Patent Application
20250035330
The present disclosure relates to systems and methods for thermostat controls for HVAC (Heating Venting and Air-Conditioning) which can simplify operation and improve efficiency.
Thermostats are the control mechanism for HVAC that allows one to set the temperature for a given room and thus regulate the functioning of the HVAC unit that serves that room. Historically, these have been mounted onto a wall in the space that is being cooled and tied to a temperature sensor so that the compressor will turn on when the temperature of the room exceeds a pre-established threshold.
Increasingly common has been the introduction of smart thermostats that are able to set programs such as nighttime and daytime usage, essentially allowing one to program a series of setpoints that can save energy by using the equipment less when the room in unoccupied or if occupants are sleeping.
Additionally, commercial establishments often control the programming of the thermostats from central locations, so that they have the ability to set and regulate temperature and operational times and thus the resultant energy use. This may include settings such as pre-cooling or warming a space a few minutes before a shift is to start, and making sure that temperature settings are within an expected company set norm.
While it is interesting to have the ability to control these thermostats remotely, each location may have multiple units to be set. The thermostat units may be from different manufacturers and may require different commands and may have to be accessed through different interfaces. Further, the capabilities across devices may vary. Variations may be in the number of available set-points, or the minimum time interval between settings.
The number of variations may be quite large for those that own or manage multiple franchises or establishments. Energy management companies that oversee large groups of entities may face even more variations in manufacturers, installation types, and even occupancy profiles which must be managed.
Further, while the program settings may be typically set to run autonomously, there may be exception periods where one wants to manually override the standard settings due to external variables such as unexpected weather or variations to schedules.
Take the example of a weather-related closure, where we may want to override the standard heating or cooling algorithm for the day knowing that the location will be closed due to weather. Similarly, in the example of a heat wave, we know that it takes too much time with the normal operating program to cool the space sufficiently for opening time and we will want to start a pre-cooling sequence earlier.
Other examples are related to changes in operating hours. There may be times when staff are asked to stay additional hours to do inventory, or to prepare for a sale. Unexpected closures due to maintenance or other unforeseen circumstances will also merit change to the normal schedules and set points.
In such cases, it is clear that adjusting the energy settings would lead to savings, even substantial savings when it comes to large energy consumers such as HVAC. However, the overriding of standard system programming which is in place can be a laborious and error prone process and is rarely undertaken. These energy savings opportunities are often lost.
Further, the thinking may often be that the energy is already budgeted. Those operating or managing the franchise or establishment may not feel these changes are part of their job description or may be uncomfortable making them. The fear of making changes that may affect the normal operating process discourages users from making any changes at all. No one wants to be the one that, while well intentioned, turned off the heat for the next day when the shift came in causing disruptions, complaints, and potential lost revenue.
In many cases, the corporate settings may even disable the ability to make local changes and well-intentioned users may be locked out of the system unable to make these changes even if they wanted to do so.
U.S. Pat. No. 11,454,410 B2 from Maruyama addresses geographical location management of HVAC by a central unit to maintain comfort levels but fails to address the control aspect of multiple manufacturers or the smart grouping of devices beyond geographical regions.
U.S. Pat. No. 8,674,816 B2 from Trundle teaches techniques for providing remote device (e.g., thermostat, lighting, appliance, etc.) control and/or energy monitoring by monitoring sensor data and setting rules to control the devices accordingly. While Trundle addresses occupancy related controls it does not address the idea of addressing groups of equipment across various manufacturers or any smart or hierarchical grouping aspects of the equipment.
Thus, it is desirable to have a system and method for controlling, and grouping by various criteria of, a wide variety of thermostats and/or other energy using/control devices.
What is desired then is a system and method that can improve the ability to address groups of thermostats or other devices in smart ways encompassing geographic locations, equipment types, and location specific details and tags in order to send remote commands to these groupings of thermostats in a simple template like fashion.
It is further desired to provide a system and method that can provide a standardized simple set of commands to configure thermostat settings in a uniform way regardless of manufacturer or existing interface or programming method.
It is still further desired to provide a method by which overriding commands and settings can be applied to general programming in a way that the general programming is resumed after the specified period.
It is still further desired to provide a simple and uniform way of collecting operational data, values, as well as status and error information from the varied group of sensors and thermostats normalizing this data in such a way that it can be used to adjust programming.
A system that allows a temporary override setting which can be easily programmed for the affected areas or stores and would automatically resume the normal programming after a predetermined period is desirable.
The ability to group devices and locations using geographic or other smart grouping data using user defined attributes and to address devices regardless of manufacturer or programming interface is also desirable.
A system that allows commands such as “start cooling all the stores in southern California one hour earlier for the next two days” as opposed to the complexity of having to access each of many systems on one of many platforms individually is desirable.
While location is just one of many possible smart groupings, another may be related to device brand, and location type. For example, units that have specific capabilities (e.g. Carrier units) for locations that have one or more outside facing walls, that belong to a certain franchise or specific owner. Even those that have subscribed to a program of energy savings can be addressed along with a myriad of other possibilities. For example, “lower the temperature of all Subway stores belonging to John Smith that are in standalone buildings for the next two days”. A system that provides such control in a simplified fashion would take away the complexity of addressing multiple systems and selecting individual locations or devices at individual locations.
It would thus be highly desirable to have a system and method of controlling groups of thermostats from a variety of manufacturers with a simplified single command language regardless of system or manufacturer used.
In one configuration, a system of entering commands whereby commands specify addressing information such as location or other location specific tags or equipment type tags, or combinations thereof are specified as the recipients for such configuration settings.
In yet another configuration, a template or profile is used to commonly configure settings which can be sent to thermostats which includes override settings and time information for when the override should be active.
In yet another configuration, devices are pre-configured with settings at the factory before being sent to the field to be installed which will include addressing information such as categorization as well as control settings and timing for general operation.
In yet another configuration, an interface is provided for entering commands addressing the varied group of thermostats in order to send configuration data via means of a template to the given devices.
In yet another configuration, a programming interface is provided for entering commands addressing the varied group of thermostats in order to send configuration data using programmatical interfaces with systems that automatically manage and send this configuration data.
A normalization layer is provided whereby the simplified commands received are modified and adapted to a format which may be specific for one or more of the thermostats being addresses so that the provided API (Application Programming Interface) of the thermostats can perform the desired function(s).
The normalization layer accesses a data store of information about the thermostats which includes the thermostat version and type along with site specific location such as geographical location, information about the site, and various attributes or tags that can be used to select and search for the particular locations. Some sample attributes include address, type, manufacturer, version, last service date, building specific information, business information, plan specific information.
While the intended purpose for the system is primarily to improve efficiency of energy use by adjusting thermostat settings proactively in anomalous situations such as unforeseen closures or extreme weather events, the system may also be used for fine tuning locations for improved efficiency.
For example, if the current programming turns thermostats on one hour before the shift starts, but we find it only takes 15 minutes to warm or cool the site to a comfortable operating temperature, we may adjust the programming in certain locations. We may find that some locations which have more energy efficient windows or good insulation may be good candidates for 15 minutes of pre-cooling whereas other locations may require more pre-cooling to achieve an optimal start/stop setting. The system applies machine learning to detect the cooling periods and the efficiency of the locations by monitoring the variables such as indoor temperature, setpoint, outdoor temperature, humidity, sunlight, and time.
Such intelligence can also be applied to adjust settings when the site measures data showing that the current programming does not create the desired changes or settings which would be ideal. Temperature and humidity sensors are read and correlated with occupancy and time data with knowledge of schedules. Attempts to override temperature settings locally and/or complaint buttons suggesting the site is too warm or cold are used as inputs for the system to pass back data for potential reprogramming using machine learning and intelligence.
The selection criteria are only limited by the granularity of the data configured in the data store of attributes about the devices and locations.
Therefore, these and other objects are achieved by providing a system for distributing commands to multiple energy control devices. The system includes a computer having software executing thereon. The computer is in communication with a plurality of energy control devices via a network connection, at least two of said plurality of energy control devices are located in different physical buildings and different geographical locations. The software is configured to receive an indication of a grouping of a set of multiple energy control devices which include said at least two of said plurality of energy control devices. The software is further configured to receive at least one command input for said set of multiple energy control devices. The software is further configured to provide control instructions to each of the multiple energy control devices which modify a control setting of each of the multiple energy control devices to modify energy usage of a plurality of energy usage devices, each energy usage device associated with one of said multiple energy control devices.
In certain aspects the software includes a normalizing feature which translates the at least one command input into a control instruction for each of the multiple energy control devices, said control instruction is formatted based on a type indication associated with each of the multiple energy control devices such that the control instructions provided to each of the multiple energy control devices are formatted to be compatible with the corresponding one of the multiple energy control devices to which said control instruction is sent. In other aspects the at least one command input is indicative of a state for an HVAC unit. In other aspects the at least one command input is indicative of a temperature set point for said set of multiple energy control devices. In yet other aspects the software is configured to present one or more filter options which are user selectable to generate said indication of a grouping. In still other aspects the at least one command input is indicative of a desired temperature at a desired time and said software generates said control instructions for each of the multiple energy control devices such that control instructions for at least two of the multiple energy control devices are different. In yet other aspects the software has access to history data indicative of historical environmental conditions for each of the different physical buildings and further indicative of one or more rates of change of temperature within each of the different physical buildings correlated to heating, ventilation and/or cooling inputs. In yet other aspects the software is configured to receive response data indicative of an actual change in temperature within each of the different physical buildings associated with one or more of the control instructions associated with that physical building. In still other aspects the software updates the history data based on the response data. In yet other aspects the at least one command input is indicative of a desired temperature at a desired time and said software generates said control instructions for each of the multiple energy control devices based on the history data such that control instructions for at least two of the multiple energy control devices are different. In still other aspects the software provides one or more filters for indication of the grouping, the filters selected from the group consisting of: owner, manager, city, state, town, zip code, geographical region, county, type of location, building type, location name or brand, window count (and/or area), exposed wall count (and/or area), presence of drive through, solarium seating presence, opening hours, plan subscription, category of device and combinations thereof. In yet other aspects the at least one command input is indicative of the group consisting of: temperature, period of time, on/off status and combinations thereof.
Other objects are achieved by providing a method for distributing commands to multiple energy control devices including one or more of the steps of: providing communications with a plurality of energy control devices via a network connection, at least two of said plurality of energy control devices are located in different physical buildings and different geographical locations; receiving an indication of a grouping of a set of multiple energy control devices which include said at least two of said plurality of energy control devices; receiving at least one command input for said set of multiple energy control devices; providing control instructions via said network to each of the multiple energy control devices which modify a control setting of each of the multiple energy control devices to modify energy usage of a plurality of energy usage devices, each energy usage device associated with one of said multiple energy control devices.
In certain aspects the method includes translating the at least one command input into a control instruction for each of the multiple energy control devices, said control instruction is formatted based on a type indication associated with each of the multiple energy control devices such that the control instructions provided to each of the multiple energy control devices are formatted to be compatible with the corresponding one of the multiple energy control devices to which said control instruction is sent. In further aspects the at least one command input is indicative of a desired temperature at a desired time and further comprising generating said control instructions for each of the multiple energy control devices such that control instructions for at least two of the multiple energy control devices are different. In still further aspects includes accessing history data indicative of historical environmental conditions for each of the different physical buildings and further indicative of one or more rates of change of temperature within each of the different physical buildings correlated to heating, ventilation and/or cooling inputs. In other aspects method includes receiving response data indicative of an actual change in temperature within each of the different physical buildings associated with one or more of the control instructions associated with that physical building. In still other aspects the method includes updating the history data based on the response data. In yet other aspects the at least one command input is indicative of a desired temperature at a desired time and said software generates said control instructions for each of the multiple energy control devices based on the history data such that control instructions for at least two of the multiple energy control devices are different. In still other aspects the at least one command input is indicative of a desired temperature at a desired time and further comprising generating said control instructions for each of the multiple energy control devices based on the history data such that control instructions for at least two of the multiple energy control devices are different. In still other aspects the response data is received from one or more of the multiple energy control devices.
While the focus in the descriptions and the examples used herein relate to thermostats and HVAC, the system can also be easily adapted to other energy using devices such as lighting. In such cases commands such as turning on/off exterior lighting, dimming lighting, and setting lighting controls in conjunction with operating hours can also be done through simplified commands such as ‘turn on all outdoor lights earlier for all stores in a given location’.
Refrigeration is another area which would benefit greatly from such remote control whereby setpoints for interior temperatures of freezers and refrigerators can also be set to coincide with peak energy demands an defrost cycles can be postponed or adjusted according to operating hours or even external temperature variances.
Other aspects and features will become apparent from consideration of the following description taken in conjunction with the accompanying drawings.
The drawings illustrate example embodiments in which:
Example embodiments as described herein improve the efficiency of configuring remote devices from multiple manufacturers based on intelligent groupings such as geographical region, business entity, or by other user defined selections. Control data, or profiles are normalized across vendors and an addressing scheme is developed to address groupings of devices through a set of simplified commands. The description below mainly relates to thermostat devices and adjusting those devices, however it is understood that disclosure is not intended to be limiting to thermostats as other devices and categories of devices can be adjusted (e.g. lighting or e.g. thermostats specific to certain device categories, etc).
Turning to the drawings,
In
Similarly, commands that are sent (110 or 160) to the normalization layer (100) addressed to logical grouping 2 (220) will be sent to the location 3 (225) thermostats.
While two distinct logical groupings are illustrated, it should not be construed as limiting. There may be overlapping groups and thermostats can be included in multiple groups. For example, a logical mapping of thermostats by a manufacturer may span multiple locations forming just a small subset of thermostats at a given location. Thermostats in a geographic region such as a zip code may encompass multiple manufacturers. These should be considered more like search criteria over a given data set. The commands may also be based on device category, such as refrigerators, air conditioning, heater, lighting, water devices, combustion fuel devices, ovens, microwaves, grills, fryers, venting or any other similar type of category of devices. There may be multiple brands/manufactures of these devices within the same category. It is further understood that thermostats is a non-limiting example of one implementation of the present system and method, other energy control/monitoring and use devices may be controlled using the systems and methods described herein.
In
In
Examples may include a storm coming to a particular geographical area where the address can be used for selection. If extreme weather is included in the event, the number of windows, exposed walls, and whether or not solarium seating is incorporated into the establishment may affect the actions taken or the timing of those actions. Further, the remote control and adjustments may be only for those locations that have opted into a plan that includes such dynamic changes and thus the parameter Plan Info is included to select appropriately. Finally, a link to a table of thermostats is included in the location info which directs the system to address the thermostats in the location.
Table 2 (430) of
In terms of plan subscriptions, it is possible that certain users may wish to allow larger variations in their settings if useful for saving money, for example, setting temperatures colder in the winter or warmer in the summer. The location's plan subscription may allow these larger variances, and thus the system may enable filters to categorize by plan subscription allowing these savings and the filter may allow a selection of a savings amount or increase in savings. For example, if adjusting the setting by 4 degrees would save 10% but another degree would save 12%, the plan subscription for certain locations may allow this type of setting adjustment, thus a filter by subscription can allow all these devices to be addressable based on having a common plan subscription. The filters may also be user defined such that additional filters can be added and then once added, selectable by other users to categorize locations/devices in the same/similar way.
It will be understood by those of skill in the art that while examples using thermostats and HVAC are utilized, the same system and method can be adapted and used for any temperature control-based systems including heating, water heaters, and other such systems as well as many other energy usage devices. For example, the present system may be useful in other monitoring/control devices which control energy usage such as refrigeration, lighting, cooking and a variety of other devices.
While the disclosure is susceptible to various modifications, and alternative forms, specific examples thereof have been shown in the drawings and are herein described in detail. Is should be understood however that the disclosure is not limited to the particular forms or methods or embodiments disclosed.
| Number | Date | Country | |
|---|---|---|---|
| 63528956 | Jul 2023 | US |
