The following description refers by way of example to Heating system. However, the system is arbitrary for regulation Temperatures used, for example, to control the cooling performance of an air conditioner.

From the prior art central heating systems are known in de What in a boiler to a flow temperature warmed What water is passed through lines in individual radiators, in de The transported water gives off its heat and with a flows back to the boiler lower return temperature. typi Usually, every room in a building is one or more Radiators equipped.

There are also heating systems known in which each Room has an individual, independent heating element, for example, an oil stove.

In all types of heating, the problem is the rain room temperature.  

Systems are known in which the user controls the inflow of the heat transporting medium by means of a regulator directly provides. These include, for example, the regulation of the inflow of warm water in a radiator by means of a setting screw be, or the regulation of the feed of gas or oil in an ent speaking heating element. Especially with more modern central Heating systems use thermostatic valves with which a desired room temperature can be permanently adjusted can. After exceeding or falling below the set room temperature In this case, a regulating mechanism (valve) is activated fourth, which reduces or increases the heating power accordingly. When permanently leaving or entering a room must the Mechanism or the valve turned on or turned off.

As disadvantageous in these known heating control systems their great effort is considered.

So must with a simple control of the heating power means an adjusting mechanism constantly be adjusted up and down because This system is not capable of a permanently constant Temperature to produce.

Although a thermostatic valve can maintain a constant temperature right, but when leaving the room by hand be controlled when heating energy is to be saved. The Experience teaches that turning down users is common is forgotten. Will not leave the heating after leaving the room turned off, this leads to useless energy consumption, and even understandably too high costs.

Recently, there are processor-controlled heating control systems I've become known for which explicit time requirements of a User can be entered. So the user can change the tem  at a peripheral radiator (or at a zen tralen heating unit) time related.

From DE-40 09 774 is a method for entering program data into a heating controller for creating a while the daily service life of a heating system Room temperature profile known. Here, the operator of the Investment during a day at fixed times enter the desired temperature values on a setting device, the heating controller is set according to the set values Daily basis profile created. This daily basis profile will be after committed to a learning phase of a few days, if at one the same temperature value at about the same time the following days entered again (confirmation) or by another tempera value is replaced (correction), or if after expiry of a no confirmation or correction took place for a certain time. To Values entered at the end of the learning phase only become the cor the daily base profile is taken into account when the controller an explicit instruction is given.

In this method, it is considered disadvantageous that of User or operator of the heating a high attention ver is reached, since he is at different, regular times Tempe has to make input to which then a temperature profile is created. Such a method is also very inflexible, because a changeover of the system ver again with programming effort ver is bound. Experience has shown that in such a case then already stored temperature profile, which is the current Requirements no longer completely accepted, accepted to escape this programming effort.

Especially in large office buildings, the above description NEN disadvantages apparent. Because of inattention of the  Users incur unnecessary heating costs, this is often ver seeks to provide fixed settings of central heating. A However, such firm attitude is very insufficient on the modern working world (flexible working hours, etc.). Depending on Start of work of the individual employee becomes in a room too early, in another heated too late.

The object of the invention is therefore to create a climatic cycle system that can be operated in a simple and efficient manner is so that the attention of a user as little as possible is claimed, and effective cost can be saved.

This problem is solved by a climate control system with the Features of claim 1.

With the climate control system according to the invention is a wesent more economical use of resources or energy and thus ensures a cost savings. The indoor climate can be optimal adjusted to the needs of one or more persons the.

Furthermore, a high level of user-friendliness is ensured. The User does not have to do complicated programming. unmarried This is due to concrete user input or instructions System adjustable.

Depending on the version of the system, different user inputs are specified:
In the simplest embodiment, only the instructions "present" or "absent" are provided. According to a given instruction, the system regulates to a preset temperature.

According to a preferred embodiment shown in FIG. 1, "warmer" and "colder" inputs are possible. These instructions may be provided along with the above "present" / "away" instructions, but it is also sufficient to leave only these "warmer" or "colder" statements. The system is capable of generating both a presence forecast and a forecast of a desired temperature profile based solely on these "warmer" and "cold" statements.

The climate control system according to the invention is both central heaters, as well as in non-central heating, such as local Radiant heaters, etc., usable. At a central heating can the control according to the invention both at the central control unit (for example boilers), as well as to individual heating bodies are made.

The climate control system according to the invention is able indi individual user habits, such as kitchen use only at mealtimes, bath only in the morning and in the evening, sleep Room usage only during the night to be considered.

To be considered particularly advantageous that the erfindungsge suitable system without much effort on conventional heating system can be retrofitted.

Advantageous developments and modifications are counter stood the dependent claims.

Embodiments of the invention will now be described with reference to the beige added drawings explained in more detail.  

It shows:

Fig. 1 shows schematically a preferred embodiment of to the invention OF INVENTION Climate control system, namely the construction of a heating control system,

Fig. 2a is a presence prognosis for a typical week day,

FIG. 2b shows a presence prognosis for a holiday,

Fig. 3 shows a desired temperature profile and an actual temperature profile when providing an automatic Her under control of the temperature.

In Fig. 1, the air conditioning system according to the invention is the case of a peripheral heater of a central heating Darge presents.

A heating valve K can be controlled by means of a servomotor L. The climate control system according to the invention is in the environment of the heating valve K, for example, directly on the radiator, ange introduced.

The system has a single-chip micro-processor A. This microprocessor includes a ROM (program memory) (not shown). Microprocessor A is connected to a timer module B, a RAM module C and AD converters E, F, G. The A / D converters E, F, G each receive signals from a warmer / cold switch H, a temperature sensor I and a threshold value controller (S-value controller) J. The AD converters E, F, G via the lines 1.1 , 1.2 , 1.3 received signals to the microprocessor A via the lines 2.1 , 2.2 , 2.3 weitergege ben. A processed signal is given via line 4.1 to a DA converter D. The signal generated by this DA converter D signal controls the actuator L, which regulates the opening state of the heating valve K.

A new setting of the date or the time is about one Switch O possible.

Optionally, further inputs can be provided. By way of example, FIG. 1 shows a moisture sensor N, which is connected via a line 1.4 to a further AD converter M. The signal generated by this AD converter M is given to the microprocessor A via a line 2.4 .

Another option of an optional probe is at play an outdoor temperature measuring device mentioned.

The system controls the heating activity with the help of a progno se about the presence of a user.

It will first be described in the following, in which Wei the climate control system according to the invention a prognosis over created the presence of a person in the room to be heated.

The state of the heating system is over the time t (eg abso lute time t or date and time of day) and all outside entrances completely described. The time t is here by the timer construction stone B provided. These state parameters will be in the following called "ground state".

The user enters via the switch H to the system as desired, usually at irregular intervals, instructions. In Ausfüh approximately example according to FIG. 1, only the inputs "warmer" or "colder" are possible. The system thereby contains information regarding the input time and the desired temperature control.

In a simplified embodiment, it is also possible to Enter "presence of the user" or "absence of the user to be provided.

It is possible that the time thus entered is discrete time interval to allocate. For example, if you divide the 24 hours of the Day in 96 15 minute intervals, will be an entered time each associated with such a 15 minute interval. The Inter Valleys do not necessarily have the same length. It is For example, in the early morning to provide two-hour intervals.

Additional parameters, from the ground state for the forecast basis are, for example, time of day, year time, weekday or the presence of a Sunday or holiday.

The derived parameters along with the ground state become hereinafter referred to as "derived state".

Alternatively, the derived parameters can be used as coefficients of Fourier functions, trigonometric functions, polynomials or Taylor series. This proves to be an advantage liable if a periodic behavior with a period duration from Z. For example, a day should be forecast.

The system stores in RAM C the user inputs together men with the current ground state, that is with respect to the User input becomes an n-tuple, consisting of the user and the parameters time and temperature. A  such "memory unit" consisting of user instruction and Basic state is called "instruction" in the following. The total melten instructions form the memory of the "learning" Heating control system and provide the basis for more Calculations.

At overflow of memory, the oldest instructions be deleted or overwritten.

On the basis of all stored instructions becomes the forecast posed about the user's presence. This presence forecast is recreated or updated with each user input lisiert. It takes place in the background, that is, almost parallel (Multitasking).

The microprocessor A now calculates based on the stored for the following time interval (eg 1-5 Std.), For example, minute-by-minute discretized, the property probability of a user. This becomes a true calculated in the interval from 0 to 1.

It can also have a general presence function yet continuous presence probabilities determined who which, however, are scanned discreetly again.

The interval length of the forecasting interval should be at least be the same length of time as the heater for the high heat to a maximum temperature on average (heating leading period).

A preferred method for creating the prognosis is the Regression analysis. This method is from the field of Sta and forecasting technology well known.  

As an alternative to the regression analysis, elements of the new ronal networks, varianzreduzierende method or a fuzzy Logic method can be used.

It is in this context regarding the combination of Basic parameters (in general, but not necessarily, a linear combination), only important that their weights be estimated so that they are as good as possible the target size (Anwe senheit of the user).

The regression analysis determines the weights of the parameters of the derived state, so that this weighted combines the Estimate the probability of presence.

This will be explained in detail by means of an example:

It should in the following example about a week's presence in a typical law office can be predicted.

The following terms are introduced:
t: time t on an infinite time axis,
f (t): presence function at time t, where f (t) = 0 indicates that the user is absent at a time t, and f (t) = 1 indicates that a user is present at the time t,
R (t): current room temperature at time t of a user input.

The ground state of the system is represented by the values t and R (t) certainly.

An "instruction" is defined by a tuple (t, R (t), f (t)). That is, an instruction tuple consists of the time t, the tempe C and the position of the switch H (f (t) = 0 or 1) to Time t.

In the case of further sensor inputs, z. B. the humid nometer, the instruction tuple is extended accordingly. In the case of an additional air moisture meter N it would then: (t, R (t), f (t), N (t)).

From a given ground state at time t, the derivative state consisting of an amount F₁ (t) to F _n (t) is now generated.

The functions F₁ to F _n can be either discrete (workday yes / no, holiday yes / no, etc.) or continuous (sine function, Cosi nusfunktion, etc.).

In order to predict a recurring daily course, the Fourier basis functions (sin x, cos x, sin 2x, cos 2x,. , .) with a transformation of the time of day from 0:00 to 24:00 on the interval 0 to 2π. Shall be wide regularities are forecast to be about a week (Month, year), the corresponding interval becomes (0-2π) shown, ie about Monday 0⁰⁰ to 0 and Saturday 24⁰⁰ to 2 π. Otherwise, for a more advanced forecast of the User presence, not only by the time of day gege ben, such as using a room on weekdays (Monday until Friday), as well as limited on Saturday, but not on Sundays or public holidays, etc., are discreet, dated from the date  initiated functions (holiday Yes / No with corresponding radio tion values 1 or 0), which are superfluous with the continuous functions to be cleaned.

In the present example, the following are derived Functions predicts, where with t 'the transformation of the Time interval (0: 00, 24: 00) after (0, 2π) is defined:

Weekday Yes / No (1, 0) F₁ (t) Holiday Yes / No (1, 0) F₂ (t) Sin t ' F₃ (t) Cos t ' F₄ (t) Sin 2t ' F₅ (t) Cos 2t ' F₆ (t) , , , @ Sin 11t ' F₂₃ (t) Cos 11t ' F₂₄ (t) 1 F₂₅ (t) (constant value)

With the derived functions F₁ (t) to F _n (t) and the property fs (t), the linear regression model described below is built according to the "least squares method".

Table 1 shows the times during which a user is scheduled to log in System enters a statement marked with crosses. In the Table 1 can be viewed half an hour from the user zer have been entered via switch H. But that's just it if possible, that the user only at the beginning and the At the end of his presence, switch H is always labeled "Warmer / Käl instructs or informs the system that it is present / absent is. In such a case, the time between these two points in a freely selectable manner, not  necessarily equidistant, discretized, and it becomes each generated discrete time t of the associated destination radio tion value or presence value f (t) generated in such a way that f (t) = 1 when t is in the presence phase and others if it is zero.

In the concrete example, the late evening and night hours (from 20 o'clock) hourly, and the remaining periods half-hourly dis cretized (see Table 1).

For n generated instructions (direct user instructions or "intermediate statements" generated by the system), a matrix F with the elements F _i (t _j ) with i = 1 _,. , , n and j = 1,. , , m, ie a matrix with m rows and n columns.

The result is the target vector f with the elements f (t _j ) with j = 1,. , , m. The result is also the vector with the weights of the individual F _i , namely X with the elements x _i (i = 1,... N).

In the present example, the system shown in Table 1 entered visible attendance times, z. On Mondays Presence 8: 30-11: 30 and 13: 30-17: 30 clock.

Based on these data, the weights x _i , i = 1,. , , n the presence prediction function

P (t _j ): = Σ x _i f _i (t _j ) with i = 1 _,. , , n and j = 1,. , , n

determined so that the objective function or presence function f (t) P (t) is predicted as well as possible. This happens after the method of least squares (Gaussian method, too called linear regression analysis). It is the following for this solve linear equation system:

F _* F ^T _* X = f,

where F ^{T is} the transpose of the matrix F defined. The result is the following solution:

X = (F _* F ^T ) ^-1 _* f.

In the example chosen arise as a solution in the upper most of the rows indicated in Table 2a, ie z. B. x₁ = 0.19, x₂ = -0.18 etc.

The concrete forecast value at time t then results as the result value of the prediction function P (t) with the calculated x _i .

FIGS. 2 a and 2 b show the presence forecasts determined with the calculated weights for a typical day of the week ( FIG. 2 a) or a holiday ( FIG. 2 b).

The forecast values are in the column "Forecast" in the respective Tables 2a and 2b explicitly stated.

With the introduction of a threshold (S-value) can now determine when to forecast presence d. H. for which times to heat.

Is about, as in the present example (see Fig. 2a and 2b), assuming a threshold of 0.5, it is for all time intervals Inter with a predicted presence probability greater than 0.5, the presence, and for the presence of true probabilities less than or equal 0,5 the absence assumed.

The determined presence probabilities are in the present example, continuous values between 0 and 1, where a value of 0 represents a safe absence while a 1 means a secure presence. Calculated values that less than zero will also be a safe absence assigned.

In a subsequent step, the presence forecasts discretized, d. H. either fixed at 0 or fixed at 1 det. If the probability value is greater than the threshold, If this probability is mapped to 1, the true is It will be less than or equal to the threshold shown on 0

By presetting the threshold, it is possible to use the Heating characteristic individually, namely rather conservative or rather progressive, to govern.

Setting a low threshold means that even with small probability of presence, d. H. at Suspicion of presence, being heated, while a high coffin lwert a heating process only when very certainly assumed Anwe in motion.

A high threshold value saves money, but brings insofar as inconvenience with, as under certain circumstances the User encounters an unheated environment.

A low threshold, however, brings larger Convenience, because even at relatively low attendance Probably the heating is set in motion. The attitudes A low threshold is of course higher Heating costs connected. The threshold switch is also available as input /  Off switch usable. If the threshold z. B. turned on 0 is already present at a probability of presence from 0, d. H. always, heated.

In Fig. 2a it can be seen that the probability of presence exceeds the threshold, which is set here by way of example to 0.5, only at certain morning or afternoon times. On a public holiday, the probability of presence remains below the threshold, so that the heating does not go up ( FIG. 2b).

The described creation of the Presence Forecast is already done possible with a system which only activates the user input " It is with such a system for example, possible that with predicted presence always regulated to a predetermined temperature. This before set temperature can be either by the user, or too centrally set for a group of heating elements who the.

It is according to another embodiment of the erfindungsge However, heating systems are also possible on the basis of Presence forecast a forecast of a desired tempera to create a structure. For this purpose, it is provided that the user zer over the switch H both "warmer" - and "colder" -Anga enter into the system. This is analogous to the above described embodiment, each such input first interpreted as presence input, and the one described above Invoice carried out.

Based on the presence forecast will match with the heated the supply so that the forecasted property a temperature is reached by a desired temperature  tur function (desired temperature) C (t) is defined.

C (t) is determined analogously to P (t), wherein for the calculation of C (t) values c (t) are used, which are generated as follows:
If a "warmer" instruction is given at a time when the room temperature R (t) is greater than or equal to C (t) minus δ T (delta T), the new desired temperature value c (t) is determined as C ( t) + δ T. If a "colder" instruction is given, ana log c (t): = C (t) minus δ T. Here, δ T is a freely selectable temperature difference with which the previous desired temperature C (t) is corrected.

It is possible C (t) factory, z. B. at a constant 20 ° C before to admit.

If the respective conditions are not fulfilled, then no new c (t) is generated.

The weights y _i , i = 1. , , n the desired temperature forecasting function

C (t _j ): = Σ x _i c _i (t _j ) with i = 1. , , n and j = 1. , , m

are to be determined such that the objective function c (t) is represented by C (t) is predicted as well as possible. Such a prognosis function becomes analogous to the presence prediction function described above advantageously by the least squares method determined. It is the following linear equation system for this purpose

F _* F ^T _* Y = c

to solve, where with F ^T, the transpose of the matrix F is defined. The result is the following solution:

Y = (F _* F ^T ) ^-1 _* f.

A concrete calculation example is not given here, it However, it should be noted that such an invoice itself completely analogous to the prognosis of Presence Probation in which only f (t) is replaced by c (t) and appreciated.

In a start-up phase, the system can initially at assumed Presence of the user a preset desired temperature taking. This preset desired temperature can either factory or determined by the user.

In the case of a user instruction "warmer" or "colder" erge there are two cases:

• 1. The system determines that it has already reached the desired temperature. The user input thus means that the previous desired temperature is either too high or too low.
  In this case, the desired temperature is increased or decreased by a differential amount (for example, between 0.5 and 1 ° C), and stored together with the ground state (absolute time and external inputs).
• 2. In the second possible case, the system determines that it is the previously valid desired temperature has not yet reached. In In this case, from a user instruction of the kind "would "nothing can be closed  User instruction of the type "colder" a new, lower one Desired temperature can be derived at this time.

Overall, the system thus generates a presence function in Combination with a temperature profile. So if the property probability of the user's default Threshold or probability value has exceeded, the heating is adjusted to a corresponding temperature value gelt, which progno. Based on the stored temperature profile is stigmatized.

It is for the purpose of a flexible heating operation or a fast re-learning of the heating system possible, user's instructions weighted according to their relevance. So can at For example, the recent user inputs are weighted more heavily the (exponential weighting).

For an effective installation of the heating control system, various possibilities are advantageously usable:
First, it is possible to provide the system already factory with a presence probability profile and a Temperaturpro fil. In this case, standard profiles can be used who the, but it could also be based on individual information of users or buyers of the system-oriented profiles are created by the factory.

It is also possible, a completely unprogrammed or uninitialized system. This naturally leads especially in the start-up phase of the system, that a still completely insufficient prognosis basis exists. The User must then be prompted to make inputs which then build a reliable forecasting system  can.

Is the system off with a presence / absence switch? equipped and able to switch between a presence statement and an absence statement (i.e., in the presence of the user zers) on their own at discrete time intervals generate the forecasting process in a relatively short time provided an adequate statistical basis.

In the case, however, that only concrete inputs of the user the System are available as a basis for forecasting, in an initial phase, generally after installation of the Heating, any forecast of user presence or about the desired temperature with considerable uncertainties or Be subject to fluctuations.

For this purpose, the user, depending on the number of existing instructions, so irritated or excited that he feels obliged to instruct the system more often give.

This happens because the system of its own tempera downgrades, so that the user often the "heat mer "statements, which of course implies the system zit the presence of the user is communicated.

Also in the event that one system is operated by multiple users it is, as it will typically occur in offices, it is meaningful, the presence or desired temperature forecast on to provide a solid statistical basis.

In the following, with a heating interval of the time cut from t₀ to t₁ denoted by the jump of the estate  The safety prognosis from "absent" to "present" starts, and with the corresponding jump from "present" to "absent" ends.

With t _i is a time within such an interval designated (t₀ less than or equal t _i less than t₁) ie t _i is from the range of a contiguous period of time for which presence is predicted. t o with every new user input at time t _currently new to the time t _currently set. The interval t₀ to t₁ shortens according to the interval t _current to t₁.

To explain the Temperaturherunterregelung the Reizfunk tion Z (t _i ) is introduced:

where p is a function of the number of instructions such that Z (t _i ) per hour z. For example, 2.4 degrees C for a small number of instructions, 1.0 degrees C for an average number of instructions, 0.3 degrees C for a large number of instructions, and 0.0 degrees C for a very large number of instructions Instructions increased.

If such a stimulus function is provided in the system, this regulates Effectively not to the calculated desired temperature C (t), but by the temperature decreased by Z (t), d. H. on C (t) - Z (t), where Z (t) then starts again with 0, then when either a new occupancy interval begins, or when the user has given an instruction (warmer or colder).

A greatly simplified, but in principle typical dynamic heating course, taking into account such a user tion is shown in Fig. 3. It is assumed that conditions in the system, only a small total number of user instructions is stored.

According to FIG. 3, the following system parameters are defined:
Since only a small number of stored instructions are available, the system regulates the desired temperature C (t) by 2.4 ° C per hour. C (t) is initially 20 ° C. δ T is set at 1 ° C. The current heating interval is from 8:10 to 13:10.

Further, as shown in FIG. 3, user inputs of the "warmer" type occur at times 8:50, 9:30, 9:50, 10:50, 11:30 and 12:40.

Since in this example is very much down regulated, namely at 2.4 ° C / hour, the user is too frequent system instructions coerced.

In Fig. 3, the upper line shows the predicted desired temperature C (t) while the sawtooth line represents the effective temperature history. The user inputs are marked by the squares.

The system regulates the temperature of 15 ° C at 8:10 am on the predicted desired temperature of 20 ° C high, and at 13: 10 o'clock back to 15 ° C.

If, at the time of an instruction, the temperature is one value greater than or equal δ T has dropped, in the case of an instruction the predicted desired temperature of 20 ° C restored.

If, however, as shown by way of example for the time 9:50 o'clock in FIG. 3, the temperature has not yet been reduced below a value C (t) -δ T, because not enough time has passed since the last instruction, a new prognosis will be made created over the desired temperature, and calculates the newly calculated desired temperature with 21 ° C. This process is shown greatly simplified in the present example. For a concrete calculation of the new desired temperature, a calculation according to the least squares method can of course be used advantageously.

As is further apparent from FIG. 3, at the time 9:50 o'clock each user instruction is raised to the new desired temperature of 21 ° C.

According to another embodiment, not shown the temperature in case of user input not on the Desired temperature C (t) raised, but only by one value δ T increased. In such an embodiment would u. U., um to raise the heating back to the desired temperature, more once an instruction is entered.

Such an automatic control of the heating is also advantageous in the saving of heating costs used.

The inventive climate control or heating control By itself, the system is fully capable of one to carry out efficient and user-friendly regulation.

However, it is of course also possible that invented The proper control system together with conventional control use systems, so that may be optional on one of the systems can be used. Is z. For example, that on certain days or at a certain time a completely  Unusual occupancy or use of a room is pending, that can inventive control system temporarily turned off who the.

components:
A: Single-Chip μProcessor inclusive ROM (Program Memory)
B: Timer block
C: RAM block
D: DA converter
E, F, G: AD converter
H: warmer / cooler switch
I: temperature sensor
J: Continuous "S-value controller (from 0 to 1)
K: heating valve
L: Stell (step) motor that controls K

optional:
M: Further AD converters
N: Additional sensors / sensors, eg. B. humidity sensor
O: switch for entering date and time

Signal exchange / lines:
1.1: H indicates switch position E
1.2: I gives switch position to F
1.3: J indicates switch position G
2.1-3: E, F, G give signals to A
3.1: A writes / reads values to / from C
3.2: A reads out times / date B, A starts B
4.1: A gives signal to D
5.1: D controls L

optional:
1.4: Additional sensors give switch position to further M
2.4: Further M give signals to A

Table 1

Table 2a

Table 2b

Claims (18)

1. Programmable climate control system for at least one room, characterized in that the probability of presence and / or the desired temperature of at least one user can be predicted via a prediction method, wherein the operating state of the system can be set as a function of the determined presence probability. 2. Climate control system according to claim 1, characterized in that that the prognosis method methods of regression analysis used. 3. Climate control system according to claim 1, characterized in that that the forecasting method uses methods of fuzzy logic. 4. Climate control system according to claim 1, characterized that the prognosis procedure with neural networks  is working. 5. Climate control system according to claim 1, characterized gekennzeich net, that the prognosis method varianzreduzierende method used. 6. Climate control system according to one of the preceding claims, characterized in that the determined presence probability with a via a threshold controller adjustable threshold is compared, depending on from falling below or exceeding the threshold Threshold of the operating state of the system is determined. 7. Climate control system according to claim 6, characterized net, that the threshold between 0 and 1 continuously on is adjustable. 8. Climate control system according to one of the preceding claims, characterized in that the means of prognosis Verfah determined presence probability on the basis Instructions are given by a user in the System are entered. 9. Climate control system according to claim 8, characterized net, that the user presence or absence can be entered into the system. 10. Climate control system according to claim 9, characterized net, that the presence or absence instructions over the threshold value can be entered. 11. A climate control system according to any one of claims 8 to 10, characterized in that "warmer" instructions or "Käl  ter statements can be entered into the system, allowing the System both attendance instructions and instructions regarding a desired temperature control available be put. 12. Climate control system according to one of claims 8 to 11, characterized in that the instructions via keys are negotiable. 13. Climate control system according to one of the preceding claims, characterized in that a temperature profile can be created is. 14. Climate control system according to claim 13, characterized gekennzeich net that the temperature profile based on the instructions create bar that a user enters into the system. 15. Climate control system according to one of the preceding claims, characterized in that younger user inputs in the Determination of the probability of presence stronger weighted tbar. 16. Climate control system according to one of the preceding claims, characterized in that it continuously strives to minimize energy consumption. 17. Climate control system according to claim 16, characterized net, that it is a heating control system, which by itself strives to konti the room temperature to level down. 18. Climate control system according to claim 16, characterized net, that the measure of the down regulation of the room temperature  depending on the number of saved user instructions is gene, with a small number stored Anwei a stronger and a larger number preceded by a weaker downshift hen is.