METHOD FOR CONTROLLING AN ELECTRICAL POWER CALL

Abstract

Method (100) of controlling a power demand of a heating and / or cooling device of a room in a building, the method being implemented by a control device comprising electronic circuitry adapted to implement the method.The control process includes at least the following steps: (Step 1) Acquire, for a predetermined duration, temperature data including an outside room temperature, an inside room temperature and a setpoint temperature of the heating and / or cooling device, data on the electrical power consumed by the heating and / or cooling device; (Step 2) Model an electrical power demand corresponding to a restart phase of the heating and / or cooling device; (Step 3) Predict the electrical power demand induced by the heating and / or cooling device; (Step 4) Control the heating and / or cooling device, based on a prediction from step 3 to optimally integrate it into a building's electrical consumption mix.

Description

Title of the invention: METHOD FOR CONTROLLING AN ELECTRICAL POWER CALL Technical field

[0001] The invention relates to the field of control of heating and / or cooling devices using Joule or thermodynamic effect. STATE OF PRIOR ART

[0002] It is known to use heating and / or cooling devices, such as heat pumps. Such devices are generally controlled by a setpoint temperature. In a known manner, when the indoor temperature falls below (rises above) a certain threshold relative to the setpoint temperature, the device is activated to regulate the temperature by heating or cooling the room in a building in which the device is installed.

[0003] The activation of the device to regulate the temperature is commonly referred to as "restart." These are periods during which the device is restarted to regulate the temperature from a reduced setpoint temperature to a comfort temperature. Typically, three distinct operating phases are distinguished for heating and / or cooling devices such as heat pumps: a restart phase in which the device is potentially operated at its nominal power, a maintenance phase at the comfort temperature, and a setback phase in which the temperature can drift to a low setpoint (or high in the case of cooling) from which point the device is activated to maintain the setback temperature. Heating and / or cooling devices can generate a significant electrical power demand during a restart.However, a peak in power consumption can trip an electricity meter if the electricity supply contract doesn't allow for the required power. Furthermore, a peak in consumption can significantly impact an electricity bill if it occurs during a time of day with a high tariff.

[0004] In this context, it is necessary to provide a control method which makes it possible to predict a power demand corresponding to a recovery period in order to integrate it optimally into a building's electrical consumption mix and to minimize the electrical power consumed and / or billed. Description of the invention

[0005] To this end, according to a first aspect, a method is proposed for controlling a power demand of a heating and / or cooling device in a room of a building, the method being implemented by a control device comprising electronic circuitry adapted to implement the method. The control method comprises at least the following steps: - (Step 1) Acquire, for a predetermined period, temperature data including an outside room temperature, an inside room temperature and a setpoint temperature of the heating and / or cooling device, data of electrical power consumed by the heating and / or cooling device; - (Step 2) Model an electrical power demand corresponding to a restart phase of the heating and / or cooling device; - (Step 3) Anticipate the electrical power demand induced by the heating and / or cooling device; - (Step 4) Control the heating and / or cooling system, based on a forecast from step 3, to optimally integrate it into a building's electrical consumption mix.

[0006] Thus, the control method makes it possible to anticipate a power demand corresponding to a recovery period in order to optimally integrate it into a building's electricity consumption mix. In the case of local photovoltaic energy production, the method can make it possible to synchronize the operation of the heating and / or cooling system with a production period, in order to maximize self-consumption.

[0007] According to a particular provision, step 2 of modeling includes establishing a simplified model of a recovery phase.

[0008] According to a particular provision, the duration model is established as a function of an internal temperature difference between the acquired internal temperature and a maximum acquired internal temperature, such that: <itm = mâxtT.Jj g relance^ - Tjnt(t = o)

[0009] According to a particular arrangement, the duration model is established as a function of the difference between the indoor and outdoor temperatures, both acquired at the same instant, so that [OOiO] Ara-0) -Tex^t-0)

[0011] According to a particular arrangement, the duration model is established as a function of the internal temperature difference and the temperature difference, such that Duration = f ( dT^ AT ( / = 0))

[0012] According to a particular provision, step 2 of the modeling includes calculating an average temperature difference between the inside and outside over the duration of a restart phase, such as: AT -TExt(to, to + duration) - TInt(to, to + duration)

[0013] According to a specific provision, step 2 of the modeling process involves determining a maximum power used according to and to determine the electrical energy consumed according to ^elec = AT)

[0014] According to a particular provision, the forecasting step 3 includes integrating forecast data into the model, so that a setpoint temperature is used instead of a maximum indoor temperature and / or a forecast outdoor temperature is used instead of an outdoor temperature.

[0015] According to another aspect, a device is proposed which includes electronic circuitry for implementing a process comprising at least the following steps: - (Step 1) Acquire, for a predetermined period, temperature data including an outside room temperature, an inside room temperature and a setpoint temperature of the heating and / or cooling device, data of electrical power consumed by the heating and / or cooling device; - (Step 2) Model an electrical power demand corresponding to a restart phase of the heating and / or cooling device; - (Step 3) Anticipate the electrical power demand induced by the heating and / or cooling device; - (Step 4) Control the heating and / or cooling system, based on a forecast from step 3, to optimally integrate it into a building's electrical consumption mix.

[0016] According to another aspect, a computer program product is proposed comprising program code instructions for executing the process.

[0017] According to another aspect, a non-transient storage medium is proposed on which is stored a computer program comprising program code instructions to execute the process, when said instructions are read from said non-transient storage medium and executed by a processor. Brief description of the drawings

[0018] The features of the invention mentioned above, as well as others, will become clearer upon reading the following description of at least one example of implementation, the said description being made in relation to the attached drawings, among which:

[0019] [Fig-1] schematically illustrates the sequence of a piloting process;

[0020] [Fig.2] schematically illustrates the modeling of a recovery phase;

[0021] [Fig.3] schematically illustrates a linear regression of the duration of a phase of restart based on the difference between indoor and outdoor temperatures acquired at the beginning of the restart phase;

[0022] [Fig.4] schematically illustrates a linear regression of the maximum electrical power called as a function of an average difference between the acquired indoor temperature and the acquired outdoor temperature;

[0023] [Fig.5] schematically illustrates a linear regression of the electrical energy consumed as a function of the average difference between the internal temperature acquired and the external temperature acquired;

[0024] [Fig.6] schematically illustrates a computer system adapted to implement the process.

[0025] DETAILED DESCRIPTION OF IMPROVEMENTS

[0026] Piloting method

[0027] With reference to [Fig. 1], according to a first aspect, a process is proposed 100 for controlling a power demand from a heating and / or cooling device in a room of a building. Typically, the heating and / or cooling device can be a heat pump.

[0028] The process 100 is implemented by a control device comprising electronic circuitry 200 adapted to implement the process. The control device will be described below.

[0029] As schematically shown in [Fig. 1], the piloting method 100 comprises at least the following steps: - (Step 1) Acquire, for a predetermined period, temperature data including an outside room temperature, an inside room temperature and a setpoint temperature of the heating and / or cooling device, data of electrical power consumed by the heating and / or cooling device; - (Step 2) Model an electrical power demand corresponding to a restart phase of the heating and / or cooling device; - (Step 3) Anticipate the electrical power demand induced by the heating and / or cooling device; - (Step 4) Control the heating and / or cooling system, based on a forecast from step 3, to optimally integrate it into a building's electrical consumption mix.

[0030] Thus, the control method 100 makes it possible to predict a power demand corresponding to a restart period in order to optimally integrate it into a building's electricity consumption mix. In the case of local photovoltaic energy production, the method can make it possible to synchronize the operation of the heating and / or cooling system with a production period, in order to maximize self-consumption.

[0031] Concept of relaunch

[0032] It is specified that in this document, "restart" refers to an operating phase of the heating and / or cooling device, during which the heating and / or cooling device is restarted to regulate the temperature from a reduced setpoint temperature to a comfort setpoint temperature. During a restart phase, the device is potentially operated at its nominal power.

[0033] Figure 2 shows the evolution of the indoor temperature (curve 14), the electrical power consumed (curve 15), and the outdoor temperature (curve 16) during a restart phase. Figure 2 allows observation of the evolution of the indoor temperature during a restart phase and the peaks in electrical power consumption during the restart phase. It should be noted that the curves in Figure 2 correspond to a specific example; the precise values ​​of the points on the curves are specific to the example shown and depend on the characteristics of each building and each heating / cooling system.

[0034] Step 1 - Acquisition

[0035] As previously stated, the process 100 first includes a step 1 of acquiring temperature data, for a predetermined duration.

[0036] Typically, the acquired temperature data includes: an outside room temperature, an inside room temperature, and a setpoint temperature for the heating and / or cooling device. Acquiring the setpoint temperature and the inside room temperature allows for a comparison of a desired temperature (the setpoint temperature) with an ambient temperature (the inside room temperature). As will be described below, an increase in the difference between the inside room temperature and the setpoint temperature allows for the prediction of an imminent restart phase; this is particularly observable in the graphs of [Fig. 2].

[0037] The acquired temperature data may also include the outside temperature of the room. Acquiring the outside temperature of the room makes it very advantageous to predict variations in the inside temperature of the room. Indeed, regardless of the quality of the room's insulation, an increase or a A decrease in the outside temperature necessarily affects the inside temperature of the room. In other words, the outside temperature inevitably impacts the energy losses of the building containing the room in question, and consequently, the outside temperature impacts the energy required to achieve the temperature change during the restart phase. This is particularly evident in the graphs of [Fig. 2], where it is clear that a decrease in the inside temperature corresponds to a decrease in the outside temperature.

[0038] Advantageously, incident solar radiation data can also be acquired. As with outside temperature data, incident solar radiation data can also influence the energy required to perform the temperature variation during the restart phase.

[0039] In addition, the acquired data also includes data on electrical power consumed by the heating and / or cooling device.

[0040] Preferably, the acquired data are time-stamped, which makes it very advantageous to carry out associations and comparisons.

[0041] In addition, preferably, the acquisition takes place over a predetermined period, so as to acquire a plurality of data.

[0042] Step 2 - Modeling

[0043] The process 100 then includes a step 2 of modeling an electrical power demand corresponding to a restart phase of the heating and / or cooling device.

[0044] With reference to [Fig.2], the objective of the modeling step is to determine the envelope 11 as a function of the data acquired in step 1.

[0045] In other words, as schematically shown in [Fig.2], the data acquired over the predetermined period can be graphically represented according to curves 14, 15 and 16.

[0046] Thus, in [Fig. 2], curve 14 represents the evolution of the indoor temperature as a function of time. Curve 15 represents the evolution of the electrical power consumed as a function of time, and curve 16 represents the evolution of the outdoor temperature as a function of time.

[0047] The objective of step 2 is to recognize a recovery period, based on the data acquired, in order to deduce a model schematized by the envelope 11. In other words, step 2 makes it possible to obtain a model representing the envelope 11 of the electrical power induced by the recovery.

[0048] Thus, in a particularly advantageous way, step 2 of the modeling process involves establishing a simplified model of a recovery phase. As such, As detailed below, the modeling of step 2 may involve establishing three models: duration, average power, and maximum power. These three models, together, allow for the modeling of a restart phase to determine envelope 11.

[0049] The model for the duration of a restart phase can be established as a function of the difference in indoor temperature between the acquired indoor temperature and a maximum acquired indoor temperature, such that: dTint - max^ e raise^ - Tin^t - oj

[0050] The model for the duration of a restart phase is also established as a function of the difference between the indoor and outdoor temperatures, both acquired at the same instant, so that A7'( / = 0) = 7-,-, / ( = 0)-7^( = 0)

[0051] Thus, the model of a duration of a restart phase can be established as a function of the difference in indoor temperature and the temperature difference, such that Duration = f ( dT^ AT (7 = 0))

[0052] With reference to Fig. 3, the duration of the restart is expressed in hr / °C. It is specified that the °C corresponds to the temperature increase induced by the restart (previously denoted dTint). The duration is expressed as a function of the difference between indoor and outdoor temperatures at t=0. Fig. 3 represents the superposition of a linear regression on a series of points obtained. This linear regression corresponds to the function f sought in the equation for determining the duration.

[0053] According to a particular provision, the modeling step also includes calculating an average temperature difference between the inside and outside over the duration of a restart phase, such as: AT = TExt (to, to + duration) - Thlt (to, to + duration)

[0054] According to a particular provision, the modeling step also includes determining a maximum power used according to j _ tLt€C / \ z and to determine the electrical energy consumed according to £^ = ^7-,,. ÂT)

[0055] Indeed, as previously indicated, the clever combination of modeling the duration of the restart phase with modeling the average power and the maximum power makes it possible to obtain a simple and usable model of a restart phase.

[0056] Fig. 4 represents the maximum electrical power called as a function of the average difference between indoor and outdoor temperatures and the associated linear regression, which corresponds to the function g of the equation expressing the maximum power used.

[0057] Fig. 5 represents the electrical energy consumed in kWh / °C as a function of the average indoor / outdoor temperature difference. °C is the temperature increase induced by the restart (previously denoted dTint), and the associated linear regression

[0058] According to a particular provision, the average electrical power consumed can be calculated by combining the results of the determination of the average temperature difference and the results of the determination of the electrical energy consumed, such that:

[0059] p-- _

[0060] Step 3 - Forecasting

[0061] As previously stated, following the modeling, the process 100 includes a step 3 for forecasting the electrical power demand induced by the heating and / or cooling device (i.e., forecasting the evolution of the electrical power requirement of the heating and / or cooling device). Step 3 corresponds to an application phase of the model established in step 2.

[0062] According to a particular provision, forecasting step 3 involves integrating forecast data into the model, such that a setpoint temperature is used instead of a measured maximum indoor temperature and / or a forecast outdoor temperature is used instead of a measured outdoor temperature. In other words, forecasting step 3 is not based on an observation of temperature variations, as is the case for modeling step 2, but seeks to take into account a future temperature variation by integrating into the model the setpoint temperature, which is estimated to be a maximum indoor temperature to be reached. Similarly, data from scenarios or other forecasts can be used instead of acquired data. It is specified that the data integrated into the model for step 3 can be updated when new restart sequences are recorded.

[0063] During the execution of step 3, the equations established during step 2 are used with different input data, such that:

[0064] dT^Tc^-T^ÿ

[0065] Ar = (to,to+duration) +

[0066] The following equations, combined with the calculated input quantities, are used to express the duration forecast, the maximum electrical power demand, and the average electrical power demand:

[0067] Duration AT (f - 0) (j) + D2) x dTjj) W [00681 x( Tim(j)-TEx / jY)+Ml[w

[0069] P^eU) = (El x ( T^(j)-T^UÏ)+E2l)xdTint( j) / Duration U) x 1000 [Wh / °C]

[0070] With Dl, D2, Ml, M2, El and E2 are parameters from the modeling of step 2. More precisely, these parameters are obtained by linear regression of the model of step 2.

[0071] Step 4 - Piloting

[0072] The process 100 then includes a step 4 of controlling a power demand of the heating and / or cooling device, based on a forecast from step 3 to predict a power demand corresponding to a restart period in order to optimally integrate it into a building's electrical consumption mix.

[0073] In other words, step 3 allows for the prediction of the potential triggering of a restart phase. During step 4, the method 100 allows for the control of the triggering of the restart phase, in order to optimally integrate the power demand of the restart phase with other electrical power demands occurring at the same time.

[0074] Thus, for example, in the case of a building containing an electric oven, a heat pump, and an electric vehicle charging station, if these three devices simultaneously draw power, it is possible to trip the building's electricity meter (if the subscribed electricity supply is insufficient). Controlling step 4 allows, for example, triggering a restart phase earlier or later than planned, depending on other power demands occurring. Furthermore, controlling step 4 also allows shifting the power demand of the restart phase based on tariff constraints, in order to reduce the cost of the electricity consumed during the restart phase.

[0075] Computer program product

[0076] According to another aspect, a computer program product is proposed comprising program code instructions for executing the piloting method 100.

[0077] Storage medium

[0078] According to another aspect, a non-transient storage medium is proposed on which is stored a computer program comprising program code instructions to execute the control method 100, when said instructions are read from said non-transient storage medium and executed by a processor.

[0079] Control device

[0080] According to another aspect, a control device is proposed comprising electronic circuitry (computer system 200) adapted to implement a process 100.

[0081] As schematically shown in [Fig.2], the computer system 200 may include, connected by a communication bus 210: a processor 201; a random access memory 202; a read-only memory 203, for example of type ROM (“Read Only Memory”) or EEPROM (“Electrically-Erasable Programmable Read Only Memory”); a storage unit 204, such as a hard disk drive (HDD) or a storage media reader, such as an SD card reader (“Secure Digital”); and an input / output interface manager 205.

[0082] The processor 201 is capable of executing instructions loaded into RAM 202 from ROM 203, external memory, a storage medium (such as an SD card), or a communication network. When the computer system 200 is powered on, the processor 201 is capable of reading instructions from RAM 202 and executing them. These instructions form a computer program enabling the processor 201 to implement process 100.

[0083] All or part of the process 100 can thus be implemented in software form by executing a set of instructions by a programmable machine, for example a DSP (Digital Signal Processor) or a microcontroller, or be implemented in hardware form by a dedicated machine or component, for example an FPGA (Field Programmable Gate Array) or ASIC (Application-Specific Integrated Circuit). Generally, the computer system 200 includes electronic circuitry adapted and configured to implement, in software and / or hardware form, the process 100 in relation to the computer system 200 in question.

[0084] In a particularly advantageous manner, the execution of process 100 requires few resources in terms of computing power or memory. Thus, process 100 can be executed by a device 200 with limited capabilities and low energy consumption, and not necessarily requiring the use of remote servers.

Claims

Demands

1. A method (100) for controlling a power demand of a heating and / or cooling device in a room of a building, the method (100) being implemented by a control device comprising electronic circuitry (200) adapted to implement the method, the control method (100) being characterized in that it comprises at least the following steps: - (Step 1) Acquiring, for a predetermined duration, temperature data including an outside temperature of the room, an inside temperature of the room and a setpoint temperature of the heating and / or cooling device, and data on the electrical power consumed by the heating and / or cooling device; - (Step 2) Modeling an electrical power demand corresponding to a restart phase of the heating and / or cooling device;- (Step 3) Predict the electrical power demand induced by the heating and / or cooling system; - (Step 4) Control the heating and / or cooling system, based on a prediction from step 3, to optimally integrate it into a building's electrical consumption mix.

2. Method (100) according to claim 1, wherein the modeling step 2 comprises establishing a simplified model of a restart phase.

3. A method (100) according to claim 2, wherein the duration model is established as a function of an internal temperature difference between the acquired internal temperature and a maximum acquired internal temperature, such that: dTint = max(r.^ / g relaunch^ - Tint(t = 0)

4. A method (100) according to any one of claims 2 or 3, wherein the duration model is established as a function of a difference between the indoor temperature and the outdoor temperature, both acquired at the same instant, such that AT(( = 0)=T;„(r = 0)-T„ / / = 0)

5. A method (100) according to claims 3 and 4 in combination, wherein the duration model is established as a function of the internal temperature deviation and the temperature difference, such that Duration = / (dTint, AT (7 = 0))

6. A method (100) according to claim 5, wherein the modeling step 2 comprises calculating an average temperature difference between the inside and outside over the duration of a restart phase, such that: AT = TEXt(to, to + duration) - TInt(to, to+duration)

7. Method (100) according to claim 6, wherein the modeling step 2 comprises determining a maximum power used according to Elec! ■ / and determining an electrical energy consumed according to Eel„ = hidTM, ÂT)

8. A method (100) according to any one of the preceding claims, wherein the forecasting step 3 comprises integrating forecast data into the model, such that a setpoint temperature is used instead of a maximum indoor temperature and / or a forecast outdoor temperature is used instead of an outdoor temperature.

9. A device characterized in that it comprises electronic circuitry (200) for implementing a method comprising at least the following steps: - (Step 1) Acquiring, for a predetermined duration, temperature data including an outside room temperature, an inside room temperature and a setpoint temperature of the heating and / or cooling device, and data on the electrical power consumed by the heating and / or cooling device; - (Step 2) Modeling an electrical power demand corresponding to a restart phase of the heating and / or cooling device; - (Step 3) Predicting the electrical power demand induced by the heating and / or cooling device; - (Step 4) Control the heating and / or cooling system, based on a forecast from step 3, to optimally integrate it into a building's electrical consumption mix.

10. Product computer program comprising program code instructions to perform the process (100) according to any one of claims 1 to 8.

11. Non-transient storage medium on which is stored a computer program comprising program code instructions to execute the method (100) according to any one of claims 1 to 8, when said instructions are read from said non-transient storage medium and executed by a processor.

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