Patent Publication Number: US-2019195525-A1

Title: Method and apparatus for operating heating and cooling equipment via a network

Description:
The present disclosure relates to a method and apparatus for operating heating and cooling equipment via a communications network, e.g., a communications network of a network service provider. 
     BACKGROUND 
     An enterprise may have a large number of equipment deployed in various locations, e.g., various buildings. Each building may contain any number of heating and cooling equipment, e.g., Heating, Ventilation, and Air Conditioning (HVAC) units, humidifiers, etc. HVAC units are equipment used for providing control of temperature and indoor air quality. Typically, building operations personnel select settings for an indoor temperature, a humidity level, an amount of ventilation, etc., via a controller of a building management system. 
     When the HVAC unit is working properly, the actual temperature and quality of indoor air should closely correlate with the environmental parameters set by the building operations personnel via the building management system. One method of monitoring the efficiency and health of the HVAC unit is to gather and analyze the HVAC unit over a period of time. 
     SUMMARY OF THE DISCLOSURE 
     In one embodiment, the present disclosure teaches a method and apparatus for operating cooling and heating equipment via a communications network. For example, the method receives data that is captured by at least one sensor deployed at a location of a plurality of locations, wherein the data is associated with at least one parameter of an equipment, wherein the equipment is deployed at the location, receives atmospheric data for the location, receives utility rate data for the location, monitors the at least one parameter of the equipment, the atmospheric data, and utility rate data for the location, determines for the at least one parameter of the equipment, a remedial analytics is needed to be performed, when a deviation from a baseline is detected that is greater than a threshold for a maximum deviation from the baseline that is established for the at least one parameter, performs the remedial analytics, and generates a recommendation for a remedial action based on a result of the remedial analytics that is performed. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The teaching of the present disclosure can be readily understood by considering the following detailed description in conjunction with the accompanying drawings, in which: 
         FIG. 1  illustrates an example network related to the present disclosure; 
         FIG. 2  illustrates a flowchart of an example method for operating heating and cooling equipment via a network; and 
         FIG. 3  illustrates a high-level block diagram of a computing device specially programmed to perform the functions described herein. 
     
    
    
     To facilitate understanding, identical reference numerals have been used, where possible, to designate identical elements that are common to the figures. 
     DETAILED DESCRIPTION 
     The present disclosure relates to a method and apparatus for operating heating and cooling equipment via a communications network, e.g., a communications network of a network service provider. The teachings of the present disclosure may be applied via any type of wired or wireless communications network. 
     As described above, monitoring the efficiency and health of heating and cooling equipment can be provided for a large number of equipment, e.g., a large number of HVAC units, humidifiers, etc., at various equipment sites distributed over very large geographic areas, e.g., across multiple states. One way for managing the large number of equipment is by performing a scheduled maintenance in a predetermined interval, e.g., annually, semi-annually, quarterly, monthly, etc. Furthermore, remedial analytics can also be performed in a predetermined interval, e.g., annually, semi-annually, quarterly, monthly, etc. to predict potential equipment failures. 
     As a result, ensuring the equipment are operating as expected will require frequent scheduled visits to the various equipment sites. For example, maintenance personnel may be periodically dispatched to each equipment site irrespective as to whether a problem actual exists at the equipment site. During some scheduled visits, the maintenance personnel may identify issues and perform repairs, whereas during some other visits, the maintenance personnel may find that all of the equipment are functioning as expected and no repair is required. This method of sending maintenance personnel to offsite locations to perform scheduled maintenance does improve reliability of the equipment but at great cost, e.g., performing preventive replacement or maintenance of equipment when no failure is actually detected. A review of maintenance records over a number of years may reveal that the frequent scheduled dispatches were probably unnecessary. 
     One way to reduce unnecessary dispatches of maintenance personnel is to wait for the equipment to actually fail or to severely deviate from designed performance expectations. For example, a circuit card deployed in the HVAC system may have failed and the HVAC system can no longer maintain a temperature set for a particular environment. When this condition is determined by a tenant of a building who may report the failure, maintenance personnel may be sent to address the failure, e.g., replacing the failed circuit card. Although this approach solves the problem of unnecessary dispatches of maintenance personnel, it often creates significant inconvenience for the occupants of the building and potentially incurs the loss of operational efficiency for a long period of time prior to the actual failure. Namely, the faulty piece of equipment may have been underperforming for some time prior to the actual failure event, thereby incurring a greater amount of energy cost in its operation. 
     The present disclosure provides a method for operating heating and cooling equipment via a communications network in an approach that utilizes a large amount of data condensed down to describe equipment behavior against relational variables such as weather data, hours of operation and local utility rates. For example, the operational performances of HVAC equipment in all modes of operation against benchmarks or baselines are continually monitored. This approach will allow the method to quickly detect operational deviations and to recommend a remedial action to be taken, e.g., a corrective action to be taken by the responsible organization e.g., operational expense management for repair and maintenance vs. capital expense management for efficiency improvements. 
     In one embodiment, the baseline for a parameter of the one or more parameters of the equipment is generated based on one or more measurable aspects of the equipment and atmospheric data for a location at which the equipment is deployed. The location comprises a geographical location (e.g., a physical location). Furthermore, such data is collected and analyzed over a large geographical area, e.g., across multiple states. 
     In one embodiment, each parameter of the one or more parameters of the equipment is a parameter that is defined for tracking at least one measurable aspect of the equipment. In one embodiment, each measurable aspect of the at least one measurable aspect is indicative of one or more of: an efficiency (e.g., in terms of financial cost or consumed power) of the equipment in performing at least one task of the equipment, a likelihood of a potential failure of the equipment to perform at least one task of the equipment, and a degradation of an ability of the equipment in performing at least one task of the equipment. 
     For an illustrative example, suppose the equipment is an air conditioner. Then, the tasks of the air conditioner may comprise: cooling a room and reducing a humidity level of a room. A measurable aspect of the equipment may be for identifying a symptom of inefficiency as to performing at least one of the tasks in the list of tasks of the air conditioner, a degradation of efficiency as to performing at least one of the tasks in the list of tasks of the air conditioner, or a likelihood of a potential failure to perform at least one of the tasks in the list of tasks of the air conditioner. The parameter may then be defined for tracking how efficiently the air conditioner is able to perform the task of cooling a room or reducing a humidity level of a room. 
     For example, suppose an air conditioner operating optimally runs 25% of the time to cool a room to 78° F. when the outside temperature is 90° F. Suppose, as the air conditioner becomes less efficient, the air conditioner starts running for longer periods of time. For example, it runs 50% of the time to cool the same room to 78° F. when the outside temperature is 90° F. Alternatively, the inefficiency may also be measured in terms of the increased in financial cost or consumed energy to cool the same room to 78° F. when the outside temperature is 90° F. In this example, the percentage of time that the air conditioner is running (or alternatively financial cost or consumed units of energy) may be an appropriate measureable aspect of the air conditioner for identifying a symptom of inefficiency as to the performing of the task of air conditioning. Then, a parameter may be defined for measuring and tracking the percentage of time the air conditioner is running. As the value of the parameter that is defined for the percentage of time the air conditioner is running reaches a predetermined threshold, e.g., the air conditioner is running 50% of the time, maintenance may be performed on the air conditioner prior to an actual failure of the air conditioner. Alternatively, a different mode of operation can also be recommended, e.g., running the air conditioner earlier in the morning to take advantage of a lower utility rate in the early morning hours and so on. 
     As described above, the equipment may have to perform any number of tasks. For example, an HVAC system may be used to perform the tasks of: air conditioning, heating, and air ventilating. Accordingly, a particular parameter may be defined, for the equipment, for tracking and indicating how efficiently the equipment performs each particular task. For instance, for the HVAC system, a first parameter may be for indicating how well a condenser of the HVAC system is able to perform the task of air conditioning, a second parameter may be for indicating how well a heat pump of the HVAC system is able to perform the task of heating, a third parameter may be for indicating how well a ventilation component of the HVAC system is able to perform the task of keeping the indoor air quality in a desired range, and the like. 
     In one embodiment, the one or more measureable aspects of the equipment are determined from various sources that comprise one or more of: a manufacturer of the equipment, a manufacturer of a component of the equipment, a subject matter expert, a utility entity, and an agency, e.g., a government agency. In one example, data for predicting a failure may be identified by a manufacturer of the equipment. For instance, the manufacturer may identify an increase in a coolant temperature of an HVAC system, an increase in a humidity level measured near the HVAC system, and a carbon dioxide level measured near the HVAC system, as appropriate measurable aspects for predicting a failure of the HVAC system. The measurable aspects of the equipment may then be the coolant temperature, the humidity level near the HVAC system, and the carbon dioxide level near the HVAC system. 
     In another example, a government agency may be tasked with providing guidance and/or standards for an indoor air quality. For instance, in the United States of America, the Environmental Protection Agency (EPA) sets standards for indoor air quality to ensure that biological, chemical and particulate levels in buildings are at levels that would not impact the satisfaction, productivity and health of the occupants. The measurable aspects of the equipment in buildings may then be based on the air quality standards set by the agency providing the guidance and/or standards, e.g., the EPA. For an illustrative example, suppose the EPA sets, for an indoor space, a maximum of 9 ppm for an average carbon monoxide level over a 24 hour period. Then, the measurable aspect is the carbon monoxide level. One or more sensors may be used to take measurements of carbon monoxide levels in a building. The carbon monoxide levels that are observed may be provided to a server in a predetermined time interval. For example, the carbon monoxide levels may be provided to an application server of a network service provider, e.g., every 10 minutes, every hour, every four hours, every day, etc. The average of the carbon monoxide levels may then be computed for a desired period of time, e.g., a period of 24 hours. If the average of the carbon monoxide levels that is computed for the 24 hour period exceeds the 9 ppm, a maintenance action may be taken. For example, maintenance personnel may be dispatched to identify a cause for the excessive level of carbon monoxide in the building and/or perform a remedial action. 
     In another embodiment, a utility entity, e.g., a power company, an electricity company, a natural gas company, a solar power company, a wind power company, and the like may be tasked with providing guidance and/or standards for utility rates, e.g., different pricing during each season, each month, each week, each day, each part of the day (e.g., morning, afternoon, evening and so on), each hour and so on. In another example, the utility entity may provide a rate when power is predominately generated using natural gas or nuclear power, whereas another rate may be provided if backup coal fired power plants need to be brought on line due to unexpected demands or failure of other energy generating sources. In another instance, the utility entity may provide energy consumption rate noted historically or on a real time basis as to a particular geographical region (e.g., a state or a group of states), as to a particular area in a city or a town, as to a particular building or building type, as to a particular type or a size of HVAC system deployed in a building and the like. This information or performance data will allow remedial analytics to be performed by the present method. 
     As described above, for each equipment of the one or more equipment, the one or more measureable aspects of the equipment are determined from various sources. For each equipment at a location, once the one or more measurable aspects of the equipment are known, the enterprise may deploy one or more sensors for measuring each of the one or more measurable aspects of the equipment. For example, the enterprise may have deploy at the location one or more: thermometers for measuring temperature, hygrometers for measuring humidity, CO 2  meters for measuring carbon dioxide levels, CO meters for measuring carbon monoxide levels, an anemometer for measuring wind speed, and the like. A thermometer may be for capturing a temperature of a space (e.g., room) or a coolant of the equipment itself. In one embodiment, the enterprise may also deploy one or more other sensors to capture energy consumption, run times of each condenser, run times of each heat pump, volume of air flow per given time period, and the like. For illustrative example, suppose a building has two HVAC systems with a temperature of a coolant being identified as being a measurable aspect for each of the two HVAC systems. Then, for each of the two HVAC systems, the enterprise may deploy a thermometer for sensing a temperature of the coolant of the respective HVAC system. 
     When deployment of one or more equipment and the one or more sensors is completed for the location, the network service provider is provided with information about the one or more equipment and the one or more sensors that are deployed at the location. The information about the one or more equipment and the one or more sensors at the location may comprise one or more of: an address of a building, a latitudinal and longitudinal coordinate of the location, an altitudinal location (if applicable), electricity rate for the location, a list of the one or more equipment, a size and a capacity of each equipment, a number of units of each equipment, a lead unit designation (if applicable), a list of the one or more sensors, and information about each sensor. 
     In one embodiment, the information about a particular sensor of the one or more sensors at the location may comprise one or more of: a type of power source used by the particular sensor, and a type of communication used by the particular sensor for transmitting data captured by the particular sensor. For example, the particular sensor may transmit captured data to a server, e.g., an application server of a network service provider, via an access network, e.g., via a cellular network or a Wi-Fi access point. In another example, the particular sensor may transmit data captured by the particular sensor to an application server of a network service provider via a dedicated local area network (LAN). In another example, the particular sensor may transmit data captured by the particular sensor to an application server of a network service provider via a local gateway server. The local gateway server can be deployed in the vicinity of the particular sensor. For example, the particular sensor and the local gateway server may be in a same building. 
     In one embodiment, the network service provider or the enterprise determines a time frequency of capturing data via the one or more sensors. For example, the frequency of capturing data (i.e., performing the sensing) may be every ten minutes, every hour, etc. For illustration, a thermometer may capture a temperature of a coolant or a room every ten minutes and provide the captured temperature (i.e., temperature data) to a server, e.g., to a dedicated application server of a network service provider. In one embodiment, the captured data is continuously aggregated over a predetermined time period prior to being provided to the server, e.g., the captured data is stored locally by the sensor for a predefined period of time to minimize the number of communication sessions with the application server. For the example above, the thermometer may capture the temperature of the coolant every ten minutes, and aggregate the temperature of the coolant that is captured for each day. At the end of each day or even in real time, the temperature data that is captured and aggregated for the day may be provided to the server. 
     Each particular sensor has at least one way to provide the data that is captured by the particular sensor to the network service provider. In one embodiment, each particular sensor transmits the data captured by the particular sensor directly to a server, e.g., an application server, located in the communications network of the network service provider. For example, a particular sensor may have an ability to communicate with the server, e.g., the application server, via an access network, e.g., a Wireless-Fidelity (Wi-Fi) network, a cellular network (e.g., 2G, 3G, and the like), a long term evolution (LTE) network, 5G, and the like. 
     In one embodiment, each particular sensor of the one or more sensors transmits the data captured by the particular sensor to a local gateway server. The local gateway server is physically located near the location of the one or more sensors. For instance, the local gateway server and the one or more sensors may be located in a same building, in a same floor, or in a same room. The local gateway server gathers data from the one or more sensors and forwards the data that it gathered from the one or more sensors to a server, e.g., to the application server located in the communications network of the network service provider. For an illustrative example, if a building has five HVAC units and each HVAC unit has ten (10) sensors, it may be beneficial to have each particular sensor of the fifty (50) sensors sending data captured by the particular sensor to a local gateway server in the building. The local gateway server may then provide the data that is gathered from any number of the fifty sensors to the application server located in the communications network of the network service provider. This will again reduce the number of communication sessions with the application server of the network service provider. 
     In one embodiment, the local gateway server provides the data that is gathered from any number of the one or more sensors to the application server in a predetermined time interval. For example, the data that is gathered by the local gateway server may be provided to the application server every four hours, every eight hours, every day, etc. In one embodiment, the local gateway server provides the data that is gathered to the application server upon receiving a query. For instance, the application server may send a query to the local gateway server when preparing to perform an analysis on data captured via the one or more sensors at the location. Thus, the local gateway server is capable of providing the data that is gathered to the application server in a predetermined time interval and/or upon receiving an on-demand query. In other words, the local gateway server is responsive to queries from the application server in addition to being configured to provide the data that is gathered in a predetermined time interval. 
     In some scenarios, there may be a need to have one or more sensors deployed at a location where a power line is not readily available. Moreover, a location may not be appropriate for a connection to a Local Area Network (LAN). Thus, there may be a location where using sensors that require no power line or a connection to a LAN may be more desirable. For such a location, sensors that operate on batteries may be deployed. Then each particular sensor that operates on batteries and has no connection to a LAN may transmit data captured by the particular sensor to the server, e.g., the application server of the network service provider, via a wireless access network, e.g., via a cellular network or a W-Fi access point. 
     As described above, each particular sensor of the one or more sensors transmits the data that is captured by the particular sensor to a maintenance server, e.g., an application server deployed in the communications network of the network service provider. The equipment maintenance is then provided via the maintenance server, e.g., the application server. In order to provide the equipment maintenance in accordance to the teachings of the present disclosure, the application server receives the data that is captured by each of the one or more sensors. The data that is captured by the one or more sensors is associated with at least one parameter of one or more parameters of an equipment, wherein the equipment is deployed at the location. 
     In one embodiment, the operation of receiving the data that is captured by a particular sensor of the one or more sensors is performed via a local gateway server deployed at the location. In one embodiment, the operation of receiving the data that is captured by a particular sensor of the one or more sensors is performed directly, where the receiving of the data that is captured directly comprises receiving the data that is captured without having the data that is captured traversing a local gateway server. For example, the data that is captured may be received from the particular sensor that captured the data via a wireless access network, e.g., a cellular network or a Wi-Fi access point. 
     In one embodiment, the operation of receiving the data that is captured by the particular sensor of the one or more sensors is performed in a predetermined time interval. For example, the operation of receiving the data that is captured by the particular sensor may be performed every minute, every ten minutes, every hour, every four hours, etc. 
     In one embodiment, the receiving of the data that is captured by the particular sensor of the one or more sensors occurs upon sending a query for obtaining the data that is captured by the particular sensor. The query may be sent either to the particular sensor or the local gateway server that gathers the data that is captured by the particular sensor. 
     In one embodiment, a time frequency of capturing the data by the particular sensor of the one or more sensors is determined by the network service provider. In one embodiment, a frequency of receiving the data that is captured by the particular sensor is determined by the network service provider. For illustrative example, the network service provider may determine that temperature needs to be captured every hour, humidity needs to be captured every four hours, and data that is captured by one or more sensors at the location is received every twelve hours via the local gateway server gathering the data at the location, and so on. 
     In addition to the data that is captured by the one or more sensors, the application server also receives atmospheric data for the location at which the one or more sensors are deployed. In one embodiment, a frequency of receiving the atmospheric data for the location is determined by the network service provider. 
     In one embodiment, the atmospheric data comprises one or more of: an outside temperature of the location, an outside humidity level of the location, a wind speed of the location, an amount of sunlight of the location, and an outside air quality of the location. In one embodiment, the outside air quality of the location may specify a level of pollution, a level of a type of gas or chemical, a density of one or more types of particulates, and the like. For example, the level of gas may indicate a level of carbon dioxide in the air. In another example, the level of gas may indicate a level of carbon monoxide in the air. In another example, the level of gas may indicate a level of chlorine gas in the air. In one embodiment, the one or more types of particulates may comprise one or more of: dust particulates, pollen particulates, and particulates defined in terms of their size. For example, the EPA may specify an indoor air quality standard that defines a requirement for ventilation based on a diameter of a type of particulate. For instance, the requirement for ventilation may be different for particulates with diameters less than 10 micrometers versus for particulates with diameters greater than or equal to 10 micrometers. The atmospheric data for the location that is received may then include the types of particulates. 
     In one embodiment, the atmospheric data is received from a weather data source, e.g., a database of a national weather service. For example, in the United States of America, the National Oceanic and Atmospheric Administration (NOAA) maintains a website (e.g., weather.gov) for enabling users to receive weather information for any location in the United States of America. The application server may then receive the atmospheric data for any location in a predetermined interval and store the atmospheric data in a database of the network service provider. When the atmospheric data is needed for analysis, the atmospheric data may then readily be retrieved from the database in which it is stored. 
     In one embodiment, for each equipment of the one or more equipment, the application server utilizes an analytical engine for generating a baseline for each particular parameter of the one or more parameters of the equipment. The analytical engine may comprise a prediction model. The prediction model is trained using historical (i.e., known) atmospheric data and performance data. In other words, from historical records associated with each equipment of the one or more equipment, the prediction model learns the relationship between the atmospheric data for the location at which the equipment is deployed and the performance data of the equipment. For example, the prediction model may learn historically (e.g., using historical data) that on days with a maximum outside temperature of greater than or equal to 90° F., the average percentage of time a particular HVAC system operates is 60%. Similarly, the predication model may learn historically, on days with a maximum outside temperature of 70° F.≤maximum outside temperature &lt;90° F., the average percentage of time the particular HVAC system operates is 45%. Hence, from the historical records associated with the particular HVAC system, a baseline for a percentage of time the particular HVAC system is operating versus a maximum outside temperature (for a day or any portion of a day) may be generated. In addition, such data is compared or aggregated over a large geographical area such as over a number of states. This will allow the prediction model to detect weather patterns that may impact the performance of the HVAC system, e.g., detecting a weather system traversing from the west coast towards the east coast of the United States and tracking its impact of HVAC systems across the country. 
     Similarly, a baseline may be generated for all the other parameters of the HVAC system. For the example described above, the application server may generate: a baseline for a percentage of time the condenser is running during a 24 hour period versus the maximum outside temperature that is observed during the same 24 hour period at the location of the HVAC system, a baseline for an average coolant temperature observed during a 24 hour period versus the maximum outside temperature that is observed during the same 24 hour period at the location of the HVAC system, a baseline for a percentage of time the heat pump is running during a 24 hour period versus the minimum outside temperature that is observed during the same 24 hour period at the location of the HVAC system, a baseline for an average temperature of a heating element that is observed during a 24 hour period versus the minimum outside temperature that is observed during the same 24 hour period at the location of the HVAC system, a baseline as to financial cost based on utility rates in operating the HVAC system, a baseline as to consumed units of energy in operating the HVAC system, a baseline as to different types of energy sources used in operating the HVAC system and the like. 
     In turn, the application server, for each particular parameter of the equipment, establishes a threshold for a maximum deviation from the baseline that is generated for the same parameter. For example, for some deployment scenarios, how far a value of a parameter is from a value that is considered normal may be relevant. 
     For an illustrative example, assume a building has five HVAC systems of the same make and model, with one particular HVAC system being located in a room with several heat generating devices while the remaining four HVAC systems are in other rooms that have no heat generating devices. Assume also that the four HVAC systems are running on average 40% of the time, and the data collected on the percentage of times that the four HVAC systems are running within a range of 35% and 45%. For the particular HVAC system that is located in the room with several heat generating devices, the data collected on the percentage of time that the particular HVAC system is running may indicate that the particular HVAC is running on average 65% of the time, and the data collected on the percentage of time that the particular HVAC system is running within a range of 50% and 80%. Accordingly, for the particular HVAC system, the baseline for a percentage of time the particular HVAC system is running may be set to 65% and the threshold for the maximum deviation from the baseline for the percentage of time the particular HVAC is running may be set such that running the particular HVAC system 80% of the time would not trigger a scheduling of a maintenance action. In other words, for each particular parameter of the equipment, the baseline is generated first. Then, the threshold for the maximum deviation from the baseline is established in a manner that would not cause triggering of a maintenance action when the equipment is running at a level that is acceptable for the particular deployment scenario of the equipment. For the particular HVAC system described above, the threshold for the maximum deviation may be set to cause an action when the HVAC system is running more than 80% of the time, e.g., 85% of the time. 
     The application server then monitors each of the one or more parameters of the equipment and the atmospheric data for the location at which the equipment is operating. In one embodiment, the monitoring of a particular parameter of the one or more parameters comprises receiving a value of the particular parameter in a predetermined time interval. The value of each parameter of the one or more parameters that is received is processed and stored in a database. 
     In one embodiment, the monitoring of the particular parameter of the one or more parameters further comprises aggregating a plurality of values of the particular parameter, wherein the plurality of values of the particular parameter are received over a predetermined time for aggregation. For example, the aggregation of the data may be performed every four hours, every eight hours, every twelve hours, every day, every week, etc. 
     In one embodiment, the monitoring of the particular parameter of the one or more parameters comprises monitoring a parameter associated with a battery life. For example, the monitoring may be for a sensor that operates on batteries. Then, the monitoring may be for a particular parameter that tracks a battery life associated with the sensor that operates on batteries. 
     In one embodiment, the monitoring of the particular parameter of the one or more parameters comprises monitoring a parameter associated with signal strength of a particular sensor of the one or more sensors. For example, the signal strength of the particular sensor may be monitored when the receiving of the data that is captured by the particular sensor is performed without having the data that is captured by the particular sensor traversing a local gateway server deployed at the location. 
     The application server then determines, for each particular parameter of the one or more parameters of the equipment, if a deviation from the baseline that is greater than the threshold for the maximum deviation from the baseline that is established for the particular parameter is detected. For a particular parameter of the one or more parameters of the equipment, if there is a deviation from the baseline but the deviation from the baseline is less than or equal to the threshold for the maximum deviation from the baseline that is established for the particular parameter, there is no need to trigger the scheduling of an action based on the deviation that is detected. However, for the particular parameter of the one or more parameters of the equipment, if there a deviation from the baseline and the deviation from the baseline is greater than the threshold for the maximum deviation from the baseline that is established for the particular parameter, the deviation may be an indication that an potential issue with the equipment is developing. In one example, the detecting of the deviation may be useful for one or more of: assessing a risk or likelihood of a pending failure of the equipment, triggering a scheduling of a maintenance action to be taken before a failure actually occurs, changing a mode of operation of the HVAC system (e.g., starting an air conditioner sooner in the day, maintaining a cooler temperature in the building through the night when the utility rate may be the lowest), and so on. For instance, it is now possible to schedule an on-demand maintenance appointment or visit for the equipment such that an issue that is developing with the equipment is addressed prior to an actual failure. 
     An actual “failure” is deemed to be a condition where the equipment is no longer able to meet the performance parameter set for the equipment. For example, an HVAC system not able to maintain a set temperature of 78° F. for a properly sized room or area when the outside temperature is 85° F. can be deemed to be an actual failure, whereas an HVAC system not able to maintain a set temperature of 72° F. when the outside temperature is 107° F. may not be deemed to be an actual failure. 
     In one embodiment, for each particular parameter of the one or more parameters of the equipment, if the deviation from the baseline that is greater than the threshold for the maximum deviation from the baseline is established for the particular parameter, then a remedial analytics is performed to determine whether a remedial action is to be taken. For example, the application server determines whether the results of the remedial analytics will trigger a remedial action. For example, the results of the remedial analytics may have reached a threshold for generating a ticket for taking an action. The application server generates the ticket when the results of the remedial analytics trigger a remedial action. In one embodiment, the ticket may be for dispatching maintenance personnel or for making an adjustment to an existing maintenance schedule. For example, the existing maintenance schedule may indicate that maintenance personnel will be visiting the equipment site in ten days. However, the results of the remedial analytics may indicate that an issue is developing with the equipment and remediation within the next three days is more appropriate. Then, the ticket may be generated to make adjustments to a maintenance schedule and to ensure that a maintenance visit to the location of the equipment occurs within the next three days. Alternatively, the mode of operations of one or more of the HVAC systems may be altered. 
     In one embodiment, the application server sends the ticket that is generated to a system or a user. For example, the user may be an individual or an organization responsible for maintenance of the equipment, dispatching of maintenance personnel, or changing a mode of operation of the HVAC systems. The application server then continues monitoring each of the one or more parameters of the equipment. 
       FIG. 1  illustrates an example network  100  related to the present disclosure. In one illustrative embodiment, the network  100  may comprise a plurality of buildings  111   a - n  (broadly any enclosed environment), an access network  101 , a core network  103 , a system  125  (e.g., a server) of an entity that provides atmospheric data, a system  126  (e.g., a server) of an entity that provides data about utility rates, a system  127  (e.g., a server) of an agency that provides various standards such as indoor air quality and the like, a system  128  (e.g., a server) of a user, e.g., a system of a dispatcher, maintenance personnel or HVAC system operation managers, and a database server  129  containing equipment maintenance and/or operation data. For example, records on maintenance and current operational modes of various HVAC equipment may be stored in the database  129 . 
     The access network  101  may comprise a Wireless-Fidelity (Wi-Fi) network, a cellular network (e.g., 2G, 3G, and the like), a long term evolution (LTE) network, 5G and the like. The core network  103  may comprise any type of communications network, such as for example, a traditional circuit switched network (e.g., a public switched telephone network (PSTN)) or a packet network such as an Internet Protocol (IP) network (e.g., an IP Multimedia Subsystem (IMS) network), an asynchronous transfer mode (ATM) network, or a wireless network. It should be noted that an IP network is broadly defined as a network that uses Internet Protocol to exchange data packets. 
     In one illustrative embodiment, the building  111   a  comprises an equipment  112 , one or more sensor devices  113   a - 113   n , and a local gateway server  114 . In one embodiment, the core network  103  may include an Application Server (AS)  104  and a database server  106 . In one embodiment, the AS  104  is configured to perform the methods and functions described herein (e.g., the method  200  discussed below). For example, the AS  104  may be deployed as a hardware device embodied as a dedicated server (e.g., the dedicated computer  300  as illustrated in  FIG. 3 ). In other words, the AS  104  is for providing equipment maintenance in accordance with the teachings of the present disclosure. The application server  104  may comprise an analytic engine  105 . The application server  104  may be communicatively coupled with the database server  106 . 
     In one embodiment, the database server  106  may be used for storing data gathered from various internal and external sources. For example, atmospheric data gathered from the system  125 , utility rate data gathered from the system  126 , standards for indoor air quality gathered from the system  127 , and maintenance and operation mode data gathered from the database  129 , may be stored in the database server  106 . The application server  104  may then access the data gathered from the various internal and external sources when performing an analysis, generating a baseline, and providing the equipment maintenance. 
     In one embodiment, the one or more sensor devices  113   a - 113   n  may communicate with the application server  104  via the local gateway server  114  and the access network  101 . In one embodiment, the one or more sensor devices  113   a - 113   n  may communicate with the application server  104  directly via an access network, e.g., via a cellular network or a Wi-Fi access point. A sensor device is said to be communicating directly via an access network when the communication occurs without the use of the local gateway server, e.g., the local gateway server  114 . 
     It should be noted that the network  100  may include additional networks and/or elements that are not shown to simplify  FIG. 1 . For example, the access network and the core network of  FIG. 1  may include additional network elements (not shown), such as for example, base stations, border elements, gateways, firewalls, routers, switches, call control elements, various application servers, and the like. In addition, the building  111   a  may include any number of equipment (e.g., HVAC systems), sensors, local gateway servers, etc. 
     Although a single database is shown in core network  103  of  FIG. 1 , the various types of data may be stored in any number of databases. For instance, various databases, e.g., a database for equipment, a database for sensors, a database for standards, a database for guidance for indoor air quality, a database for atmospheric data, a database for building and equipment maintenance data, a database for battery life of sensors, etc., may be used. In addition, the various types of data may also be stored in a cloud storage. In other words, the network service provider may implement the service for providing equipment maintenance of the present disclosure by utilizing distributed sensor devices and storing data in a cloud storage and/or a centralized server. 
     In one embodiment, the AS  104  is used for implementing the present method for providing equipment maintenance. The AS  104  of the present disclosure is for receiving, for each particular sensor of one or more sensors at a location, data that is captured by the particular sensor, wherein the data that is captured by the particular sensor is associated with at least one parameter of one or more parameters of an equipment, wherein the equipment is deployed at the location, for receiving atmospheric data for the location, for monitoring each particular parameter of the one or more parameters of the equipment and the atmospheric data and utility rate for the location, for determining, for each particular parameter of the equipment, if a remedial analytics needs to be performed, when a deviation from a baseline greater than a threshold for a maximum deviation from the baseline that is established for a particular parameter is detected, and for generating a ticket for a remedial action based on the results of the remedial analytics indicating a remedial action to be taken. 
       FIG. 2  illustrates a flowchart of an example method  200  for operating heating and cooling equipment via a communications network in accordance with the present disclosure. In one embodiment, the method  200  may be implemented in an application server, e.g., an application server  104 , or the processor  302  as described in  FIG. 3 . 
     The method  200  may be implemented for any number of locations and any number of equipment at each location. For example, the AS  104  may be used for a plurality of locations of an enterprise, with any number of equipment being maintained via the AS  104  at each location of a plurality of locations of the enterprise (e.g., across multiple states). For clarity, the flowchart of the example method  200  is described herein for each equipment. However, the method may be performed for any number of equipment in parallel across multiple locations. The method  200  starts in step  205  and proceeds to step  207 . 
     In optional step  207 , the processor receives, for a location, information about equipment to be maintained and one or more sensors that are deployed at the location. For example, for the location, the processor may receive, a physical location of each equipment and each sensor, a lead unit designation (if applicable), a type of communication for each sensor of the one or more sensors, etc. 
     In step  210 , the processor receives, for each particular sensor of the one or more sensors at the location, data that is captured by the particular sensor of the one or more sensors, wherein the data that is captured by the particular sensor of the one or more sensors is associated with at least one parameter of one or more parameters of an equipment, wherein the equipment is deployed at the location. 
     In one embodiment, each particular sensor of the one or more sensors is for capturing the data that is received from the particular sensor, wherein the data that is captured by the particular sensor is associated with at least one parameter of the one or more parameters of the equipment. 
     In one embodiment, each particular parameter of the one or more parameters of the equipment is a parameter that is defined for tracking one or more measurable aspects of the equipment. In one embodiment, a measurable aspect of the one or more measurable aspects of the equipment may be for tracking at least one of: a percentage of time the equipment is running, a percentage of time a component of the equipment is running, a temperature of the location at which the equipment is deployed, and a temperature of a component of the equipment. 
     In step  215 , the processor receives atmospheric data and utility rates for the location. For example, the processor receives atmospheric data from a weather data source, e.g., a database of a national weather service. In another example, the processor receives up-to-date utility rates data from one or more utility entities as to their current rates. 
     In optional step  220 , the processor generates a baseline for each particular parameter of the one or more parameters of the equipment. 
     In optional step  225 , the processor, for each particular parameter of the one or more parameters of the equipment, establishes a threshold for a maximum deviation from the baseline that is generated for the particular parameter of the one or more parameters of the equipment. For example, if there are five HVAC systems in a building and three parameters are to be monitored for each HVAC system, fifteen baselines are generated, one baseline for a particular parameter of a particular HVAC system. Then, for each of the fifteen baselines, a threshold for a maximum deviation from the baseline (i.e., the baseline of the respective parameter) is established. 
     In step  230 , the processor monitors each particular parameter of the one or more parameters of the equipment and the atmospheric data for the location. For example, the processor may monitor a temperature, a humidity level, utility rates, consumed units of energy, and so on for the location. For the example above, each of the fifteen particular parameters at the location is monitored. In addition, the atmospheric data for the location at which the five HVAC systems are deployed is monitored. 
     In step  235 , the processor determines, for each particular parameter of the one or more parameters of the equipment, whether a deviation from the baseline greater than the threshold for the maximum deviation from the baseline that is established for the particular parameter is detected. If the deviation from the baseline greater than the threshold for the maximum deviation that is established for the particular parameter is detected, the processor proceeds to step  250 . Otherwise, the processor proceeds to step  230 . 
     In step  250 , the processor determines whether a remedial analytics needs to be performed. If the processor determines a remedial analytics needs to be performed, the processor proceeds to step  255 . Otherwise, the processor proceeds to step  230 . 
     In step  255 , the processor performs a remedial analytics associated with the equipment. For example, the analytic engine may comprise machine learning models (MLMs) that take a large number of inputs and attempt to determine whether a remedial action is necessary. For instance, the machine learning model may be initially trained with various historical data and then subsequently updated based on newly received data. In general, a system for generating a predictive model (a machine learning model) may collect, aggregate, and/or store various types of data that may be used as training data and which may be processed via the machine learning models. In accordance with the present disclosure, an analytic engine  105  (a machine learning model) may be created (trained) for a given remedial action based upon various data that may be collected in connection with the various sensors. Once trained, the analytic engine may be applied to new data that is collected to generate a recommended course of action, broadly a remedial action. 
     A machine learning model, such as an analytic engine, may be generated from the training data set in a variety of ways. For instance, the purpose of a machine learning algorithm (MLA) may be to generate a machine learning model, such as a SVM-based classifier, e.g., a binary classifier and/or a linear binary classifier, a multi-class classifier, a kernel-based SVM, etc., a distance-based classifier, e.g., a Euclidean distance-based classifier, or the like, or a non-classifier type machine learning model, such as a decision tree, a KNN predictive model, a neural network, and so forth. For illustrative purposes, examples of the present disclosure are described herein primarily in connection with classifier type MLAs/machine learning models. In one example, the training data set may include labeled data which may be used in training the machine learning model to discriminate positive examples from negative examples. In another example where the classifier comprises a SVM, the machine learning algorithm may calculate a hyper-plane in a hyper-dimensional space representing the features space of all possible triggering parameters. The hyper-plane may define a boundary in the feature space which separates positive examples from negative examples. Once a classifier, or other type of machine learning model/predictive model, is generated for a particular network service and for a particular theme, the classifier may be applied to new data. 
     In step  260 , the processor determines whether the results of performing the remedial analytics associated with the equipment causes or triggers a remedial action to be taken. For example, the results of the remedial analytics associated with the equipment may have reached a threshold for generating a ticket, e.g., a maintenance ticket or an operation mode change ticket. If the results of the remedial analytics associated with the equipment causes the remedial action, the processor proceeds to step  265 . Otherwise, the processor proceeds to step  230 . 
     To illustrate, the method may employ an example savings calculation to make a recommendation as to a change in the mode of operation. In other words, the result from performing the savings calculation may dictate a change as to the operation of the HVAC systems. For example, the following equation can be used: 
     
       
         
           
             
               Savings 
                
               
                   
               
                
               
                 ( 
                 $ 
                 ) 
               
             
             = 
             
               Δ 
                
               
                   
               
                
               T 
               * 
               1.08 
               * 
               Efficiency 
               * 
               
                 ( 
                 
                   Fan 
                    
                   
                     CFM 
                     12000 
                   
                 
                 ) 
               
               * 
               
                 ( 
                 
                   Bin 
                    
                   
                       
                   
                    
                   Hours 
                 
                 ) 
               
               * 
               Utility 
                
               
                   
               
                
               Rate 
             
           
         
       
     
     where ΔT is the temperature differential penalty from non-optimal operation, 1.08 is an example conversion constant for the heat coefficient of air, Efficiency is the system heat removal performance, e.g., in 
     
       
         
           
             
               kW 
               Ton 
             
             , 
           
         
       
     
     Fan CFM is the mass flow rate of air through supply fans, Bin Hours is a variable for hours expected to fall in a weather bin and Utility Rate is a variable for the average utility rate based on peak demand and consumption. For example, in one embodiment, the present method looks at observed data, compares the observed data against expected behavior and quantifies annualized inefficiency costs for each mode of operation. The data coming from the HVAC equipment may stream at predefined intervals, e.g., at 10 minute intervals, but each tag is grouped into appropriate weather “bins” to then compare inputs vs. outputs. In one example, sensor data in each weather bin is then averaged. This results in a handful of actionable data points and greatly reduces the amount of data that has to be stored. With an extended duration of data, the present remedial analytics may detect or reveal a gradual decrease in performance for each mode of operation/weather bin. It should be noted that the above equation is only one illustrative savings calculation that can be employed and should not be interpreted as a limitation of the present disclosure. Other instances of savings calculations can be used. 
     In step  265 , the processor generates a ticket or a recommendation for the remedial action to be performed. The ticket is based on the results of the remedial analytics associated with the equipment. In one embodiment, the processor generates the ticket for dispatching maintenance personnel to the location of the equipment. In another embodiment, the processor generates the ticket for updating a maintenance schedule. For example, the updating of the maintenance schedule may be for performing maintenance at an earlier time than previously scheduled. In another example, the updating of the maintenance schedule may be for performing maintenance at a later time than previously scheduled. In another embodiment, the processor generates the ticket for changing a mode of operation of the HVAC system, e.g., changing a time of operation of the HVAC system, bring “online” another HVAC system, taking “offline” a current operating HVAC system and so on. 
     In optional step  270 , the processor sends the ticket that is generated to a user or a system. For example, the ticket may be sent to an individual or a system responsible for maintenance or operation of the equipment at the location or dispatching of maintenance personnel to the location. The processor then proceeds either to step  207  to receive information, to step  230  to continue monitoring, or to step  299  to end the process. 
     It is important to note that the equipment (types and quantities), the measurable aspects, types of sensors, the types of measurements that are to be performed, the types of computations that are performed, the length of time used for comparisons, etc. are not intended to limit the applicability of the teachings of the present disclosure. For example, the local gateway server may comprise a 900 MHz 3G cellular network gateway server or any other gateway servers. The sensors may comprise a temperature sensor (e.g., one or more sensors for measuring air or space temperature, for measuring discharge line temperature, for measuring air supply line temperature, for measuring suction line temperature, for measuring discharged condenser air temperature and the like), a humidity sensor, a timer for run times, a discharge sensor (e.g., for air, for fluid, and the like), etc. The measurements may be absolute measurements, or measurements of differences between predicted and actual values obtained via sensors. 
     In addition, although not specifically specified, one or more steps, functions or operations of method  200  may include a storing, displaying and/or outputting step as required for a particular application. In other words, any data, records, fields, and/or intermediate results discussed in the method can be stored, displayed and/or outputted either on the device executing the method or to another device, as required for a particular application. 
     Furthermore, steps, blocks, functions or operations in  FIG. 2  that recite a determining operation or involve a decision do not necessarily require that both branches of the determining operation be practiced. In other words, one of the branches of the determining operation can be deemed as an optional step. Moreover, steps, blocks, functions or operations of the above described method  200  can be combined, separated, and/or performed in a different order from that described above, without departing from the example embodiments of the present disclosure. 
     As such, the present disclosure provides at least one advancement in the technical field of equipment maintenance. For instance, in one example, the present disclosure provides a server and a communication network that is able to analyze data collected from various sources to identify predict equipment issues and generate a ticket or recommendation, e.g., for a dispatch of personnel for a preventative action, for adjusting a schedule of a previously scheduled maintenance of the equipment or for changing a mode of operation. 
     Although the above disclosure was discussed in the context of an HVAC system, other types of equipment deployed in a building can also benefit from the monitoring methods of the present disclosure. For example, sophisticated medical equipment or laboratory equipment used in a hospital or a laboratory can also use the monitoring methods of the present disclosure. 
       FIG. 3  depicts a high-level block diagram of a computer suitable for use in performing the functions described herein. As depicted in  FIG. 3 , the system  300  comprises one or more hardware processor elements  302  (e.g., a central processing unit (CPU), a microprocessor, or a multi-core processor), a memory  304 , e.g., random access memory (RAM) and/or read only memory (ROM), a module  305  for operating heating and cooling equipment via a communications network, and various input/output devices  306  (e.g., storage devices, including but not limited to, a tape drive, a floppy drive, a hard disk drive or a compact disk drive, a receiver, a transmitter, a speaker, a display, a speech synthesizer, an output port, an input port and a user input device (such as a keyboard, a keypad, a mouse, a microphone and the like)). Although only one processor element is shown, it should be noted that the computer may employ a plurality of processor elements. Furthermore, although only one computer is shown in the figure, if the method  200  as discussed above is implemented in a distributed or parallel manner for a particular illustrative example, i.e., the steps of the above method  200 , or each of the entire method  200  is implemented across multiple or parallel computers, then the computer of this figure is intended to represent each of those multiple computers. 
     Furthermore, one or more hardware processors can be utilized in supporting a virtualized or shared computing environment. The virtualized computing environment may support one or more virtual machines representing computers, servers, or other computing devices. In such virtualized virtual machines, hardware components such as hardware processors and computer-readable storage devices may be virtualized or logically represented. 
     It should be noted that the present disclosure can be implemented in software and/or in a combination of software and hardware, e.g., using application specific integrated circuits (ASIC), a programmable gate array (PGA) including a Field PGA, or a state machine deployed on a hardware device, a computer or any other hardware equivalents, e.g., computer readable instructions pertaining to the method(s) discussed above can be used to configure a hardware processor to perform the steps, functions and/or operations of the above disclosed method. 
     In one embodiment, instructions and data for the present module or process  305  for operating heating and cooling equipment via a communications network (e.g., a software program comprising computer-executable instructions) can be loaded into memory  304  and executed by hardware processor element  302  to implement the steps, functions or operations as discussed above in connection with the illustrative method  200 . Furthermore, when a hardware processor executes instructions to perform “operations,” this could include the hardware processor performing the operations directly and/or facilitating, directing, or cooperating with another hardware device or component (e.g., a co-processor and the like) to perform the operations. 
     The processor executing the computer readable or software instructions relating to the above described method can be perceived as a programmed processor or a specialized processor. As such, the present module  305  for operating heating and cooling equipment via a communications network (including associated data structures) of the present disclosure can be stored on a tangible or physical (broadly non-transitory) computer-readable storage device or medium, e.g., volatile memory, non-volatile memory, ROM memory, RAM memory, magnetic or optical drive, device or diskette and the like. Furthermore, a “tangible” computer-readable storage device or medium comprises a physical device, a hardware device, or a device that is discernible by the touch. More specifically, the computer-readable storage device may comprise any physical devices that provide the ability to store information such as data and/or instructions to be accessed by a processor or a computing device such as a computer or an application server. 
     While various embodiments have been described above, it should be understood that they have been presented by way of example only, and not a limitation. Thus, the breadth and scope of a preferred embodiment should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents.