GB2562118A - Energy Consumption estimation - Google Patents

Energy Consumption estimation Download PDF

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Publication number
GB2562118A
GB2562118A GB1707262.0A GB201707262A GB2562118A GB 2562118 A GB2562118 A GB 2562118A GB 201707262 A GB201707262 A GB 201707262A GB 2562118 A GB2562118 A GB 2562118A
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Prior art keywords
temperature
control system
energy consumption
temperature control
schedule
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GB201707262D0 (en
Inventor
Maciol Ryszard
Hamouz Miroslav
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GB GAS HOLDINGS Ltd
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GB GAS HOLDINGS Ltd
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Priority to GB1707262.0A priority Critical patent/GB2562118A/en
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Publication of GB2562118A publication Critical patent/GB2562118A/en
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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/04Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00Commerce, e.g. shopping or e-commerce
    • G06Q30/02Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
    • G06Q30/0283Price estimation or determination
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06Electricity, gas or water supply

Abstract

Method of estimating energy consumption associated with a temperature control system arranged to control the temperature in an environment by receiving a control schedule 308 and using it to simulate operation of the temperature control system 300; and using the simulation to determine, and output, an energy consumption value 310. Also disclosed is a method of receiving data relating to a temperature response of the environment 306 to activity of the temperature control system; determining parameters of a thermal mode 304; receiving a temperature control schedule 308; and calculating estimated energy consumption data corresponding to operation for the temperature control system in accordance with the control schedule, based on the received control schedule 308 and the thermal model 304. The temperature control system may be a domestic heating system involving activation of a heater device such as a boiler. The thermal model may be based on past performance data.

Description

Energy consumption estimation

The present invention relates to systems and methods for estimating energy consumption of an environmental control system, for example a domestic heating system.

Domestic heating systems are often controlled in accordance with a heating schedule programmed by a user. This allows the user to configure the heating system to provide the desired comfort levels at various times. However, energy efficiency is becoming increasingly important in view of environmental considerations as well as the rising cost of energy. To reduce energy consumption, a user may vary the schedule, e.g. to reduce the total durations for which the heating system is on. However, the energy savings that might be realised by making such changes are difficult to predict, since the energy consumption of the system depends on many factors, such as the performance of the system, the design and layout of the environment being heated, insulation, etc. This makes it very difficult for users to select an appropriate heating schedule that will achieve reasonable comfort levels whilst meeting energy consumption requirements.

The present invention seeks to address some of these issues. Accordingly, in a first aspect of the invention, there is provided a method of estimating energy consumption associated with a temperature control system arranged to control the temperature in an environment, the method comprising: receiving a control schedule for the temperature control system; simulating operation of the temperature control system in accordance with the control schedule; determining, based on the simulation, an energy consumption value indicative of energy consumption associated with the temperature control system when controlled in accordance with the control schedule; and outputting the energy consumption value.

The environment may be a domestic dwelling or property, such as a house or apartment (or part thereof), but may also be any other environment in which climate control is performed by a temperature control system.

The simulating step preferably comprises simulating activation of the temperature control system to control the temperature in the environment; preferably wherein activation of the temperature control system corresponds to activation of a temperature control device (e.g. a boiler) arranged to alter the temperature in the environment, preferably in response to an activation signal. Thus, the simulating step may simulate activation of a boiler or other temperature control device, for example by calculating when and/or for how long such a device is turned on/off in order to achieve a required temperature in the environment.

Preferably, the method comprises simulating operation of the temperature control system based on a thermal model for the environment. The thermal model preferably specifies a temperature response of the environment to activity (and optionally inactivity) of the temperature control system. For example, the thermal model may specify temperature response of the environment when the temperature control device is activated (e.g. firing of a boiler) and/or when the temperature control device is deactivated (e.g. boiler off).

The thermal model preferably comprises a function specifying a temperature of the environment in dependence on one or more model parameters (where the function may be defined as one or more mathematical formulae, a computational algorithm or in any other way).

The one or more model parameters may comprise one or more of: an external temperature, an offset temperature, and a passive cooling time constant. The thermal model may comprises a function of the form: Tint(t) = (Toffset + T)(l - e_t/T) + Tint(0)e_t/T, where Tint(t) is the interior temperature of the environment at time t, T is a temperature value representative of the exterior temperature, optionally fixed for the simulation period, Toffset is an offset from the external temperature representing an equilibrium temperature when the temperature control system is in a permanent ON or OFF state, Tint(0) is the internal temperature at the start of the simulation and τ is a passive cooling time constant.

Alternatively, the one or more model parameters may comprise one or more of: an external temperature, a cooling time constant, a heating time constant, and one or more parameters representing an initial condition. The thermal model may comprise a function of the form: Tint(t) = T + Ae_tAi + Be_t/T2, where Tint(t) is the interior temperature of the environment at time t, τ± and τ2 are (respectively) heating and cooling time constants and A and B are model parameters representing an initial condition (e.g. based on the internal temperature at the start of the simulation period).

The method may comprise selecting one or more (or all) model parameters based on one or more properties of the environment, the properties preferably comprising one or more of: geographical location, size, number of rooms or bedrooms, number of occupants, property type, property age, property energy performance rating. The properties may be stored as a building profile for the environment.

The method may alternatively or additionally comprise determining one or more (or all) model parameters based on past performance data relating to the temperature control system.

More generally, the method may comprise generating the thermal model based on past performance data relating to the temperature control system, the past performance data optionally including data received from the temperature control system. The past performance data may comprise one or more of: a control schedule for a past time period; data specifying activations of the temperature control system during a past time period, the data optionally comprising or derived from a control signal used for activating the temperature control system to perform temperature control; interior temperature data relating to an interior temperature of the controlled environment during a past time period; and exterior temperature data relating to a temperature external to the controlled environment during a past time period.

Generating the thermal model may include learning model parameters based on past performance data using a machine learning, model fitting (e.g. using approximation and/or regression) or similar approach.

The control schedule preferably specifies one or more active periods for the temperature control system and optionally, for one or more (or each) of the active periods, a target temperature value. Simulating operation of the temperature control system may then comprise simulating operation of the temperature control system to attain a specified target temperature value in the environment during a specified active period defined in the control schedule.

Simulating the operation of the temperature control system may comprise calculating temperature changes in the environment in response to operation in accordance with the control schedule. For example, the simulation may calculate the internal temperature at discrete time intervals. The simulated temperature may then in turn influence the simulated operation of the temperature control system.

Simulating the operation of the temperature control system preferably comprises generating information defining a plurality of activation periods of the temperature control system, the information optionally comprising a simulated control signal for activating the temperature control system (e.g. a simulated call-for-heat signal may be generated). Generating the information may comprise simulating operation of a temperature control algorithm, the simulation of the temperature control algorithm generating a control signal based on a modelled interior temperature of the environment and the control schedule, the modelled interior temperature preferably modified during a simulation period based on the generated control signal.

The method may alternatively or additionally comprise calculating an activation time of the temperature control system, the activation time indicating a total time that the temperature control system is activated to control temperature during a simulated period (e.g. this may be determined based on the determined activation periods/durations and/or based on a simulated control signal).

The energy consumption value is preferably calculated based on the activation periods and/or total activation time, and based on a consumption model, the consumption model preferably specifying an energy consumption rate. The energy consumption model or rate is preferably determined based on data received from the temperature control system, optionally based on activation data of the temperature control system during a predetermined past time period and energy consumption data of the temperature control system for the past time period. The consumption model may comprise a function providing an energy consumption value for a period of time in dependence on a simulated activation time of the temperature control system during the period of time, the model preferably comprising one or more model parameters determined based on past energy consumption data and/or selected based on one or more properties of the temperature control system and/or of the environment. These may include any or all of the properties mentioned above for use in selecting properties of the thermal model. In an alternative approach, a typical energy consumption rate (e.g. based on a boiler type/model) may be used. Instead of merely using a total activation duration (e.g. multiplied by an energy consumption rate), the consumption model may determine consumption based on durations of individual activations (for example, such that energy consumption rates may depend on the activation duration).

In an embodiment, the simulation step may be performed for each of a plurality of control schedules with a respective energy consumption value calculated for each of the control schedules. The method then preferably comprises selecting one of the plurality of control schedules based on the energy consumption values, and outputting the selected control schedule. For example, one of the plurality of control schedules may be selected based on the energy consumption values calculated for the control schedules and based on a target energy consumption value.

This feature may also be provided independently. Accordingly, in a further aspect of the invention (which may be combined with any of the other aspects set out), there is provided a method of estimating energy consumption associated with a temperature control system arranged to control the temperature in an environment, the method comprising: receiving a target energy consumption value; simulating operation of the control system in accordance with a plurality of control schedules to determine estimated energy consumption values for each control schedule; selecting one of the plurality of control schedules, based on the estimated energy consumption values and the target energy consumption value; and outputting the selected control schedule. The simulating step may comprise performing a method as set out above.

In either of the above aspects, the method may include selecting the control schedule having an associated energy consumption value meeting one or more predetermined criteria, the criteria optionally comprising one or more of: the energy consumption value for the selected schedule not exceeding the target energy consumption value; the energy consumption value for the selected schedule being within a threshold distance of the target energy consumption value; and/or selecting the control schedule having an associated energy consumption value closest to the target energy consumption value. The method may comprise generating one or more of the plurality of control schedules, preferably as variations of a/the received control schedule.

In any of the above aspects, the method may comprise transmitting the received or selected control schedule to the temperature control system and/or configuring (preferably automatically) the temperature control system to operate in accordance with the received or selected control schedule.

In any of the described aspects, the temperature control system may be a heating system, preferably wherein activation of the temperature control system to control temperature of the environment comprises activating a heater device, for example a boiler.

In a further aspect of the invention (which may be combined with either of the above aspects), there is provided a method of determining energy consumption associated with a temperature control system arranged to control the temperature in an environment, the method comprising: receiving data from the temperature control system relating to a temperature response of the environment to activity of the temperature control system; determining based on the data one or more parameters of a thermal model characterising the temperature response of the environment; receiving a temperature control schedule for use with the temperature control system; and calculating estimated energy consumption data corresponding to operation of the temperature control system in accordance with the control schedule, based on the received control schedule and the thermal model.

The calculating step is preferably further performed based on a consumption model characterising energy consumption of the temperature control system. One or more parameters of the consumption model may be determined based on past consumption and/or past control data of the temperature control system.

The method in this aspect may further comprise performing a method as set out in any of the above-described aspects.

In a further aspect, the invention provides a system having means (optionally in the form of a processor and associated memory storing software code) for performing any method as set out herein.

The invention further provides a computer readable medium or computer program product comprising software code adapted, when executed on a data processing apparatus or system, to perform any method as set out herein.

Any feature in one aspect of the invention may be applied to other aspects of the invention, in any appropriate combination. In particular, method aspects may be applied to apparatus and computer program aspects, and vice versa.

Furthermore, features implemented in hardware may generally be implemented in software, and vice versa. Any reference to software and hardware features herein should be construed accordingly.

Preferred features of the present invention will now be described, purely by way of example, with reference to the accompanying drawings, in which:-

Figure 1 illustrates a domestic heating system;

Figure 2A illustrates control of a boiler by a boiler control module;

Figure 2B illustrates a control schedule, boiler control signal and temperature response of the heated environment;

Figure 3A illustrates a first energy consumption estimation mode;

Figure 3B illustrates a second energy consumption estimation mode;

Figure 4 illustrates the flow of data in an energy consumption estimation process;

Figure 5 illustrates a process flow for an energy consumption estimation process; and

Figure 6 illustrates a computing device suitable for performing disclosed methods.

Overview

Embodiments of the invention provide a system for estimating the energy consumption resulting from applying a particular heating schedule to a heating system. The heating system may, for example, be a domestic heating system installed in a house, apartment or other residential property. The heating system allows programming of heating schedules via a smart thermostat or other control device. The estimation system uses a predictive model learned on past readings of thermostat target temperatures, internal temperatures and boiler ON/OFF signals and house aggregate energy (gas, electricity) consumption over a period of time.

The estimation period can be in the past (e.g. to estimate what the energy consumption would have been for a particular past period using a particular heating schedule) or in the future (e.g. to estimate energy consumption in a future time period for a particular schedule, assuming a typical weather pattern for that period of the year in the relevant location).

Use of such a predictive model, learned for the particular heating system installation and the particular property, enables the system to advise on the energy consumption and hence also costs of particular schedules. Additionally, the system may provide functionality for proposing a schedule to achieve a particular energy consumption target - for example, given a predefined monetary budget (or total energy consumption target, e.g. in kWh), a schedule may be proposed that meets the monetary or energy budget whilst still achieving the best possible comfort level.

The predictive model is based on a thermal simulator. Given a schedule and external temperature (actual for a past interval or typical/average for a future interval), the simulator simulates what the internal temperature would be in the house over time, and also the periods when the boiler will be firing. The estimated total time the boiler will be firing (e.g. in hours) together with a normalized boiler consumption rate parameter learned from past performance data (e.g. calculated as kWh/h - effectively the average supply boiler power) is then used to predict the total energy needed to heat the particular house according the particular schedule.

Figure 1 illustrates components of an example domestic heating system to which the estimation system may be applied.

The heating system 100 includes a programmable thermostat 102 which connects wirelessly to a boiler control module 104. The boiler control module is connected by a wired connection to central heating I hot water boiler 106. The boiler may, for example, be a conventional gas boiler arranged to provide a supply of heated water to a series of radiators in the user’s home and to a hot water tank for onward supply to hot water taps.

The thermostat 102 and boiler control module 104 are further connected wirelessly to a heating system hub 108. The hub 108 is connected to the user’s home network and/or internet access infrastructure. For example, in a typical configuration the heating system hub 108 is connected via a wired connection to a wireless or wired home router I access point 112, which in turn provides access to the Internet through a modem 114, such as an ADSL or fibre modem. Depending on access technology, router 112 and modem 114 may be combined in a single device or replaced with other access devices appropriate to the access technology.

The heating system hub 108 thus acts as a gateway between the heating system 100 and other local network devices (e.g. a user device 110) as well as the external network. The thermostat 102, boiler control module 104 and hub 108 are preferably connected in a mesh configuration, so that communication between thermostat and receiver may be direct or via the hub.

The thermostat 102 measures ambient temperature using an internal temperature sensor and sends temperature information to the boiler control module 104. Alternatively or additionally, an interior temperature sensor 101 may be provided for measuring the interior temperature of the property heated by the heating system, which may be located remote from the thermostat (e.g. in the same room or a different room). An exterior temperature sensor 103 may further be installed on the exterior of the building to record the exterior temperature. Multiple interior and/or exterior temperature sensors may be provided, for example to obtain temperature measurements in different parts of the property (where there are multiple temperature sensors their measurements may be combined e.g. by averaging).

The boiler control module 104 stores programmed schedules and controls the boiler to switch environmental heating and hot water on and off based on schedules and direct control information received via the thermostat 102, other user devices 110/116 and/or remote server 120.

In one example, a user may program a daily heating schedule at wireless thermostat 102, and the thermostat sends the schedule to the boiler control module 104. The boiler control module then uses the schedule together with temperature measurements received from the thermostat (and/or other interior temperature sensor 101) to turn the boiler on or off as needed to attain a target temperature specified in the schedule. Instead of scheduled operation, the boiler may also be controlled based on manual settings (e.g. a manual target temperature). The target temperature may also be referred to herein as the “set point”. While in this example, the above control functions are performed by a standalone controller device, they could alternatively be integrated into the thermostat, the boiler, or any other suitable device.

The user may additionally interact with the system from a separate user device 110 connected to the local network or from a user device 116 located outside the user’s home and connected to the Internet 118. User devices 110 and 116 may take the form of smartphones, tablet computers, personal computers, and the like. User devices may include an application for controlling the heating system, for example to create or edit a heating/hot water schedule, switch between manual/scheduled operation, adjust temperature, activate boost mode, etc. The application may then send information to the wireless thermostat 102, to the boiler control module 104 or the hub 110 as required (e.g. to update a heating schedule). A control interface may also be provided through a remote server 120. This may be a web interface accessible by user devices 110, 116, to perform the operations discussed above, e.g. setting a schedule. The server then transmits control information (e.g. a modified schedule) to the heating system 100 (e.g. to the boiler control module 104).

Additionally, the application on the user device and/or the server-based control interface may provide a consumption estimation function, as discussed in more detail below.

While the above example describes a networked control system (including networked thermostat and boiler control module), the described analysis/estimation and control methods are equally applicable to other types of heating system (including those lacking network features).

Operation of the boiler control module is illustrated further in Figure 2A.

The boiler control module 104 receives temperature measurement data 202 from the thermostat 102 (and/or other interior temperature sensor(s)) and optionally pre-processes the measurements to provide a temperature signal indicative of the current ambient temperature in the environment in which the thermostat is situated.

Additionally, the receiver/controller receives control information 204, from the thermostat, or from some other device e.g. via the system hub 108 from the internal or external network. The control information may provide a control schedule defining target temperatures for one or more time periods during the day. An example of a control schedule is set out below in Table 1:

Table 1

Thus, the schedule consists of a sequence of time periods, configured as OFF” periods (no heating is to be performed) or ON” periods (heating to a specified target temperature is required).

In scheduled mode of operation, the receiver/controller controls the boiler 106 based on the schedule, by heating the environment to the target temperature set for an ON” time period during that time period. During an OFF” period no heating is performed (except that heating may still be performed during an OFF period to prevent damage to pipework and devices if the temperature falls below a frost protection threshold, e.g. 5°C). In this example, a schedule is defined over a period of a day but different schedule periods may be used. Furthermore, different schedules may be defined for different periods, e.g. one schedule may apply to weekdays and another to weekend days, or a different schedule may be defined for each day of the week.

In a manual mode, a target temperature may be set directly (without being linked to a particular time period) and the system heats the environment to that temperature until the heating is manually deactivated, the target temperature is changed, or scheduled mode is activated.

In either case, while in the active (“ON”) state, the receiver/controller controls operation of the boiler by switching the boiler on or off as needed to achieve (approximately) the desired target temperature.

Control of the boiler is via a call-for-heat (CFH) signal 206. In typical embodiments, the CFH signal has two states: when in the ON or HIGH state, the boiler turns on to heat water which is supplied to radiators in the building. When in the OFF or LOW

state, the boiler is off and no hot water is supplied. In such a system, a desired temperature is achieved by alternating the CFH signal between ON and OFF states as needed.

An example of a heating schedule and associated CFH signal is illustrated in Figure 2B. Here the schedule is represented by the target temperature curve 210. As can be seen, during a first period of time (from to to t1), the target temperature has a first value x1. During a second period of time (t1 to t2) the target temperature has a second value x2 (where x2 could, for example, represent a lower target temperature or a frost protection threshold during an OFF period of the schedule).

The corresponding CFH signal is shown as curve 214, and the actual measured temperature in the property (e.g. as measured by the thermostat’s internal temperature sensor) is shown as temperature curve 212. During a first period pO, the CFH signal is ON, causing the heating system to be on and the temperature to rise, until it reaches the target temperature. During period p1, the CFH signal alternates between ON and OFF states to maintain the temperature approximately at the target temperature x1 (in fact, the temperature will oscillate somewhat as shown due to various factors including heat loss, delay of the temperature response, variations in external temperature, doors/windows opening and closing affecting insulation of the building etc.) During period p2, the CFH signal is mostly OFF to allow the temperature to drop to the lower temperature target x2. As before, the temperature may overshoot and so CFH transitions to ON to maintain the target temperature at x2. In period p3, the CFH remains mostly ON to elevate the temperature back to target temperature x1.

In some cases, the control system may further vary the CFH signal timings e.g. to reduce overshoot/undershoot (for example by applying pulse-width modulation to the signal), to activate heating earlier than dictated by the schedule to reduce the delay until the required target temperature is achieved, or to improve energy efficiency.

As the above illustrates, the exact pattern of boiler firings (frequency and duration of CFH ON periods) can be complex and may be affected by the temperature response of the property, external temperatures, occupant behaviour etc., and thus the exact energy consumption resulting from the schedule may be difficult to predict.

Data collection

In a preferred embodiment, data from the heating system is fed back to the server for storage and processing. The data sent to the server may include, for example, any of: • Measured temperature data from any of the temperature sensors (e.g. thermostat sensor, interior sensor 101, exterior sensor 103), • The control schedule set by the user • Information on when the heating system is turned on or off; this could include the CFH signal itself (or a sampled version of it, e.g. as series of ON/OFF flags recorded at intervals, e.g. every minute). Alternatively, aggregate information could be sent, for example indicating ON durations (e.g. the proportion of time the CFH signal was active and/or the boiler was on over a given time interval) • Energy consumption information for the property as a whole (e.g. from an energy meter) or for the heating system or boiler specifically (e.g. where a dedicated energy meter for the heating system/boiler is installed). For example, for a gas boiler, gas consumption may be determined. This information may also be obtained from billing records for the property.

The energy consumption information is used to determine an average energy consumption rate of the boiler. This may be done, for example, by computing a ratio of energy consumed to total boiler firing time (e.g. measured from the CFH signal or estimated from the heating schedule) over a given interval. Where consumption is only known for the whole property and the boiler is not the only consumer of the relevant energy type (e.g. if a gas cooker is also used in the property) then the consumption due to the boiler can be estimated from the total amount, or a measurement interval can be chosen where the boiler is likely the only consumer of that fuel type (e.g. during the night).

Energy consumption estimation

Embodiments of the invention provide energy consumption analysis and estimation functionality. These functions may be implemented at server 120, accessed remotely via a user device, for example using a web interface or native client application.

The analysis is based on simulation of the effects of the heating system on the environment being heated. Two main analysis modes are illustrated in Figures 3A and 3B.

Figure 3A illustrates a schedule evaluation mode. In the schedule evaluation mode, the estimated energy consumption for a particle heating schedule is determined.

Simulation is performed based on a consumption model 302, a thermal model 304 and external temperature data 306.

The consumption model 302 provides information on the expected energy consumption rate of the heating system. This preferably indicates the consumption per unit time that the boiler is on (e.g. in Watt units, as kWh/day or in any other suitable units of measurement). The consumption model may use typical values (e.g. an average value for similar properties, a typical value for the given boiler type etc.) However, in preferred embodiments, a consumption value is determined empirically from past data collected from the heating system, such as past boiler activations and consumption information, as described above.

The thermal model indicates the (expected) response of the environment to activation of the heating system. For example, this may define an expected rate of temperature change as a function of one or more of: • Current temperature in the environment • Boiler ON/OFF state and/or firing duration • External temperature

The thermal model will typically be specific to the property where the heating system is installed. For example, in a well-insulated property, heat loss will be slow while the heating system is off and heat gain will be relatively rapid when the heating is on. A less well-insulated property may have faster heat loss during off times and slower heat gain during on periods. Heating rates may also depend on the number and placement of radiators, property layout etc.

The thermal model 304 is preferably again derived empirically based on data collected from the heating system, including temperature data relating to the heated environment (e.g. from the thermostat sensor and/or interior temperature sensor 101), the CFH signal and external temperature measurements (e.g. from exterior temperature sensor 103 or from weather data for the location of the property).

Exterior temperature data 306 provides information indicative of the external temperature at the time period for which estimation is to be performed (which may be a past or future time period as discussed further below).

In addition, the system provides the heating schedule 308 that is to be evaluated as input to the simulation. This may, for example, have been entered by a user, e.g. via the web interface, via the thermostat, or in any other way.

The simulator 300 simulates operation of the system over a defined time period based on the thermal model and external temperature data.

The simulation typically involves specifying appropriate starting conditions (e.g. internal temperature, external temperature) at the beginning of the simulated period and then applying the heating control algorithm that would be used by the actual boiler control module 104, using the schedule being evaluated, to generate a simulated CFH signal. Response of the interior temperature in the environment is then calculated for a first time interval based on the thermal model 304 derived for the environment from past data and based on the exterior temperature data 306 (e.g. to model heat loss to the exterior). Depending on the length of the period being evaluated and the required accuracy, the external temperature data may indicate a single average exterior temperature or may indicate multiple temperature values, e.g. forming a temperature curve.

The modelled interior temperature is modified according to the calculated temperature response, and the simulation then uses the modified interior temperature as the starting point for the next simulation interval. Simulation proceeds in this manner in discrete time steps, adjusting the CFH signal as needed in accordance with the chosen control algorithm, the evolving interior temperature, and the schedule, until the end of the period to be evaluated (this could e.g. be a day, a week, a month or any other time period).

The simulated CFH signal over the evaluation period provides an indication of when the heating system is active (CFH on) and when it is inactive (CFH off). The total on-time is then determined (by summing the individual ON periods). The consumption model is then used to calculate the consumption for that schedule (for example by multiplying the total ON duration by the consumption rate). This results in a consumption estimate 310 for the input schedule 308.

The consumption estimate may be expressed in kWh or in any suitable way. Tariff information may be used to determine a monetary value corresponding to the predicted consumption (e.g. by multiplying a tariff rate by the consumption value).

The calculated data is then output to the user, e.g. via a suitable graphical user interface.

The described approach can be used to determine, based on historical data, how a change in heating schedule would have affected a user’s energy consumption and/or energy bill for a past time period. In that case, the exterior temperature data 306 may be actual temperature data recorded during the relevant time period (e.g. by the exterior temperature sensor 103 at the user’s property, or alternatively using temperature data from a weather service or the like).

Alternatively, the described approach can be used to predict a future energy consumption and/or cost. In that case, estimated exterior temperatures may be used (e.g. based on typical temperatures at the location in question at the relevant time of year, or by predicting exterior temperatures based on past temperatures recorded by the exterior temperature sensor 103 at the property). A second analysis mode is depicted in Figure 3B.

In this mode, analysis is again performed based on a consumption model 302, thermal model 304 and exterior temperature 306, as described above. However, in this case, instead of a predetermined heating schedule that is to be evaluated, in this case a consumption target value 312 is specified. The simulator 300 then simulates a number of possible heating schedules to determine a proposed schedule 316 that produces a simulated energy consumption that is sufficiently close to the consumption target 312.

For example, the simulator may start from a default schedule (or multiple alternative default schedules) and vary parameters of the schedule(s), rerunning the simulation for each schedule variation. Alternatively, a heating schedule 314 may be provided as additional input. For example, this could be the user’s existing configured heating schedule. The simulator then seeks a modified version of the schedule that meets the consumption target 312.

The system may select a heating schedule that meets the consumption target to within a defined threshold distance (preferably without exceeding the target). Alternatively the system may select the schedule variation having a consumption closest to (and preferably not exceeding) the consumption target (e.g. after a defined number of simulation iterations or some other termination criterion). The schedule selection criteria may also be based on additional criteria, such as achieving a specified comfort level. For example, the system may prefer a schedule that achieves a higher average temperature to one that achieves a lower average temperature whilst at the same time falling within a given distance of the target consumption value.

By way of example, this analysis mode may allow the user to identify ways of reducing energy consumption. For example, the user may request a schedule that reduces their energy expenditure by a given absolute or relative amount (e.g. “reduce my bill by 10%”). The system then uses the simulation to identify a variation of the user’s current schedule that achieves the specified reduction.

The variations introduced may include, for example, reducing the duration of one or more ON periods, removing one or more ON periods from the schedule, and/or reducing the target temperature for one or more ON periods of the schedule.

As a further variation, the system may generate multiple proposed alternative schedules. For example, one proposal may involve reducing ON durations whilst retaining target temperatures, while another may involve reducing target temperatures without reducing ON durations of the schedule. The user may then select the preferred schedule.

While these examples are given with a view to achieving energy savings, the system may of course also be used to increase energy consumption to a particular target (e.g. if a user wishes to maximise comfort levels for a defined energy budget), in which case the schedule could similarly be varied by increasing ON durations, adding ON periods, and increasing target temperatures.

As a further variation, an activity/occupancy profile could also be used as one of the inputs. Such a profile may, for example, specify occupancy of the property over a time period such as a week (e.g. specifying times when occupants are at work/school etc.) The proposed schedule may then take this occupancy profile into account (e.g. keeping the heating off in periods where the property is unoccupied). This allows the user to determine, given their typical weekly occupancy pattern for the house and a fixed consumption budget (e.g. kWh) for the heating, which heating schedule they should adopt to stay within this budget.

Automatic schedule control

After the user has evaluated one or more schedules (as per the Fig. 3A process) or generated one or more proposed schedules (as per the Fig. 3B process), the user may select to activate a given schedule. The system can then transmit the schedule to the heating system (e.g. to the smart thermostat or boiler control module) and configure the heating system to operate in accordance with the selected schedule.

This control feature can be extended further. In one embodiment, the system continually monitors the thermal performance of the building in response to the control schedule (based on the temperature data collected by the thermostat and external temperature sensor(s) if available) and refines the thermal model 304. The system then repeats the Figure 3B process at intervals (e.g. weekly or monthly) to reevaluate consumption using the current schedule as a starting point. If the simulation produces a better schedule (e.g. one that is closer to the consumption target), the system configures the heating system with the new schedule (e.g. by pushing the schedule to the heating system hub, boiler control module and/or thermostat). Alternatively a notification could be sent to the user allowing the user to confirm whether or not the revised schedule should be used, with the schedule updated if the user responds affirmatively.

Implementation of thermal and consumption models

The following section describes in more detail example implementations of the thermal and consumption models (for use in the Figure 3A/3B estimation processes).

Thermal models

In this example, the thermal model is based on the following inputs: • house profile (can be used to set model parameters as described further below) • internal temperature - measured by the thermostat • external temperature - measured or estimated based on weather data

Additionally, the following inputs could be used: • internal temperature from additional channels measured by other sensors in the house (e.g. multiple sensors in different rooms may allow a more sophisticated/accurate model) • boiler output temperature (proxy to water temperature in radiators)

The schedule simulator 300 takes the simulated internal temperature Tint and anticipated target temperature time series (as specified by the heating schedule) and calculates the time the heating would be on by simulating what the thermostat would do for simulated inputs.

In an embodiment, the following model is used for simulation (prediction) of internal temperature Tint-

Here, Text is external temperature (measured, estimated or simulated), and ToffsetoN is an estimation of the offset from the external temperature, such that Text + ToffsetON is a maximum temperature achievable in the house if heating was ON all the time -in other words the equilibrium temperature where the house heat loss equals the heat gain provided by the heating system. TorrsetOFF is an estimation of the offset from the external temperature when heating is OFF, in other words Text + ToffsetoFFis the equilibrium temperature the house will settle to given the constant external temperature Text. Thus ToffsetoN and ToffsetOFF represent boundary conditions for the differential equations. e(t) is an error term, and τ is a passive cooling time constant.

The model parameters here are: ToffsetON,ToffsetOFF,T. If we fixText = T then there is an analytical solution for Tint at time t:

Tint(t) = (ToffsetQN + T)(l - e’t/T) + Τίηί(0)θ-ντ for heating ON,

Tint(t) = (ToffsetoFF + T)(l - e’t/T) + Τίηί(0)θ-ντ for heating OFF.

Tint (0) is the initial condition (i.e. the internal temperature at the start of the simulation).

Alternatively, the system can use a model with separate time constants for cooling and heating - analytically this will become:

Tint(t) = T + Ae'^ +

Here, τ1,τ2,Α,Β are the model parameters with A, B being bound by an initial condition. Additional heating system boiler temperature measurements will help in training such a model to unambiguously determine parameters A and B.

Alternatively, other prediction models (i.e. other than an analytical model/formula as described above) can be used - such approaches may include: • A computational-practical approach, e.g. using ARMA (autoregressive moving average), • A physics-based approach e.g. forward integration of ODE (Ordinary Differential Equation) that describes our model. • A purely data-driven approach e.g. based on deep neural networks. • Ensemble models such as random forest regression.

To determine model parameters (i.e. training the model), a number of approaches can be used.

Firstly, a “global model” can be used, in which the model parameters are fixed globally, for example τ = 20 hours and TOffsetoN = 20 °C.

Secondly, a “house model” can be used that is specific to the house/property in question. In this approach model parameters are based on national averages precomputed for a particular household profile.

Some or all of the following information may be used to define a household profile: • None - use national averages (making this equivalent to the global model). • Geographical location of house • Number of rooms/bedrooms • Size, e.g. characterized by living area (e.g. square footage) • Number of people living in the household • Type of house: terraced, mid-terraced, detached, bungalow, flat etc. • Age of the house • Energy performance rating A look-up table can provide model parameters based on the type of house assessed against these parameters (which may be collected from the user, e.g. via a web interface). Where an exact match for the user-provided parameter values is not available, the closest match in the look-up table can determine the model parameters selected.

Thirdly, in an advanced version of the “house model”, model parameters can be identified by one of the following methods: • Fitting an analytical solution using approximation/regression. • Using time series models, e.g. using a recursive ARMA (Autoregressive-moving-average) modelling approach, or an integrated ODE (Ordinary Differential Equation) model approach. • Using machine learning for purely data driven models such as neural networks or random forests.

Consumption models

Heating on-time H over a period of time T simulated (e.g. as per the method set out in the previous section) can be expressed as follows: H = [ ^(^target’ ^int)

JT

Here I can be just a Heaviside function that outputs 1 when the target temperature is above the internal temperature (Ttarget > Tint) and 0 otherwise, but it can also be represented by a complex thermostat control algorithm (preferably a simulation or exact implementation of the actual control algorithm used by the thermostat). Such a control algorithm could determine the CFH signal from the schedule in a more complex manner, for example, activating the heating ahead of an ON period so that the target temperature is reached closer to the start of the ON period, and/or using pulse-width modulation of the CFH signal on approach to the target temperature to reduce temperature overshoot.

Energy consumption E over a daily, weekly, monthly or other defined period of time can be predicted using a regression model with a set of parameters, in the following example identified as a, b, c. Parameters can be learned using past consumption, weather data and temperature measurements.

Parameter a represents usage independent of heating, e.g. hot water/cooking/secondary heating system. To create a model accurate for practical application we add simulated heating on-time H'.

E = a + bH

Use of parameter a allows estimation of total gas consumption (and thereby a consumer can be provided with a total cost evaluation for gas usage). Then the term bH provides an estimate of the energy used by the heating system modelled with the thermal model. Alternatively, the overall value E could be disaggregated into various categories including space heating using a separate disaggregation model.

Alternatively, the a parameter could be excluded in certain cases if purely heating based estimation is required.

Further, to increase accuracy of the model we can add terms that additionally depend on internal and external temperature: E = a + bH + cf(Tint, Text, H)

The consumption model inputs can include: • house profile (e.g. defined by way of user-specified parameters as discussed above) • past consumption, for example based on consumption readings from a smartmeter (e.g. taken at 30 minute our hourly intervals, though daily readings can be sufficient) • past bill information

The consumption model provides the appropriate model parameters depending on the inputs (e.g. selecting parameters from look-up table based on house profile as described above).

Additionally, the following data can be used to refine the models: • past internal temperature • past schedule (specifying the target temperature as a time series) • past weather data

Note that, internal temperature and weather data can be used to refine the results of the consumption model independently of their use in the thermal models.

The consumption model can be more sophisticated than a simple cost per unit time (e.g. cost/hour) model.

For example, it may be that the total cost for heating for a given boiler on-time may vary depending on whether that on-time represents a single boiler activation or multiple shorter activations (e.g. the boiler ignition cycle may play a part). Thus, two separate periods of one hour duration could use more energy that one continuous period of two hours duration. To account for this, the pattern of boiler activation periods can be taken into account (e.g. this may be learned from previous consumption data).

More generally, by using a data driven approach (as opposed to a theoretical modelling approach), other factors which might not be understood or recognised in terms of physical cause and effect can be taken into account. A data-driven approach could be employed, for example, to determine the thermal model (based on past temperature performance of the property or of similar properties) and/or the consumption model (based on past consumption data).

Figure 4 summarises the data flow of the energy consumption estimation process. The heating schedule 308 is provided as the main input to the thermal model 304. Additional inputs such as actual/expected external temperature data may be provided as discussed previously. The thermal model 304 is used to compute heating on-time data 402 based on simulation of the heating system over a defined period. The on- time data may be single total on-time value or data series defining a sequence of heating activations (e.g. based on a simulated CFH signal). The on-time data 402 is provided as input to the consumption model 302 which computes corresponding energy consumption data. This may include a single consumption value and/or a series of values (e.g. daily consumption over the evaluated period). Consumption data 404 may include additional data such as energy cost data (e.g. calculated based on configured tariff information).

Figure 5 summarises the process flow of the energy consumption estimation process.

In step 502, past heating system control and temperature response data is received (e.g. past schedule data and/or CFH signal data along with internal temperature data measured by the thermostat). In step 504, the parameters of the thermal model are determined as described above. The model parameters characterise the temperature response of the property to activity of the heating system (including heat loss when the heating is inactive). In step 506, past consumption data is received (e.g. from an energy meter), and is used to determine consumption model parameters in step 508.

In step 510 a proposed or actual control schedule is received. In step 512, the heating control system and thermal response of the heated environment is simulated based on the derived thermal model to determine heating system activations required to achieve the specified control schedule. In step 514, the energy consumption due to application of the schedule is determined, based on the heating system activation data and the consumption model. In step 516, the computed energy consumption data is provided as output. Output may, for example, be to a user interface, or to a subsystem performing automatic control schedule adjustment as described previously.

Steps 502-508 relating to data-driven model generation may be performed separately from the estimation steps 510-516. Steps 502-504 for determining the thermal model may further proceed independently of I in parallel to steps 506-508 for determining the consumption model, and either part of the method may be omitted e.g. where a fixed predetermined model (e.g. derived based on theoretical considerations, or average user data) is used. The model generation steps may also be repeated at intervals, so as to provide thermal and/or consumption models that adapt continuously to performance characteristics of the heating system and the environment in which it operates.

As discussed previously, estimation steps 510-514 may be performed on-demand, e.g. to evaluate a particular schedule (see Figure 3A) or may be applied repeatedly, e.g. to evaluate schedule variations when searching for a schedule meeting certain constraints (e.g. consumption target; see Figure 3B).

Other applications

The described approach can be applied to other forms of climate control other than hot water/boiler systems. For example, it could be adapted to a furnace/hot air system or be applied to air conditioning or other cooling systems or a combination of such systems. Where applied to cooling systems, the system is typically controlled to cool an environment down to a target temperature (rather than heating the environment up to a target temperature), but the principles of the above method may be applied. Some applications (e.g. refrigeration) may have a fixed target temperature that applies at all times, and thus have no need for a control schedule.

Whilst described above principally in relation to residential dwellings/properties (e.g. houses/apartments) the described approaches can be extended outside of residential applications to industrial or commercial buildings such as hotels, offices, factories, chemical processing plants, refrigeration facilities etc. Indeed, the invention may be applied to any environment in which climate control is applied (not just buildings) -examples may include vehicles such as climate controlled cars or refrigerated trucks, aircraft, ships etc.

Computer system

Figure 6 illustrates a computer system which may be used for implementing the methods described above.

The computer system is in the form of computer server 120. The server includes one or more processors 602 together with volatile I random access memory 604 for storing temporary data and software code being executed. Persistent storage 606 (e.g. in the form of hard disk or solid state storage) persistently stores software and data, including a simulation program 610 and the various data used by the simulation program, e.g. consumption model data 302, thermal model data 304 and exterior temperature data 306. The persistent storage may include other software and data, such as an operating system and device driver software for general operation of the server. A network interface 612 allows communication with external networks (including the Internet). The server can connect to the heating system using the network interface to receive data such as heating schedules, temperature data and heating control data and transmit a selected/generated schedule to the heating system. The server can also communicate via the network interface and connected networks with a user device to provide access to the consumption simulation/estimation functions, e.g. via a web interface.

In one example, such a web interface and associated web application allows a user to input schedules, consumption targets and other relevant information and run simulations to evaluate performance of schedules, refine a schedule etc. Schedules may also be pulled from/pushed to the thermostat 102 / boiler control module 104 by the application. Instead of (or in addition to) a web application, a dedicated mobile application (e.g. for smartphones/tablet computers) may be provided and/or the simulation functionality may be implemented directly on thermostat 102 using a user interface of the thermostat.

It will be understood that the present invention has been described above purely by way of example, and modification of detail can be made within the scope of the invention.

For example, while described in relation to a heating system, the described estimation processes could be applied to other temperature control systems. For example, the described processes could be applied to cooling systems such as air conditioners or refrigerators.

The examples above relate to heating systems in domestic settings (e.g. residential houses/apartments), but the methods are equally applicable to temperature control systems in other settings (e.g. commercial properties, offices, factories), and to other forms of environmental control systems (e.g. humidity regulation).

The predictive model approach can be further generalized to any type of appliance to allow cost or energy consumption of particular appliance configurations to be evaluated. In that case, the prediction model may be learned from an appliance’s past configurations and energy consumption data for the appliance or for the whole property where the appliance is installed. For example, the energy consumption of a washing machine over a defined period (e.g. a month) may be predicted based on particular configurations (e.g. wash programs) and particular usage frequencies.

Claims (36)

1. A method of estimating energy consumption associated with a temperature control system arranged to control the temperature in an environment, the method comprising: receiving a control schedule for the temperature control system; simulating operation of the temperature control system in accordance with the control schedule; determining, based on the simulation, an energy consumption value indicative of energy consumption associated with the temperature control system when controlled in accordance with the control schedule; and outputting the energy consumption value.
2. A method according to claim 1, wherein the simulating step comprises simulating activation of the temperature control system to control the temperature in the environment, preferably wherein activation of the temperature control system comprises activation of a temperature control device arranged to alter the temperature in the environment, preferably in response to an activation signal.
3. A method according to claim 1 or 2, comprising simulating operation of the temperature control system based on a thermal model for the environment.
4. A method according to claim 3, wherein the thermal model specifies a temperature response of the environment to activity of the temperature control system.
5. A method according to claim 3 or 4, wherein the thermal model comprises a function specifying a temperature of the environment in dependence on one or more model parameters.
6. A method according to claim 5, the one or more model parameters comprising one or more of: an external temperature, an offset temperature, and a passive cooling time constant, preferably wherein the thermal model comprises a function of the form: Tint(t) = (Toffset + T)(l - e-tA) + Tint(0)e_t/T where Tint (t) is the interior temperature of the environment at time t, T is a temperature value representative of the exterior temperature, optionally fixed for the simulation period, Toffset is an offset from the external temperature representing an equilibrium temperature when the temperature control system is in a permanent ON or OFF state, Tint (0) is the internal temperature at the start of the simulation and τ is a passive cooling time constant.
7. A method according to claim 5, the one or more model parameters comprising one or more of: an external temperature, a cooling time constant, a heating time constant, and one or more parameters representing an initial condition, preferably wherein the thermal model comprises a function of the form: Wt) = T + + Be-^, where Tint (t) is the interior temperature of the environment at time t, and τ2 are heating and cooling time constants and A and B are model parameters representing an initial condition.
8. A method according to any of claims 5 to 7, comprising selecting one or more (or all) model parameters based on one or more properties of the environment, the properties preferably comprising one or more of: geographical location, size, number of rooms or bedrooms, number of occupants, property type, property age, property energy performance rating.
9. A method according to any of claims 5 to 8, comprising determining one or more (or all) model parameters based on past performance data relating to the temperature control system.
10. A method according to any of claims 3 to 9, comprising generating the thermal model based on past performance data relating to the temperature control system, the past performance data optionally including data received from the temperature control system.
11. A method according to claim 9 or 10 wherein the past performance data comprises one or more of: a control schedule for a past time period; data specifying activations of the temperature control system during a past time period, the data optionally comprising or derived from a control signal used for activating the temperature control system to perform temperature control; interior temperature data relating to an interior temperature of the controlled environment during a past time period; and exterior temperature data relating to a temperature external to the controlled environment during a past time period.
12. A method according to any of claims 9 to 11, comprising determining the thermal model based on past performance data by model fitting using approximation and/or regression and/or using machine learning.
13. A method according to any of the preceding claims, wherein the control schedule specifies one or more active periods for the temperature control system and optionally, for one or more (or each) of the active periods, a target temperature value.
14. A method according to claim 13, wherein simulating operation of the temperature control system comprises simulating operation of the temperature control system to attain a specified target temperature value in the environment during a specified active period defined in the control schedule.
15. A method according to any of the preceding claims, wherein simulating the operation of the temperature control system comprises calculating temperature changes in the environment in response to operation in accordance with the control schedule.
16. A method according to any of the preceding claims, wherein simulating the operation of the temperature control system comprises generating information defining a plurality of activation periods of the temperature control system, the information optionally comprising a simulated control signal for activating the temperature control system.
17. A method according to claim 16, wherein generating the information comprises simulating operation of a temperature control algorithm, the simulation of the temperature control algorithm generating a control signal based on a modelled interior temperature of the environment and the control schedule, the modelled interior temperature preferably modified during a simulation period based on the generated control signal.
18. A method according to any of the preceding claims, comprising calculating an activation time of the temperature control system, the activation time indicating a total time that the temperature control system is activated to control temperature during a simulated period.
19. A method according to any of claims 16 to 18, comprising generating the energy consumption value based on the activation periods and/or activation time, and based on a consumption model, the consumption model preferably comprising an energy consumption rate.
20. A method according to claim 19, comprising determining the energy consumption model or rate based on data received from the temperature control system, optionally based on activation data of the temperature control system during a predetermined past time period and energy consumption data of the temperature control system for the past time period.
21. A method according to claim 19 or 20, wherein the consumption model comprises a function providing an energy consumption value for a period of time in dependence on a simulated activation time of the temperature control system during the period of time, the model preferably comprising one or more model parameters determined based on past energy consumption data and/or selected based on one or more properties of the temperature control system and/or of the environment.
22. A method according to any of the preceding claims, comprising performing the simulation step for a plurality of control schedules and calculating a respective energy consumption value for each of the control schedules.
23. A method according to claim 22, comprising selecting one of the plurality of control schedules based on the energy consumption values, and outputting the selected control schedule.
24. A method according to claim 23, comprising selecting one of the plurality of control schedules based on the energy consumption values calculated for the control schedules and based on a target energy consumption value.
25. A method of estimating energy consumption associated with a temperature control system arranged to control the temperature in an environment, the method comprising: receiving a target energy consumption value; simulating operation of the control system in accordance with a plurality of control schedules to determine estimated energy consumption values for each control schedule; selecting one of the plurality of control schedules, based on the estimated energy consumption values and the target energy consumption value; and outputting the selected control schedule.
26. A method according to claim 25, the simulating comprising performing a method as set out in any of claims 1 to 21.
27. A method according to any of claims 23 to 26, comprising selecting the control schedule having an associated energy consumption value meeting one or more predetermined criteria, the criteria optionally comprising one or more of: the energy consumption value for the selected schedule not exceeding the target energy consumption value; the energy consumption value for the selected schedule being within a threshold distance of the target energy consumption value; and/or selecting the control schedule having an associated energy consumption value closest to the target energy consumption value.
28. A method according to any of claims 22 to 27, comprising generating one or more of the plurality of control schedules, preferably as variations of the received control schedule.
29. A method according any of the preceding claims, comprising transmitting the received or selected control schedule to the temperature control system and/or configuring the temperature control system to operate in accordance with the received or selected control schedule.
30. A method according to any of the preceding claims, wherein the temperature control system is a heating system, preferably wherein activation of the temperature control system to control temperature of the environment comprises activating a heater device, for example a boiler.
31. A method of determining energy consumption associated with a temperature control system arranged to control the temperature in an environment, the method comprising: receiving data from the temperature control system relating to a temperature response of the environment to activity of the temperature control system; determining based on the data one or more parameters of a thermal model characterising the temperature response of the environment; receiving a temperature control schedule for use with the temperature control system; and calculating estimated energy consumption data corresponding to operation of the temperature control system in accordance with the control schedule, based on the received control schedule and the thermal model.
32. A method according to claim 31, wherein the calculating step is further performed based on a consumption model characterising energy consumption of the temperature control system.
33. A method according to claim 32, comprising determining one or more parameters of the consumption model based on past consumption and/or control data of the temperature control system.
34. A method according to any of claims 31 to 33, further comprising performing a method as set out in any of claims 1 to 30.
35. A system having means for performing a method as set out in any of the preceding claims.
36. A computer-readable medium comprising software code adapted, when executed on a data processing system, to perform a method as set out in any of claims 1 to 34.
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