Managing Emissions Demand Response Event Generation

This application is a continuation of U.S. application Ser. No. 18/455,392, filed Aug. 24, 2023, entitled “MANAGING EMISSIONS DEMAND RESPONSE EVENT GENERATION,” which is a continuation of U.S. application Ser. No. 17/350,787, filed Jun. 17, 2021, entitled “MANAGING EMISSIONS DEMAND RESPONSE EVENT GENERATION,” now U.S. Pat. No. 11,781,769, issued Oct. 10, 2023, which shares a common description and figures with U.S. application Ser. No. 17/350,793, filed on Jun. 17, 2021, entitled “DYNAMIC ADAPTATION OF EMISSIONS DEMAND RESPONSE EVENTS,” U.S. application Ser. No. 17/350,801, filed on Jun. 17, 2021, entitled “MANAGING USER ACCOUNT PARTICIPATION IN EMISSIONS DEMAND RESPONSE EVENTS,” and U.S. application Ser. No. 17/350,808, filed on Jun. 17, 2021, entitled “MANAGING EMISSIONS DEMAND RESPONSE EVENT INTENSITY.” The entire disclosure of all of the aforementioned U.S. Patent Applications are hereby incorporated by reference, for all purposes, as if fully set forth herein.

A thermostat can be used to control heating system, cooling system, fans, ventilation systems, dehumidifiers, humidifiers, or any other related systems. Users can benefit from using a smart thermostat that can communicate via a wireless network with a cloud-based server. Such wireless network connectivity can allow for the thermostat to be controlled remotely by a user or by various services provided by the cloud-based server. Scheduling the electricity consumption of an HVAC system controlled by a thermostat to coincide with times of cleaner electricity availability can reduce carbon emissions.

Various embodiments are described related to a method for performing an emissions demand response event. In some embodiments, a method for performing an emissions demand response event is described. The method may comprise receiving, by a cloud-based HVAC control server system, an emissions rate forecast for a predefined future time period. The method may comprise determining, by the cloud-based HVAC control server system, using the emissions rate forecast, an emissions differential value for each of a plurality of points in time during the predefined future time period, thereby creating a plurality of emissions differential values. The emissions differential value may represent a change in emissions over time. The method may comprise generating, by the cloud-based HVAC control server system, based on the determined plurality of emissions differential values and a predefined maximum number of emissions demand response events, an emissions demand response event having a start time and an end time during the predefined future time period. The method may comprise causing, by the cloud-based HVAC control server system, a thermostat to control an HVAC system in accordance with the generated emissions demand response event.

Embodiments of such a method may include one or more of the following features: the emissions differential value for each of the plurality of points in time may be determined from a difference between a first emissions rate before the point in time and a second emissions rate after the point in time. The generated emissions demand response event may be a preemptive emissions demand response event. For a preemptive emissions demand response event, the cloud-based HVAC control server system may cause the thermostat to adjust a setpoint temperature that increases usage of the HVAC system. When the HVAC system is in a cooling mode, causing the thermostat to adjust the setpoint temperature for a preemptive emissions demand response event may comprise decreasing the setpoint temperature. When the HVAC system is in a heating mode, causing the thermostat to adjust the setpoint temperature for a preemptive emissions demand response event may comprise increasing a setpoint temperature.

Embodiments of the method may also include one or more of the following features: the generated emissions demand response event may be a deferred emissions demand response event. For a deferred emissions demand response event, the cloud-based HVAC control server system may cause the thermostat to adjust a setpoint temperature that decreases usage of the HVAC system. When the HVAC system is in a cooling mode, causing the thermostat to adjust the setpoint temperature for a deferred emissions demand response event may comprise increasing a setpoint temperature. When the HVAC system is in a heating mode, causing the thermostat to adjust the setpoint temperature for a deferred emissions demand response event may comprise decreasing the setpoint temperature.

The method may further comprise determining, for each of the plurality of emissions differential values, a preemptive event score being equal to the emissions differential value for a preemptive emissions demand response event ending at the point in time associated with the emissions differential value, thereby creating a plurality of preemptive event scores. The method may further comprise determining, for each of the plurality of emissions differential values, a deferred event score being equal to a negative of the emissions differential value for a deferred emissions demand response event ending at the point in time associated with the emissions differential value, thereby creating a plurality of deferred event scores. Generating the emissions demand response event may be based on a ranking of the plurality of preemptive event scores and the plurality of deferred event scores.

In some embodiments of the method, the predefined maximum number of emissions demand response events may be a maximum number of preemptive emissions demand response events during the predefined future time period. Generating the emissions demand response event may further comprise restricting generation of a preemptive emissions demand response event when a number of preemptive emissions demand response events previously generated during the predefined future time period may be equal to the maximum number of preemptive emissions demand response events.

In some embodiments of the method, the predefined maximum number of emissions demand response events may be a maximum number of deferred emissions demand response events during the predefined future time period. Generating the emissions demand response event may further comprise restricting generation of a deferred emissions demand response event when a number of deferred emissions demand response events previously generated during the predefined future time period may be equal to the maximum number of deferred emissions demand response events.

In some embodiments, generating the emissions demand response event may further comprise determining that a previously generated preemptive emissions demand response event was generated. Generating the emissions demand response event may further comprise restricting generation of an additional preemptive emissions demand response event until a minimum time period after the previously generated preemptive emissions demand response event.

The method may further comprise determining that the generated emissions demand response event may be a deferred emissions demand response event. The method may further comprise restricting generation of a new deferred emissions demand response event within a predefined minimum time period before and after the generated emissions demand response event. Generating the emissions demand response event may further comprise restricting generation of an emissions demand response event having an end time later than a predefined latest time of day, restricting generation of an emissions demand response event having a start time earlier than a predefined earliest time of day, or both.

In some embodiments, generating the emissions demand response event may further comprise comparing an event score for the generated emissions demand response event with a minimum emissions demand response event score. Generating the emissions demand response event may further comprise determining that the event score for the generated emissions demand response event may be greater than the minimum emissions demand response event score. Causing the thermostat to control the HVAC system in accordance with the generated emissions demand response event may be at least partially based on the determination that the event score may be greater than the minimum emissions demand response event score. The predefined future time period may be 24 hours.

In some embodiments, a system for performing an emissions demand response event is described. The system may comprise a cloud-based power control server system. The cloud-based power control server system may comprise one or more processors. The cloud-based power control server system may comprise a memory communicatively coupled with and readable by the one or more processors and having stored therein processor-readable instructions which, when executed by the one or more processors, cause the one or more processors to receive an emissions rate forecast for a predefined future time period. The one or more processors may determine, using the emissions rate forecast, an emissions differential value for each of a plurality of points in time during the predefined future time period, thereby creating a plurality of emissions differential values. The emissions differential value may represent a change in emissions over time. The one or more processors may generate, based on the determined plurality of emissions differential values and a predefined maximum number of emissions demand response events, an emissions demand response event having a start time and an end time during the predefined future time period. The one or more processors may cause a thermostat to control an HVAC system in accordance with the generated emissions demand response event.

Embodiments of such a system may further comprise a plurality of thermostats comprising the thermostat. The system may further comprise an application executed on a mobile device configured to control the thermostat via communication with the cloud-based power control server system. In some embodiments, the emissions differential value for each of the plurality of points in time is determined from a difference between a first emissions rate before the point in time and a second emissions rate after the point in time. The generated emissions demand response event may be a preemptive emissions demand response event. The processor-readable instructions, when executed, further cause the one or more processors to cause the thermostat to adjust a setpoint temperature that increases usage of the HVAC system.

In some embodiments, a non-transitory processor-readable medium is described. The medium may comprise processor-readable instructions configured to cause one or more processors to receive an emissions rate forecast for a predefined future time period. The one or more processors may determine, using the emissions rate forecast, an emissions differential value for each of a plurality of points in time during the predefined future time period, thereby creating a plurality of emissions differential values. The emissions differential value may represent a change in emissions over time. The one or more processors may generate based on the determined plurality of emissions differential values and a predefined maximum number of emissions demand response events, an emissions demand response event having a start time and an end time during the predefined future time period. The one or more processors may cause a thermostat to control an HVAC system in accordance with the generated emissions demand response event.

Embodiments of such a medium may include one or more of the following features: the predefined maximum number of emissions demand response events may be a maximum number of deferred emissions demand response events during the predefined future time period. The processor-readable instructions may be further configured to restrict generation of a deferred emissions demand response event when a number of deferred emissions demand response events previously generated during the predefined future time period may be equal to the maximum number of deferred emissions demand response events. The processor-readable instructions are further configured to restrict generation of an emissions demand response event having an end time later than a predefined latest time of day, restricting generation of an emissions demand response event having a start time earlier than a predefined earliest time of day, or both.

Utility companies face ongoing challenges with consistently satisfying the demand for electricity while reducing the overall generation of carbon emissions. The variance in consumers' demand for electricity, combined with the varying availability of cleaner electricity, can often make it challenging to satisfy the consumers' demands and consistently maintain a low level of carbon emissions.

The variances in consumer demand and cleaner electricity supply can be attributed to a number of factors. Consumer demand may be driven by factors such as the weather, a consumer being home or away, the time of day, the day of the week, or the time of year. For example, utility companies may experience increased demand during extreme heat or cold waves or in the evenings when residents have returned to their homes and have increased their electricity consumption. Similarly, the supply of cleaner electricity may depend on factors such as the weather, the time of year, and/or season. For instance, during stormy weather or during the winter when the days are shorter, the availability of solar power may decrease. Similarly, there may be seasonal or daily variations in wind patterns that correlate with a decrease or increase in electricity generated by wind turbines.

When cleaner electricity supply is unable to meet demands, a utility company may need to rely on sources of electricity that tend to create more pollution, including carbon dioxide. For instance, when demand is relatively low, a greater portion of demand may be satisfied using clean and relatively clean electricity sources, such as wind, solar, and hydropower. However, when demand increases and/or cleaner electricity supply is lower, other, more polluting, power sources may need to be utilized, such as diesel generators, coal-fired power stations, and natural gas turbines.

To reduce the consumption of electricity when more polluting power sources are in use, which can be referred to as “dirtier electricity” and, thus, decrease pollution, Emissions Demand Response (“EDR”) events may be utilized. The objective of EDR events is to reduce the aggregate use of dirty energy and increase the aggregate use of clean energy. EDR events may achieve this objective by shifting electricity consumption earlier or later in time to coincide with times when electricity will be produced using cleaner energy sources and away from times when electricity will be produced using dirtier energy sources. For example, an EDR event may attempt to shift electrical load from times when the electricity will be produced using petroleum to times when electricity will be produced using wind or solar energy. As another example, for a grid with natural gas and coal power plants, and minimal carbon free energy, an EDR event may shift electrical load from times when coal will be used to generate electricity towards times when natural gas will be used to generate electricity.

At any particular point in time, an adjustment to the consumption of electricity will correspond with an adjustment in the production of electricity by one or more power plants in order to balance the supply of electricity with the demand. Each of the one or more power plants producing the electricity will have their own emissions characteristics, which could be measured as the amount of carbon emissions generated per unit of electricity produced. As the demand for electricity increases, the production of electricity, and therefore emissions, may also increase depending on the source of the electricity. Similarly, as the demand for electricity decreases, the production of electricity, and therefore emissions, may also decrease depending on the source of the electricity. The amount of emissions produced in the generation of the additional electricity will be based on the emissions characteristics associated with the source of the electricity as will the amount of emissions eliminated by the generation of less electricity. The aggregate amount of emissions that would be produced or reduced as the electrical load changes can be represented by a value, called the Marginal Emissions Rate (“MER”), and is usually measured by weight of carbon dioxide per unit of energy consumed or produced, for instance, lbs-CO2/MWh.

MER Forecasts may be generated to predict the MER at various times in the future. By using current and forecast MER data, EDR events may be generated to shift the electricity load from times when electricity consumption will produce higher levels of carbon emissions to times when carbon emission will be significantly less. In some embodiments, a goal is to reduce carbon emissions with shifts of electric loads including, but not limited to, HVAC loads such as electric-powered cooling (e.g., air conditioners), running a fan, and electric-powered heating systems. The aggregation of many small shifts across many structures (e.g., homes, buildings, apartments, offices) can result in large changes in the emissions resulting from that electricity usage.

One way of shifting electric loads can be by making adjustments to user thermostat temperature setpoints. Using the current and predicted emissions rate data, a system can determine when and for how long an adjustment to user setpoints will achieve reduced emissions. Similarly, because the system knows whether the emissions rate will rise or fall, it can determine whether to increase or decrease the thermostat setpoint temperature. With forecasted emissions data, the system can generate scheduled events at various points during the span of time covered by the forecast. However, due to the uncertain nature of forecasted data, updated forecasts and current emissions data can be used to periodically or occasionally modify the previously generated events, thereby achieving improvements in carbon emissions reduction.

In the past, achieving a reduction in carbon emissions, especially by individuals, could be challenging due to the perceived amount of effort required to reduce one's carbon footprint. People who may otherwise be reluctant to take active steps to reduce their carbon footprint, can reduce carbon emissions with very little effort by allowing automatic adjustments to their thermostat setpoints. However, the perceived amount of discomfort associated with reducing carbon emissions creates an additional barrier to overcome. This is especially true in the context of heating or cooling, as some people may be sensitive to even minor changes in the ambient temperatures. Similarly, some people may be sensitive to the number of times their thermostat setpoint is automatically adjusted each day.

The features described herein advantageously address this sensitivity in a number of ways. For example, people can have the ability to opt-in and/or opt-out of emissions reduction programs of varying levels at any time. Further, even when opted-into a program, people can have the ability to make real-time adjustments to their setpoint temperature at any time during the execution of an emissions reduction event, as described further below. One objective achieved by some of the embodiments is the judicious creation of a balance between aggressive thermostat control, which provides good potential reduction of carbon emissions, but could result in more annoyance or discomfort and associated real-time setpoint overrides, and less aggressive control, which generally provides more comfort and less annoyance, and less probability of real-time setpoint overrides, but which does not provide as much potential for reduction of carbon emissions.

One way of balancing discomfort with a reduction in carbon emissions can be by placing constraints on the generation, execution, and termination of EDR events. For example, the number of load shifting events per day may be limited or the number of a specific type of EDR event may be limited. Similarly, constraining events to certain times during the day and spacing them throughout the day, and/or limiting the aggressiveness of temperature offsets from the normally scheduled temperature setpoints, may reduce the perceived level of discomfort to a user. With more advanced systems, characteristics specific to a user account associated with a thermostat may be used to determine characteristics of an EDR event. For instance, a system may learn over time that occupants in a home or building with a thermostat associated with a first account are willing to tolerate more frequent events with small changes to the setpoint temperature while occupants in a home or building associated with a second account are willing to tolerate events with a larger adjustment to the setpoint temperature but with less. Therefore, by adjusting the constraints per user account or by thermostat associated with a user account, an increased amount of carbon emissions reduction may be achieved while limiting the amount of user discomfort. Further detail regarding these embodiments, and others, is provided in relation to the figures.

While the above description focuses on the use of smart thermostats, the embodiments detailed herein can be applied to other smart controllable systems that use significant amounts of electricity for which use can be time-shifted. For example, the consumption of electricity by various appliances such as Electric Vehicle (“EV”) charging stations and smart refrigerators may be shifted from times when energy consumption will produce high levels of carbon emissions to times when carbon emission will be lower. As another example, electrical load from other older or ‘non-connected’ devices may still be shifted using various devices designed to control the amount of electricity flowing to a particular device such as smart outlets or smart light sockets.

Further detail regarding the generation and management of EDR events is provided in relation to the figures.illustrates an embodiment of a systemfor managing EDR events. Systemcan include: cloud-based power control server system; emissions data system; network; mobile device; personal computer; smart thermostat; Electric Vehicle (“EV”) charging station; and smart appliance. Smart thermostatcan be connected to Heating, Ventilation, and Air Conditioning (“HVAC”) system. EV charging stationmay be connected to electric vehicles. In some embodiments, one or more of the components of systemmay be communicatively connected to other components of systemvia network.

Cloud-based power control server systemcan include one or more processors configured to perform various functions, such as generate and manage EDR events, as further described in relation to, infra. Cloud-based power control server systemcan include one or more physical servers running one or more processes. Cloud-based power control server systemcan also include one or more processes distributed across a cloud-based server system. In some embodiments, cloud-based power control server systemis connected over networkto any or all of the other components of system. For instance, cloud-based power control server systemmay connect to emissions data systemto receive current and forecast emissions data. In some embodiments, the current and forecast emissions data is represented as a percentage value representing the relative emissions at a point in time compared to the recorded emissions over a period of time in the past. For example, a value of zero at a particular point in time might mean that the emissions rate is equivalent to the minimum rate of emissions over the past two weeks while a value of one-hundred might mean that the emissions rate is equivalent to the maximum rate of emissions over the past two weeks. In some embodiments, the current and forecast emissions data is represented as the MER (e.g., lbs-CO2/MWh). The forecast emissions data may include forecasted rates of emissions at regular intervals over a period of time into the future. For example, an emissions rate forecast may include the predicted emissions rate at five minute intervals over a 24 hour period of time. The forecast emissions rate, or MER, data may range in accuracy depending on the source and/or how the emissions rate is determined. For example, the forecast emissions rate may be generated using a model that accepts multiple inputs with varying degrees of correlation to the actual emissions rate, such as, weather data, publicly available grid demand and/or price data, and historical emissions rate data. Alternatively, other forecast emissions rates may be based directly on data obtained from utilities and/or grid operators.

The data received from emissions data systemcan in turn be used by cloud-based power control server systemto generate and manage EDR events. Cloud-based power control server systemmay also connect to mobile deviceand personal computerto send updates or notifications about upcoming EDR events. For example, after generating an EDR event, cloud-based power control server systemmay send a notification to the user of mobile deviceabout an EDR event that has been scheduled for a smart thermostatowned by the user of mobile device. Cloud-based power control server systemcan also distribute the instructions or details of newly generated EDR events to smart thermostat, EV charging station, and/or smart appliance.

Emissions data systemcan be a server system, such as a cloud based server system, connected through networkand may be capable of running one or more processes related to collecting and generating emissions rate data. Alternatively, emissions data systemcan be a commercially available service such as WattTime™ or any other similar website or web service with a published Application Programming Interface (“API”) that provides such emissions rate data and/or equivalents thereof and/or substitutes therefor, such as websites or web services that provide forward-looking estimates of “dirtiness” per kilowatt-hour or, more generally, some forward-looking estimate of “undesirability” or “less desirability” per kilowatt-hour. For example, emissions data systemmay publish an API allowing external systems, such as cloud-based power control server system, to connect to it over networkin order to send requests for data and receive the requested data in response. Emissions data systemmay also connect to external services to receive data from various sources. For example, emissions data systemmay connect over networkto multiple utility companies in order to receive emissions data corresponding to the current and expected emissions generated by the power plants owned by the utility company providing power to a city or region. Emissions data systemcan also connect to other data sources, such as a national weather service, in order to collect additional data relevant to generating an emissions rate forecast using a model or any other suitable calculation. Emissions data systemin turn can use all of the data it collects, along with historical emissions rate data, to generate detailed forecasts of the estimated MER for a period of time into the future.

Networkcan include one or more wireless networks, wired networks, public networks, private networks, and/or mesh networks. A home wireless local area network (e.g., a Wi-Fi network) may be part of network. Networkcan include the Internet. Networkcan include a mesh network, which may include one or more other smart home devices, and may be used to enable smart thermostat, EV charging station, and smart applianceto communicate with another network, such as a Wi-Fi network. Any of smart thermostat, EV charging station, and smart appliancemay function as an edge router that translates communications received from other devices on a relatively low power mesh network to another form of network, such as a relatively higher power network, such as a Wi-Fi network.

Mobile devicemay be a smartphone, tablet computer, laptop computer, gaming device, or some other form of computerized device that can communicate with cloud-based power control server systemvia networkor can communicate directly with any of thermostat, EV charging station, and smart appliance(e.g., via Bluetooth® or some other device-to-device communication protocol). Similarly, personal computermay be a laptop computer, desktop computer, or some other computerized device that can communicate with cloud-based power control server systemvia networkor can communicate directly with any of smart thermostat, EV charging station, and smart appliance. A user can interact with an application executed on mobile deviceor personal computerto control or interact with smart thermostat, EV charging station, and smart appliance. For example, the user of mobile device, or personal computer, can connect via networkto smart thermostatat the user's home to monitor the status of smart thermostator send heating and cooling instructions to smart thermostatthat will in turn cause an HVAC system to provide heating or cooling to the user's home. Mobile devicemay also be connected over networkto cloud-based power control server system. For example, cloud-based power control server systemmay send notifications to the user of mobile deviceabout opportunities to participate in EDR events or cloud-based power control server systemmay send updates about the status of upcoming or ongoing EDR events. The notifications or updates may be in the form of a text message, an email, or a notification through an application.

Smart thermostatcan be a smart thermostat capable of connecting to networkand controlling an HVAC system. Smart thermostatmay include one or more processors that may execute special-purpose software stored in a memory of smart thermostat. Smart thermostatcan include one or more sensors, such as a temperature sensor or an ambient light sensor. Smart thermostatcan also include an electronic display. The electronic display may include a touch sensor that allows a user to interact with the electronic screen. Smart thermostatmay connect via networkto cloud-based power control server system. For example, smart thermostatmay receive instructions for an EDR event from cloud-based power control server system. Smart thermostatmay also receive emissions rate data from cloud-based power control server systemvia network.

In some embodiments, smart thermostatmay connect via networkto mobile deviceor personal computer. For example, smart thermostatmay receive heating or cooling instructions from a user's mobile deviceor personal computer. In some embodiments, smart thermostatwill modify EDR events and/or opt out of future EDR events altogether. For example, smart thermostatmay receive an input, such as a setpoint temperature adjustment, at the thermostat that results in an ongoing EDR event being modified. As another example, smart thermostatmay receive one or more instructions from mobile deviceresulting in smart thermostatno longer participating and/or generating future EDR events. As another example, smart thermostatmay receive one or more Smart thermostatmay also be connected to an HVAC systemand may cause HVAC systemto provide heating or cooling until a setpoint temperature measured at smart thermostathas been achieved. HVAC systemmay be any type of HVAC system such as: an electric water heater connected to a hydronic baseboard, an electric baseboard, a fan unit of forced air system, etc.

EV charging stationmay be a charging system capable of charging one or more electric vehicles. EV charging stationmay also be connected via networkto cloud-based power control server system. For example, EV charging stationmay receive instructions for an EDR event from cloud-based power control server system. EV charging stationmay also receive emissions rate data from cloud-based power control server systemvia network. In some embodiments, EV charging stationmay connect via networkto mobile deviceor personal computer. For example, EV charging stationmay send notifications or updates to a user's mobile deviceor personal computerregarding the charging status of the user's electric vehicle. Similarly, smart appliancemay be any appliance capable of connecting to networkand modifying the consumption of electricity by either the smart appliance or a device connected to smart appliance.

illustrates an embodiment of a systemfor managing EDR events. Systemcan include: cloud-based power control server system; emissions data system; network; mobile device; smart thermostat; and HVAC system. Emissions data systemmay function as detailed in relation to, supra. Smart thermostatmay function as detailed in relation to, supra. HVAC systemmay function as detailed in relation to, supra. Networkmay function as detailed in relation to, supra.

Cloud-based power control systemcan include a plurality of services such as: API engine; communication interface; event scheduler; constraints engine; historical data engine; a user management module; and forecast engine. Cloud-based power control server systemcan also include one or more databases such as emissions rate database. Cloud-based power control server systemcan also include processing systemthat can coordinate the execution of the various functionalities provided by the plurality of services and can communicate with the one or more databases such as emissions rate database.

API enginemay implement published interfaces from one or more external systems. The published interfaces may allow cloud-based power control server systemto interact with various external systems to request and exchange data. API enginemay also allow cloud-based power control server systemto communicate with various devices connected to network. For example, API enginemay implement an interface for sending text messages, emails, or application notifications to mobile device. API enginemay also allow cloud-based power control server systemto send instructions for performing EDR events to smart devices connected to network. For example, API enginemay implement an interface for smart thermostat.

Communication interfacemay be used to communicate with one or more wired networks. In some embodiments, a wired network interface may be present, such as to allow communication with a local area network (LAN). Communication interfacemay also be used to communicate with distributed services across multiple virtual machines through a virtual network. Communication interfacemay be used by one or more of the other processes in order to communicate with the other process or with external devices and services such as mobile device, emissions data system, or smart thermostat.

Event schedulermay implement the business logic for scheduling EDR events. For example, event schedulermay request and receive data from constraints engine, historical data engine, and forecast engineto determine when to schedule an EDR event in order to generate a reduction in carbon emissions. Event schedulermay also receive an emissions rate forecast for a future time period from emissions data system. In some embodiments, event schedulermay use the emissions rate forecasts to identify an emissions rate event. A future emissions rate event may be any period of time in the future when the emissions rate is expected to be at an increased or decreased level, as described further herein below. In some embodiments, event scheduleruses the emissions rate forecasts to calculate one or more emissions differential values. An emissions differential value may be understood as the rate of change of carbon emissions at any given point in time. For example, using the emissions rate forecasts, the event schedulermay calculate an emissions differential value for each of a plurality of points in time during the future time period covered by the forecast. In some embodiments, event schedulerdetermines an event score for an EDR event ending at each of the plurality of points in time. Based on the emissions differential values and the event scores, event schedulermay generate and schedule EDR events to be sent to smart thermostator any other smart appliance. Event schedulermay also modify or cancel previously generated and scheduled EDR events based on updated emissions rate forecasts. In some embodiments, constraints produced by constraints enginemay restrict the generation of EDR events by event scheduler.

Constraints enginemay create and maintain one or more constraints intended to ensure that EDR events scheduled by event schedulerproduce the least amount of user discomfort and annoyance. For example, constraints enginemay limit the number of events scheduled for a day. In some embodiments, constraint enginemay also limit the number of a specific type of event per day. Constraints enginemay limit the generation of events during restricted times of day. For example, constraints enginemay limit the generation of an EDR event when a user may be asleep or at home. In some embodiments, constraints enginedefines a minimum score required for any EDR event scheduled by event scheduler. Constraints enginemay also define a minimum amount of time between scheduled EDR events. For example, constraints enginemay require a minimum amount of time between the end of one event and the beginning of the next event of the same or a different type. In some embodiments, constraints enginerequests user account-specific data from user management moduleto define user account-specific constraints. For example, user management modulemay indicate that a specific user account always cancels EDR events of a certain magnitude, in which case, constraints enginemay define a constraint for the specific user account restricting event schedulerfrom scheduling events for that user account with a greater magnitude than the user account has indicated a willingness to tolerate.

Historical data enginemay include processes for analyzing historical data and metrics. For example, historical data enginemay periodically or occasionally analyze historical emissions rates to help predict when emissions rates will rise or fall again in the future. Historical data enginemay also analyze historical data collected from various user devices. For example, historical data enginemay record and store the effectiveness of an HVAC system associated with a user account. The effectiveness may in turn be used by event schedulerto identify optimal EDR events for the user account based on the effectiveness of the HVAC system. In some embodiments, data analyzed by historical data engineis stored in one or more databases of the cloud-based power control server system, such as emissions rate database.

User management modulemay include one or more processes for managing user accounts. For example, user management modulemay access, modify, and store account details for a specific user account such as information for one or more devices owned and operated by a user associated with the account, various settings for programs a user account may be participating in and to what extent, payment methods, setpoint temperature preferences, or user account habits. User management modulemay provide user account-specific information to constraints engineto generate user account-specific constraints and restrictions. User management modulemay also provide user account-specific information to event schedulerto help determine what events to schedule and when based on preferences associated with the user account. In some embodiments, user management modulemay also send communications to a user associated with a user account, such as notifications or updates, or to an application on a mobile deviceassociated with the user account. For example, user management modulemay send an email, text, or application invitation to a specific user account to participate in future EDR program events.

Forecast enginemay include one or more processes for analyzing, modifying, or generating emissions rate forecasts. Forecast enginemay receive emissions rate forecasts from emissions data systemor emissions rate database. In some embodiments, forecast enginemodifies the received emissions rate forecasts using data produced by historical data engineor other historical data from one or more databases such as emissions rate database. For example, after receiving an emissions rate forecast from emissions data system, forecast enginemay modify the forecast based on a combination of weather forecasts and historical emissions rates for times with similar weather. Forecast enginemay also generate independent emissions rate forecasts using a combination of historical emissions rates. In some embodiments, forecast engineanalyzes emissions rate forecasts and determines emissions differential values that event schedulercan use to generate EDR events.

One or more databases, such as emissions rate database, may store or otherwise make data accessible to cloud-based power control server system. Emissions rate databasemay include data associated with historical and predicted emissions rates. The historical emissions rate data may include both the recorded emissions rates measured by utility companies or third-party services for a city or region and old forecasts covering the recorded period of time. For example, if emissions rate databasestores the recorded and old forecasts, historical data enginemay analyze these sets of data to determine the accuracy of future forecasts. The predicted emissions rates may be one or more emissions rate forecasts covering the same or overlapping periods of time. By retaining multiple emissions rate forecasts covering the same or overlapping periods of time, historical data engineor any other analytical process may compare the forecasts and determine trends in the forecast as they approach real time. For example, a first forecast may predict a high rate of emissions at 24 hours into the forecast; a later forecast (e.g., 12 hours later) may revise the prediction indicating that the rate of emissions at the same point in time (e.g., now 12 hours into the forecast) will not be as high. If this trend is identified over enough emissions rate forecasts, forecast enginemay modify future forecasts to more accurately predict the future emissions rate. Cloud-based power control server systemmay include other databases for various purposes. For example, there may be a user database storing information specific to individual user accounts such as account details, program participation settings, HVAC system characteristics, setpoint temperature preferences etc. The one or more databases, including emissions rate database, may be implemented by one or more suitable database structures such as a relational database (e.g., SQL) or a NoSQL database (e.g., MongoDB).

Processing systemcan include one or more processors. Processing systemmay include one or more special-purpose or general-purpose processors. Such special-purpose processors may include processors that are specifically designed to perform the functions detailed herein. Such special-purpose processors may be ASICs or FPGAs which are general-purpose components that are physically and electrically configured to perform the functions detailed herein. Such general-purpose processors may execute special-purpose software that is stored using one or more non-transitory processor-readable mediums, such as random access memory (RAM), flash memory, a hard disk drive (HDD), or a solid state drive (SSD) of cloud-based power control server system.

illustrates an embodiment of a smart thermostat systemfor managing EDR events. Smart thermostat systemcan include smart thermostat; network; cloud-based server system; and backplate. Cloud-based server systemmay function as described in relation to, supra. Networkmay function as described in relation to, supra. Emissions data systemmay be connected to cloud-based server systemand may function as described in relation to, supra. Smart thermostatcan include: electronic display; touch sensor; network interface; event scheduler; constraint engine; ambient light sensor; temperature sensor; HVAC interface; housing; and cover.

Electronic displaymay be visible through cover. In some embodiments, electronic displayis only visible when electronic displayis illuminated. In some embodiments, electronic displayis not a touch screen. Touch sensormay allow one or more gestures, including tap and swipe gestures, to be detected. Touch sensormay be a capacitive sensor that includes multiple electrodes. In some embodiments, touch sensoris a touch strip that includes five or more electrodes.

Network interfacemay be used to communicate with one or more wired or wireless networks. Network interfacemay communicate with a wireless local area network such as a WiFi network. Additional or alternative network interfaces may also be present. For example, smart thermostatmay be able to communicate with a user device directly, such as by using Bluetooth®. Smart thermostatmay be able to communicate via a mesh network with various other home automation devices. Mesh networks may use relatively less power compared to wireless local area network-based communication, such as WiFi. In some embodiments, smart thermostatcan serve as an edge router that translates communications between a mesh network and a wireless network, such as a WiFi network. In some embodiments, a wired network interface may be present, such as to allow communication with a local area network (LAN). One or more direct wireless communication interfaces may also be present, such as to enable direct communication with a remote temperature sensor installed in a different housing external and distinct from housing. The evolution of wireless communication to fifth generation (5G) and sixth generation (6G) standards and technologies provides greater throughput with lower latency which enhances mobile broadband services. 5G and 6G technologies also provide new classes of services, over control and data channels, for vehicular networking (V2X), fixed wireless broadband, and the Internet of Things (IoT). Smart thermostatmay include one or more wireless interfaces that can communicate using 5G and/or 6G networks.

Event schedulermay implement the business logic for performing EDR events. For example, event schedulermay receive information associated with an EDR event generated by cloud-based server systemfor smart thermostat. Event schedulermay then convert the information into instructions to be executed at the appropriate time for the EDR event. In some embodiments, event schedulergenerates and schedules EDR events from emissions rate forecast data. For example, event schedulermay request and receive emissions rate forecasts from cloud-based server systemin order to determine when to schedule an EDR event in order to generate a reduction in carbon emissions. Using the emissions rate forecasts, the event schedulermay identify a future emissions rate event. A future emissions rate event may be any period of time in the future when the emissions rate is expected to be at an increased or decreased level, as described further herein below. In some embodiments, event scheduleruses the emissions rate forecasts to calculate an emissions differential value for each of a plurality of points in time during the future time period covered by the forecast. In some embodiments, event schedulerdetermines an event score for an EDR event ending at each of the plurality of points in time. Based on the emissions differential values and the event scores, event schedulermay generate and schedule EDR events to be run at a later time. Event schedulermay also modify or cancel previously generated and scheduled EDR events based on updated emissions rate forecasts. In some embodiments, constraints produced by constraints enginerestrict the generation of EDR events by event scheduler.