WO2022096780A1

WO2022096780A1 - Controlling heating of a house

Info

Publication number: WO2022096780A1
Application number: PCT/FI2021/050714
Authority: WO
Inventors: Simon HOLMBACKA
Original assignee: Elisa Oyj
Priority date: 2020-11-06
Filing date: 2021-10-26
Publication date: 2022-05-12
Also published as: FI20206114A1

Abstract

A computer implemented method for controlling heating of a house so that cost of consumed electricity is minimized while at the same time ensuring that temperature inside the house is not adversely affected, wherein the cost is varied by spot pricing (401) based on demand where during high demand periods the prices are higher, and wherein the heating periods are distributed over a first time period (e.g. 24h) such, that the costs are minimized. A heating schedule for the duration of a first time period is defined by: receiving (302) information about number m of heating periods needed during the first time period; determining (303) a set of suggested heating schedules (405,505) for the duration of the first time period, wherein each individual suggested heating schedule (405,505) of the set is determined by automatically distributing the m heating periods to the first time period; selecting (305) such suggested heating schedules (405,505) that fulfil predefined criteria concerning placement of the heating periods within the first time period; calculating (305) associated cost for the selected heating schedules; and outputting (306) at least the heating schedule (405,505) associated with the lowest cost to be used for controlling the heating.

Description

CONTROLLING HEATING OF A HOUSE

TECHNICAL FIELD

The present application generally relates to controlling heating of houses.

BACKGROUND

This section illustrates useful background information without admission of any technique described herein representative of the state of the art.

In electrically heated houses or other properties, heating is a major source of electricity consumption. In general, it is possible to turn heating on or off in order to control the heating. The temperature inside the house is then dependent on how long time periods the heating is on.

Load on the electric grid varies depending on timing of the heating periods. In general, it is desirable to balance load in the electric grid so that high consumption peaks are reduced.

In spot pricing of electricity, the supply and demand in the market influence the electricity price. For example in Nordic countries, Nord Pool provides spot pricing of electricity. The pricing may change on half-hour basis or on hourly basis. If spot pricing is applied on electricity used for heating, the cost of heating varies depending on timing of the heating periods. Therefore it is desirable to have the heating on during lower pricing periods and off during higher pricing periods if possible.

The challenge is to control and schedule the heating so that cost of consumed electricity is minimized while at the same time ensuring that temperature inside the house is not adversely affected.

SUMMARY

The appended claims define the scope of protection. Any examples and technical descriptions of apparatuses, products and/or methods in the description and/or drawings not covered by the claims are presented not as embodiments of the invention but as background art or examples useful for understanding the invention. According to a first example aspect there is provided a computer implemented method for controlling heating of a house. The method comprises defining a heating schedule for the duration of a first time period by: receiving information about number m of heating periods needed during the first time period; determining a set of suggested heating schedules for the duration of the first time period, wherein each individual suggested heating schedule of the set is determined by automatically distributing the m heating periods to the first time period; selecting such suggested heating schedules that fulfil predefined criteria concerning placement of the heating periods within the first time period; calculating associated cost for the selected heating schedules; and outputting at least the heating schedule associated with the lowest cost to be used for controlling the heating.

In some example embodiments, the first time period is 12-24 hours.

In some example embodiments, the predefined criteria concerning placement of the heating periods within the first time period define a requirement of including the m heating periods and a requirement of not exceeding a maximum gap between consecutive heating periods.

In some example embodiments, the maximum gap is 0.5-5 hours.

In some example embodiments, the method comprises dividing the first time period into a plurality of second time periods; and the automatic determination of each individual suggested heating schedule comprises:

- automatically distributing the m heating periods to the second time periods so that each second time period receives substantially equal number of heating periods; and

- constructing the individual suggested heating schedule based on combining the partially overlapping second time periods and respective heating periods distributed within the second time periods.

In some example embodiments, the second time period is 3-8 hours. In some example embodiments, the second time periods are at least partially overlapping.

In some example embodiments, the second time periods are multiples of a third time period. In some example embodiments, the duration of a heating period is equal to or a multiple of the third time period. In some example embodiments, consecutive second time periods overlap at least for the duration of the third time period.

In some example embodiments, the third time period is 15 minutes - 1 hour.

In some example embodiments, the method is periodically repeated.

In some example embodiments, artificial intelligence tools are used for the automatic distribution of the m heating periods and the selected heating schedules and/or the heating schedule associated with the lowest cost are used for teaching the artificial intelligence tools.

According to a second example aspect of the present invention, there is provided an apparatus comprising a processing section and a memory section including computer program code; the computer program code configured to, with the processing section, cause the apparatus to perform the method of the first aspect or any related embodiment.

According to a third example aspect of the present invention, there is provided a computer program comprising computer executable program code which when executed by a processor causes an apparatus to perform the method of the first aspect or any related embodiment.

According to a fourth example aspect there is provided a computer program product comprising a non-transitory computer readable medium having the computer program of the third example aspect stored thereon.

According to a fifth example aspect there is provided an apparatus comprising means for performing the method of the first aspect or any related embodiment.

Any foregoing memory medium may comprise a digital data storage such as a data disc or diskette, optical storage, magnetic storage, holographic storage, opto- magnetic storage, phase-change memory, resistive random access memory, magnetic random access memory, solid-electrolyte memory, ferroelectric random access memory, organic memory or polymer memory. The memory medium may be formed into a device without other substantial functions than storing memory or it may be formed as part of a device with other functions, including but not limited to a memory of a computer, a chip set, and a sub assembly of an electronic device.

Different non-binding example aspects and embodiments have been illustrated in the foregoing. The embodiments in the foregoing are used merely to explain selected aspects or steps that may be utilized in different implementations. Some embodiments may be presented only with reference to certain example aspects. It should be appreciated that corresponding embodiments may apply to other example aspects as well.

BRIEF DESCRIPTION OF THE FIGURES

Some example embodiments will be described with reference to the accompanying figures, in which:

Fig. 1 schematically shows an example scenario according to an example embodiment;

Fig. 2 shows a block diagram of an apparatus according to an example embodiment; and

Fig. 3 shows a flow diagram illustrating example methods according to certain embodiments;

Figs. 4-5 show graphs illustrating some example cases; and

Figs. 6-10 illustrate some implementation examples.

DETAILED DESCRIPTION

In the following description, like reference signs denote like elements or steps.

Various embodiments of present disclosure provide mechanisms to control and schedule heating of a house or other property so that cost of consumed electricity is minimized while at the same time ensuring that temperature inside the house is not adversely affected. As the spot pricing is based on demand and during high demand periods the prices are higher, scheduling the heating periods to lower cost periods has the effect of balancing load in the electricity grid. Therefore minimizing the cost of consumed electricity provides also the effect of balancing load in electric grid. That is, the cost of electricity can be used as a measure of load in the electric grid. Thereby, a technical problem solved by present embodiments is can be defined to be balancing load in electric grid while at the same time ensuring that temperature inside the house is not adversely affected. Le. the aim is to balance load without deteriorating user experience. As spot pricing is based on demand, minimizing cost of consumed electricity and balancing load go hand in hand and thereby solving the technical problem of balancing load in electric grid equally provides minimizing cost and vice versa.

Fig. 1 schematically shows an example scenario according to an embodiment. The scenario shows a house 101 comprising a device 102 configured to control heating of the house. The device 102 is operable to turn heating of the house on or off as scheduled. The device 102 may be for example a card or a box installed in a main fuse box of the house 101 .

Further, the scenario shows an automation system 111. The automation system 111 is configured to implement at least some example embodiments of present disclosure and to determine heating schedule for use by the device 102 to control heating of the house 101 . The device 102 is operable to interact with the automation system 111 to receive the heating schedule. In an embodiment, the automation system is a cloud service. In another embodiment, the automation system is at least partially implemented in the device 102. The automation system 111 is operable to obtain data from external sources, such as databases or other storage devices, and/or to receive input from a user.

Fig. 2 shows a block diagram of an apparatus 20 according to an embodiment. The apparatus 20 is for example a general-purpose computer or server or some other electronic data processing apparatus. The apparatus 20 can be used for implementing at least some embodiments of the invention. That is, with suitable configuration the apparatus 20 is suited for operating for example as the automation system 111 of foregoing disclosure.

The apparatus 20 comprises a communication interface 25; a processor 21 ; a user interface 24; and a memory 22. The apparatus 20 further comprises software 23 stored in the memory 22 and operable to be loaded into and executed in the processor 21. The software 23 may comprise one or more software modules and can be in the form of a computer program product.

The processor 21 may comprise a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a graphics processing unit, or the like. Fig. 2 shows one processor 21 , but the apparatus 20 may comprise a plurality of processors.

The user interface 24 is configured for providing interaction with a user of the apparatus. Additionally or alternatively, the user interaction may be implemented through the communication interface 25. The user interface 24 may comprise a circuitry for receiving input from a user of the apparatus 20, e.g., via a keyboard, graphical user interface shown on the display of the apparatus 20, speech recognition circuitry, or an accessory device, such as a headset, and for providing output to the user via, e.g., a graphical user interface or a loudspeaker.

The memory 22 may comprise for example a non-volatile or a volatile memory, such as a read-only memory (ROM), a programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), a random-access memory (RAM), a flash memory, a data disk, an optical storage, a magnetic storage, a smart card, or the like. The apparatus 20 may comprise a plurality of memories. The memory 22 may serve the sole purpose of storing data, or be constructed as a part of an apparatus 20 serving other purposes, such as processing data.

The communication interface 25 may comprise communication modules that implement data transmission to and from the apparatus 20. The communication modules may comprise a wireless or a wired interface module(s) or both. The wireless interface may comprise such as a WLAN, Bluetooth, infrared (IR), radio frequency identification (RF ID), GSM/GPRS, CDMA, WCDMA, LTE (Long Term Evolution) or 5G radio module. The wired interface may comprise such as Ethernet or universal serial bus (USB), for example. The communication interface 25 may support one or more different communication technologies. The apparatus 20 may additionally or alternatively comprise more than one of the communication interfaces 25.

A skilled person appreciates that in addition to the elements shown in Fig. 2, the apparatus 20 may comprise other elements, such as displays, as well as additional circuitry such as memory chips, application-specific integrated circuits (ASIC), other processing circuitry for specific purposes and the like. Further, it is noted that only one apparatus is shown in Fig. 2, but the embodiments of the invention may equally be implemented in a cluster of shown apparatuses.

Fig. 3 shows a flow diagram illustrating example methods according to certain embodiments. The methods may be implemented in the automation system 111 of Fig. 1 and/or in the apparatus 20 of Fig. 2. The methods are implemented in a computer and do not require human interaction unless otherwise expressly stated. It is to be noted that the methods may however provide output that may be further processed by humans and/or the methods may require user input to start. Different phases shown in the flow diagrams may be combined with each other and the order of phases may be changed except where otherwise explicitly defined. Furthermore, it is to be noted that performing all phases of the flow diagram is not mandatory.

The method of Fig. 3 provides controlling heating of a house. The method of Fig. 3 comprises the following phases:

301 : A process of defining a heating schedule is started. Heating schedule is defined for the duration of a first time period and the heating schedule comprises time periods during which the heating is on and time periods during which the heating is off. The duration of the first time period may be for example 12-24 hours. Use of a 24 hour time period provides the benefit of being able to take into account price variations over night and day.

302: Information is received about number m of heating periods needed during the first time period. For the sake of clarity it is noted that during heating periods, the heating is intended to be on. During other time periods the heating may be turned off. The information about the required m heating periods may be obtained from a database or some other storage, given as an input by a user or otherwise received. The number m may be automatically generated for example based on outside temperature. Also desired inside temperature may have an effect on the number m. The duration of one heating period may vary. Byway of example, one heating period may be for example 15 minutes - 1 hour. As an illustrative example, the number m of heating periods may correspond to 1 -12 hours of heating over a first time period of 24 hours. If the duration of a heating period is 1 hour, the number m is 1 -12 in this example. Likewise, if the duration of the heating period is 0.5 hour, the number m is 2-24 in this example.

303: A set of suggested heating schedules is determined by automatically distributing the m heating periods within the first time period. That is, a plurality of suggested heating schedules are determined by differently distributed heating periods. At minimum, the set comprises two suggested heating schedules, but clearly there may be more.

In an embodiment, the first time period is divided into a plurality of second time periods for the purpose of distributing the m heating periods within the first time period. The duration of the second time period may be for example 3-8 hours. The m heating periods are then automatically distributed to the second time periods so that each second time period receives substantially equal number of heating periods, and an individual suggested heating schedule is constructed based on combining the partially overlapping second time periods and respective heating periods distributed within the second time periods.

By dividing the first time period to shorter second time periods and by distributing substantially equal number of heating periods to each second time period one achieves the effect of automatically reducing the gaps that can possibly form between consecutive heating periods and thereby the state space of possible schedules is automatically reduced without needing to implement exhaustive search of every possible combination.

In an embodiment the second time periods are at least partially overlapping. That is, consecutive second time periods overlap for certain amount of time. In an embodiment, consecutive second time periods may overlap for example 1 hour. Clearly, the duration of the overlapping period may vary depending on the duration of the first and second time periods. At least for a 24 hour first time period, an overlap of 1 hour suits well. Having significantly larger overlap might deteriorate efficiency by increasing the number of possible combinations.

In an embodiment, the distribution of the m heating periods is implemented using artificial intelligence tools. Use of overlapping second time periods may improve performance of the artificial intelligence tools by making the artificial intelligence tools to efficiently learn, which combinations to try. Non-overlapping second time periods may provide similar effect, but use of overlapping periods may provide more efficient learning.

304: Such suggested heating schedules that fulfil predefined criteria are selected. The predefined criteria concerns for example placement of the heating periods within the first time period. In an embodiment, the predefined criteria define a requirement of including the m heating periods and a requirement of not exceeding a maximum gap between consecutive heating periods. Still further, it may be required that exactly the number m heating periods are included in the first time period. In an embodiment, it is defined that maximum gap between consecutive heating periods is 0.5-5 hours. Minimum gap is naturally 0 hours or no gap at all. Information about the maximum gap may be automatically defined or obtained from a database or some other storage or given as an input. The maximum gap may be dependent on a comfort level setting defined for the heating of the house.

305: Associated cost is calculated for the suggested heating schedules. The cost is calculated based on spot price information obtained from electricity market. For example in Nordic countries, the spot price information is obtained from Nord Pool. The spot prices that are obtained may be for the next day, whereby also the heating schedule is considered to be for the next day. It is to be noted that the duration of one heating period advantageously matches the granularity of the spot pricing. That is, if spot pricing is provided on hourly basis, it is advantageous to use heating period length of one hour to simplify the considerations. Nevertheless, other lengths and especially heating period lengths that are shorter than the granularity of the spot pricing are applicable, too.

In an embodiment, the cost calculation may take into account other factors in addition to the spot prices. There may be for example separate transfer cost for transferring the electricity in addition to the spot price. Such transfer cost may be taken into account. Additionally or alternatively, applicable taxes may be taken into account. By taking into account the transfer cost and taxes in addition to the spot pricing, all costs that are involved are taken into account. In this way, it is possible to provide a full picture of the cost of the electricity. For example transfer cost may have different pricing during day time and during night time. In such example, the transfer cost clearly has an effect on the evaluation and cost comparison of the heating schedules.

306: At least the heating schedule associated with the lowest cost is output. This heating schedule may then be used for controlling the heating.

In an embodiment, the cost is used as a measure of load in the electric grid and the purpose of calculating and minimizing the cost is finding a heating schedule that provides maximal load balancing effect while at the same time ensuring the heating of the house remains at acceptable level.

In an example embodiment, granularity of the different time periods is interlinked so that the second time periods are multiples of a third time period, the duration of a heating period is equal to or a multiple of the third time period, and consecutive second time periods overlap at least for the duration of the third time period. The third time period may be for example 15 minutes - 1 hour. This will enable automatic placement of the heating periods within the first and second time periods.

In an example embodiment, the method of Fig. 3 is periodically repeated. For example, if cost of electricity for the following day is available on the day before, the scheduling method may be repeated daily in order to optimize daily cost of consumed electricity. Consequently an optimization effect is achieved in the load of the electric grid.

In an example embodiment, artificial intelligence tools are used for the automatic distribution of the m heating periods. The artificial intelligence tools may comprise for example machine learning algorithms, such as differential evolution algorithm. The selected heating schedules and/or the heating schedule associated with the lowest cost can be used for teaching the artificial intelligence tools. In this way, the artificial intelligence tools will learn to optimize the heating schedules.

Figs. 4-5 show graphs illustrating some example cases.

Fig. 4 shows an example of spot price of electricity 401 and a heating schedule 405 over a time period of 24 hours. In the shown example, 12 hours of heating is required. The heating is on every other hour and off every other hour. Such schedule ensures that temperature inside the house is not adversely affected, but cost of consumed electricity is not optimized. Placing all heating hours to cheaper night hours would reduce the costs, but such schedule is likely to adversely affect the temperature inside the house as there would be no heating during more expensive day time hours.

Fig. 5 shows the same spot price of electricity 401 as Fig. 4 and a heating schedule 505 according to an embodiment of the invention over a time period of 24 hours. In the shown example, 12 hours of heating is required and a maximum gap of 2 hours is defined. It can be seen that the maximum gap of 2 hours provides that the heating is regularly on, which ensures that temperature inside the house is not adversely affected. However, the periods during which the heating is on tend to coincide with periods of lower electricity price and the periods during which the heating is off tend to coincide with periods of higher electricity price and/or local peaks in electricity price. In this way, the overall cost is reduced.

Figs. 6-10 illustrate some implementation examples.

Fig. 6 illustrates possible placement of heating periods within the first time period in an example implementation. Fig. 6 shows a first time period of 24 hours, which is divided into 6 second time periods Zonel - Zone6. The length of the second time periods is 4-5 hours. Granularity of the schedule (i.e. the third time period) is 1 hour. 12 hours of heating is required and a maximum gap of 2 hours is defined. Adjacent second time periods overlap for 1 hour period. 12 markers denoted by letter m are used for the 12 heating periods. The 12 markers are evenly distributed to the second time periods Zonel - Zone6 whereby each second time period receives 2 markers.

Fig. 6 shows a valid schedule that fulfils the requirement of not exceeding a maximum gap of 2 hours between consecutive heating periods. The example of Fig. 6 can be transformed into binary numbers representing configuration of the markers m in the second time periods Zonel - Zone6. In the binary representation, 1 may represent a heating period, while 0 represents no heating, or vice versa. The binary numbers of the second time periods Zonel - Zone6 are then combined to construct the suggested heating schedule for the first time periods of 24 hours.

Fig. 7 shows in binary representation an example where the first time period is 24 hours, the second time period is 4-5 hours, the number m of required heating periods is 12, the maximum gap is 2 hours and the granularity of the schedule is 1 hour. The first time period is divided into second time periods 701 -706, i.e. the number of the second time periods is 6, and heating periods are distributed to the second time periods. In phase 707, the second time periods and respective heating periods are combined to form a suggested heating schedule 708. The combination of the overlapping time periods is performed using OR operator, whereby two zeros yield a zero, one and zero yield one, and two ones yield one.

In phase 709, it is checked whether the suggested heating schedule 708 fulfils predefined criteria. In the example of Fig. 7 the suggested heating schedule 708 fails to fulfil the predefined criteria as there are only 11 heating periods and one of the gaps between consecutive heating periods is 3 hours which exceeds the limit of 2 hours.

Fig. 8 shows in binary representation an example where the first time period is 24 hours, the second time period is 4-5 hours, the number m of required heating periods is 12, the maximum gap is 2 hours and the granularity of the schedule is 1 hour.

The first time period is divided into second time periods 801 -806, i.e. the number of the second time periods is 6, and heating periods are distributed to the second time periods. In phase 707, the second time periods and respective heating periods are combined to form a suggested heating schedule 808. The combination of the overlapping time periods is performed using OR operator, whereby two zeros yield a zero, one and zero yield one, and two ones yield one.

In phase 709, it is checked whether the suggested heating schedule 808 fulfils predefined criteria. In the example of Fig. 8 the suggested heating schedule 808 fulfils the predefined criteria as there are exactly 12 heating periods as required and none of the gaps between consecutive heating periods exceeds the limit of 2 hours.

Fig. 9 shows in binary representation an example where the first time period is 24 hours, the second time period is 4-5 hours, the number m of required heating periods is 6, the maximum gap is 4 hours and the granularity of the schedule is 1 hour.

The first time period is divided into second time periods 901 -906, i.e. the number of the second time periods is 6, and heating periods are distributed to the second time periods. In phase 707, the second time periods and respective heating periods are combined to form a suggested heating schedule 908. The combination of the overlapping time periods is performed using OR operator, whereby two zeros yield a zero, one and zero yield one, and two ones yield one.

In phase 709, it is checked whether the suggested heating schedule 908 fulfils predefined criteria. In the example of Fig. 9 the suggested heating schedule 908 fulfils the predefined criteria as there are exactly 6 heating periods as required and none of the gaps between consecutive heating periods exceeds the limit of 4 hours.

Fig. 10 shows in binary representation an example where the first time period is 24 hours, the second time period is 8-9 hours, the number m of required heating periods is 8, the maximum gap is 4 hours and the granularity of the schedule is 1 hour.

The first time period is divided into second time periods 1001 -1003, i.e. the number of the second time periods is 3, and heating periods are distributed to the second time periods. In phase 707, the second time periods and respective heating periods are combined to form a suggested heating schedule 1008. The combination of the overlapping time periods is performed using OR operator, whereby two zeros yield a zero, one and zero yield one, and two ones yield one.

In phase 709, it is checked whether the suggested heating schedule 1008 fulfils predefined criteria. In the example of Fig. 10 the suggested heating schedule 1008 fulfils the predefined criteria as there are exactly 8 heating periods as required and none of the gaps between consecutive heating periods exceeds the limit of 4 hours.

It is to be noted that Figs. 6-10 provide non-exclusively some examples. Clearly other examples are equally possible within the scope of present disclosure.

Without in any way limiting the scope, interpretation, or application of the appended claims, a technical effect of one or more of the example embodiments disclosed herein is an automated scheduling of heating periods that ensures sufficient heating while reducing heating cost. By scheduling the heating for time periods with lower electricity price, an effect of balancing load in electric grid may be achieved. This may provide the effect of reducing peak consumption in electric grid. In this way, improved controlling of heating is achieved.

A further technical effect is that at least a near optimal heating schedule is obtained in manageable time frame as state space to search the heating schedule can be reduced by various embodiments. Various embodiments enable faster analysis than for example rule based implementation that would go through all possible combinations. That is, need for an exhaustive search among all possible combinations can be avoided or reduced.

If desired, the different functions discussed herein may be performed in a different order and/or concurrently with each other. Furthermore, if desired, one or more of the before-described functions may be optional or may be combined

Various embodiments have been presented. It should be appreciated that in this document, words comprise, include and contain are each used as open-ended expressions with no intended exclusivity.

The foregoing description has provided by way of non-limiting examples of particular implementations and embodiments a full and informative description of the best mode presently contemplated by the inventors for carrying out the invention. It is however clear to a person skilled in the art that the invention is not restricted to details of the embodiments presented in the foregoing, but that it can be implemented in other embodiments using equivalent means or in different combinations of embodiments without deviating from the characteristics of the invention.

Furthermore, some of the features of the afore-disclosed example embodiments may be used to advantage without the corresponding use of other features. As such, the foregoing description shall be considered as merely illustrative of the principles of the present invention, and not in limitation thereof. Hence, the scope of the invention is only restricted by the appended patent claims.

Claims

1. A computer implemented method for controlling heating of a house, the method comprising defining a heating schedule for the duration of a first time period by: receiving (302) information about number m of heating periods needed during the first time period; determining (303) a set of suggested heating schedules for the duration of the first time period, wherein each individual suggested heating schedule of the set is determined by automatically distributing the m heating periods to the first time period; selecting (305) such suggested heating schedules that fulfil predefined criteria concerning placement of the heating periods within the first time period; calculating (305) associated cost for the selected heating schedules; and outputting (306) at least the heating schedule associated with the lowest cost to be used for controlling the heating.

2. The method of claim 1 , wherein the cost is used as a measure of load in electric grid.

3. The method of any preceding claim, wherein the heating schedule associated with the lowest cost is used for balancing load in electric grid.

4. The method of any preceding claim, wherein the first time period is 12-24 hours.

5. The method of any preceding claim, wherein the predefined criteria concerning placement of the heating periods within the first time period define a requirement of including the m heating periods and a requirement of not exceeding a maximum gap between consecutive heating periods.

6. The method of claim 5, wherein the maximum gap is 0.5-5 hours.

7. The method of any preceding claim, wherein the method comprises dividing the first time period into a plurality of second time periods (701-707, 801 -806, 901-906, 1001-1003); and wherein the automatic determination of each individual suggested heating schedule comprises:

8. The method of claim 7, wherein the second time period is 3-8 hours.

9. The method of claim 7 or 8, wherein the second time periods are partially overlapping.

10. The method of any preceding claim, wherein the second time periods are multiples of a third time period, wherein the duration of a heating period is equal to or a multiple of the third time period, and wherein consecutive second time periods overlap at least for the duration of the third time period.

11 . The method of claim 10, wherein the third time period is 15 minutes - 1 hour. 17

12. The method of any preceding claim, further comprising periodically repeating the method.

13. The method of any preceding claim, wherein artificial intelligence tools are used for the automatic distribution of the m heating periods and wherein the selected heating schedules and/or the heating schedule associated with the lowest cost are used for teaching the artificial intelligence tools.

14. An apparatus (20, 111 ) comprising a processing section (21), and a memory section (22) including computer program code; the computer program code configured to, with the processing section, cause the apparatus to perform the method of any one of claims 1 -13.

15. A computer program comprising computer executable program code (23) which when executed by a processor causes an apparatus to perform the method of any one of claims 1 -13.