Smart ventilation for air quality control

A processor may control air quality in an enclosed space. A processor may receive an external air condition index dataset associated with a geographical location. A processor may receive an internal air condition index dataset from one or more data collection devices in the enclosed space. A processor may apply an optimization criteria to the external air condition index dataset and the internal air condition index dataset. A processor may, responsive to applying the optimization criteria, determine an air exchange plan. The processor may perform the air exchange plan.

BACKGROUND

The present disclosure relates generally to the field of air quality, and more particularly to techniques for controlling air quality.

Conversations about air pollutants and their effect on air quality often revolve around discussions of outdoor pollutants. While air pollutants associated with the environment and outdoors are important to consider, indoor pollutants and the need to ventilate enclosed structures to minimize negative effects associate with those indoor pollutants are also of concern. In some situations, the air quality in an enclosed structure can be worse than the air quality outside.

SUMMARY

Embodiments of the present disclosure include a method, computer program product, and system for controlling air quality in an enclosed space. A processor may receive an external air condition index dataset associated with a geographical location. A processor may receive an internal air condition index dataset from one or more data collection devices in the enclosed space. A processor may apply an optimization criteria to the external air condition index dataset and the internal air condition index dataset. A processor may, responsive to applying the optimization criteria, determine an air exchange plan. The processor may perform the air exchange plan.

DETAILED DESCRIPTION

The present disclosure relates generally to the field of air quality, and more particularly to techniques for controlling air exchange in enclosed spaces, based at least in part on air conditions. While the present disclosure is not necessarily limited to such applications, various aspects of the disclosure may be appreciated through a discussion of various examples using this context.

Air conditions (e.g., temperature, air quality index (AQI), humidity, etc.) associated with a particular geographical location (e.g., outdoor air conditions) can change dramatically within a day, fluctuating from high quality air conditions to low quality air conditions. Those unknowingly breathing low quality air can face serious health consequences, particularly if the air contains toxic pollutants. As such, it is important to consider external air quality (e.g., outdoor air conditions) as well as the air conditions of an enclosed space (e.g., indoor/enclosed space). While in some situations, the air in an enclosed space has higher quality air than the outdoor air, in other situations the opposite may be true. One traditional method of exchanging air between an enclosed space and the outdoors is through ventilation.

Ventilation controls internal air conditions by generating an air exchange between outdoors and the enclosed space. This air exchange may dilute and displace pollutants as well as, control the temperature, humidity, and air flow. While various types of ventilation presently exist, such methods can be generally categorized as either natural ventilation or mixed-mode ventilation. Natural ventilation is typically understood to refer to methods that allow for the intentional passive flow of outdoor air into an enclosed area (e.g., building, house, vehicle, etc.) through one or more planned opening (e.g., doors, windows, etc.). Natural ventilation generally relies on diffusive physical phenomena (e.g., air pressure, stack effect). For example, opening a window in the morning to let new/fresh outdoor air into the house. Unfortunately, because natural ventilation depends on environmental conditions, such systems may not provide the appropriate amount of ventilation. As such, a mixed-mode ventilation system may be implemented.

Mixed-mode ventilation may be typically understood to refer to methods that utilize both natural diffusive phenomena as well as mechanical means to promote ventilation. For example, a motor may be implemented to draw air into an enclosed space. Mixed-mode ventilation systems can use natural and mechanical means simultaneously or at different times. For example, a mixed mode ventilation system could be configured to use natural ventilation during the night and mechanical ventilation during the day or may operate differently depending on the season.

While both methods may effectively provide ventilation and air exchange between the outside and an enclosed space, such an air exchange is usually only effective when the outside air has a higher quality than the air inside the enclosed space. In situations where the outside air conditions are lower than the air conditions of the enclosed space, traditional methods of ventilation may not only be ineffective in dispelling/diluting pollutants, but may also result in wasted power consumption, particularly in situations where mechanical means of ventilation are utilized.

While in some situations people can monitor the AQI through weather websites or applications and decide independently when they should manually open a window, such methods are often not sustainable and could result in human error (e.g., opening the window at the wrong time). Enclosed spaces configured to have HVAC systems, can have air filters designed to minimize the amount and number of particular pollutants that enter an enclosed space during mixed-mode ventilation. However, these systems may still import polluted air into the enclosed space and require significant energy to function.

In embodiments discussed herein, are solutions provided in the form of a method, system, and computer program product for controlling air conditions in an enclosed space and, more particularly, for optimizing targeted air condition. Embodiments contemplated herein leverage air conditions sensor technology and other techniques (e.g., statistical analysis, artificial intelligence (AI), and/or machine learning) to consider real-time data (e.g., air quality index (AQI)), and future predicted air conditions (e.g., air conditions 6-8 hours in the future), based off of historical data, to optimize and efficiently exchange air between an enclosed space and the outside environment while minimizing energy waste.

In embodiments, a processor may receive or collect an external air condition index dataset associated with a geographical location. In these embodiments, a geographical location may include any location the enclosed space might occupy. For example, while in some embodiments a geographical location could include the external and surrounding environment of a house or office building, in other embodiments, the geographical location may include the route of a public bus as it travels throughout the day. In embodiments, the processor may receive an external air condition index dataset from one or more different data sources. These data sources may include, but are not limited to, one or more data collection devices (e.g., Internet of Things (IoT) sensor devices and/or weather imaging satellites), historical databases (e.g., weather database) associated with air conditions and/or the weather in the geographical location, and various forecasting models (e.g., air quality/conditions or weather forecasting models).

In embodiments, air condition IoT sensors (e.g., data collection devices) may be used to record one or more key performance indicators (KPI) associated with particular geographical location. A KPI may include, but is not limited to air temperature, humidity and moisture in the air, dust level (e.g., particulate size and amount), pollutant type and concentrations (e.g., high levels of carbon dioxide), or any combination thereof. In these embodiments, such data/information may be collected in real-time and stored in a historical database. In some embodiments, data/information may be utilized from a weather database and/or real-time data feeds to determine the air pollution index (API) associated with the geographical location of the enclosed space of interest. In some embodiments, a processor may collect data/information associated with the external air condition index dataset (e.g., APIs) from one or more third party vendors, such as The Weather Channel®. In embodiments, a processor can analyze the received APIs and derive KPIs associated with the outside environment of the enclosed space. In such embodiments, KPIs of a geographical location may be collected based on the zip code or GPS location of the enclosed space. While in embodiments where the enclosed space is mobile a vehicle) the geographical location may be continuously collected and updated via a GPS device, in other embodiments, a planned expected route (e.g., a bus route used for public transportation) may be used to determine what APIs should be used in the external air condition index dataset.

In embodiments, a processor may collect or receive an internal air condition index dataset. The processor may receive the internal air condition index dataset via one or more data collection devices (e.g., IoT sensor devices) configured in the enclosed space. The internal air condition index dataset may include real-time data/information associated with internal air conditions of the enclosed space and/or historical data/information associated with the historical condition of the internal air conditions. In embodiments, as data/information associated with the internal air condition index dataset is collected, it may be stored in a historical database and may be accessed later by the processor. In an example embodiment, a processor may receive an internal air condition index dataset that includes not only the current (e.g., real time) air condition data/information (e.g., temperature, humidity, pollutant particulate size, type of pollutants in the air, and/or concentrations of the pollutants) as well as how the air quality/condition data/information fluctuate over time. For example, the air quality/condition data/information may fluctuate depending on the season/time of year, particular weather patterns (e.g., hurricane or heatwave) and/or environmental events, such as large uncontrolled wildfires.

In embodiments, a processor may further analyze the external air condition index dataset and the internal air condition index dataset. In these embodiments, the processor may analyze the external air condition index dataset and the internal air condition index dataset to evaluate the risk that could occur during a particular time interval. Such an evaluation may determine the risk associated with negative effects that may occur if air is exchanged between the enclosed space and the outside space of the geographical location. In such embodiments, a processor may configure a risk index associated with this risk evaluation over a particular time interval.

In embodiments, a processor may use this risk evaluation/analysis to generate a forecast or to predict various air condition factors, such as those contemplated herein, of the particular geographical location. In some embodiments, the risk evaluation/analysis may be based, at least in part, on the internal and external air condition index datasets. In these embodiments, the internal and external air condition index datasets may be configured based, at least in part, on forecasted or predicted air conditions (e.g., forecasted or predicted air condition index). For example, a processor could be configured to calculate a current risk index, a risk index one hour from now, two hours from now and so on. While the aforementioned example provides an illustration of a risk evaluation in one-hour increments, any time interval may be used (e.g., every 15 minutes or 30 minutes).

As contemplated herein, a processor may base the risk evaluation on the internal air condition index dataset and external air condition index dataset. More particularly, the risk evaluation may include, but is not limited to evaluating the following: i) the AQI of the enclosed space and the AQI of the geographical environment outside the enclosed space; ii) the temperature difference between the enclosed space and the geographical environment; iii) humidity difference between the enclosed space and the geographical environment; or any combination thereof. In some embodiments, internal air condition index datasets may further include user configurable settings. For example, in some embodiments, a user could indicate a preferred temperature or humidity, or, due to a health issue (e.g., Asthma or other breathing issues) could require a higher AQI. In embodiments, internal air condition index datasets may also include HVAC system configurations. In these embodiments, a processor may collect data/information about the HVAC system. For example, a processor could be configured to collect/receive data/information regarding the efficiency, power consumption, and ventilation capabilities of the HVAC system associated with the enclosed space. HVAC systems and their ventilation capabilities may be impacted differently based on the characteristics of the outside air of the geographical location. For example, a significant temperature difference between the air in the enclosed space and the outdoor environment of the geographical location may impact the efficiency of the HVAC system.

In embodiments, a processor may apply an optimization criteria to the external air condition index dataset and the internal air condition index dataset to optimize and determine the optimized future air conditions (e.g., the optimized time for air exchange). In embodiments, external and internal air condition index datasets may include datasets having forecast KPI values associated with outside air and inside the enclosure air. In one example embodiment, at 7:00 am in the morning, a processor may forecast that in one hour (i.e., 8:00 am) the external air condition will have a AQI of 140, a temperature of 70° F., and the weather cloudy. Continuing this example embodiment, the processor may also forecast that in two hours (i.e., 9:00 am), the external air condition will have an AQI of 130, a temperature of 75° F., and the weather is cloudy with a chance of rain. In these embodiments, a processor may also forecast that in three hours (i.e., 10:00 am) the external air condition will have an AQI of 90, a temperature of 78° F., and the weather is sunny without cloud cover. The processor may use these future air conditions (e.g., air conditions at 8:00 am, 9:00 am, and 10:00 am) to optimization these intervals and generate an air exchange plan (e.g., opening a window at 10:00 am). In some embodiments, a processor may apply the risk index, based on the evaluation/analysis of the external air condition index dataset and the internal air condition index dataset, to the optimization criteria. In embodiments, an optimization criteria may include, but is not limited to, identifying an acceptable AQI, low energy consumption, and, in enclosed spaces configured to receive solar power, efficient use of solar batteries (e.g., power bank usage efficiency associated with a solar power system). In these embodiments a processor may apply the optimization criteria to the internal air condition index dataset and the external dataset and using optimization software (e.g., CPLEX®) can determine and/or generate an air exchange plan.

An air exchange plan may include a recommendation for the optimal time and/or method of ventilation or air exchange (e.g., natural ventilation or mix-mode ventilation) between an enclosed space and the outside environment of the geographical location, while ensuring a sufficiently low AQI and minimizing energy consumption/increasing energy efficiency (e.g., reducing the number of times a solar battery may have to charge or discharge). For example, an air exchange plan could recommend opening a window between 7:00 am and 9:00 am, and/or using a HVAC system every hour for 15 minutes between the hours of 12:00 pm and 5:00 pm. In some embodiments, a processor may select an air exchange plan from a set of air exchange plans. In these embodiments, a processor may access a database having a set of air exchange plans that may be previously configured to address particular risk indexes and/or optimization criteria.

In embodiments, a processor may perform the air exchange plan. In embodiments, performing an air exchange plan may include activating or sending a notification. In some embodiments a notification could include sending a notification message, such as a text message, email, or notification message to a mobile application, to a user. In one example embodiment, a user could live in a house (e.g., enclosed space) with an outdated HVAC system or portable air-conditioning system. In this example, the house could be in a geographical location where, due to nearby environmental conditions (e.g., forest wildfire) the AQI is very high. However, due to shifts in the direction of the wind, the AQI shifts from a low level to a sufficient level. The user could receive a notification message from a processor on his mobile phone recommending an air exchange plan. This notification message could recommend the optimal time and/or method a user should ventilate his house. Continuing this example, the notification message could recommend the user open his window or turn on his portable air-conditioner at a particular time when the AQI is 50 and temperature is 80° for one hour and then close the window for the next 5 hours when AQI for his geographical area exceeds 100 and temperature exceeds 89° F.

In another example embodiment, the user could have a solar panel system installed on the roof of his house to mitigate energy costs. Often solar panel systems have batteries configured to save solar power that cannot be immediately consumed. Using embodiments contemplated herein, a processor may receive or collect the solar battery status. This information/data may be included in the internal air condition index dataset and evaluated during the optimization process with the optimization criteria (e.g., power consumption efficiency). In such embodiments, the air exchange plan may include consuming the solar energy more or less without continuously charging and discharging the solar battery. Because the life expectancy of a solar battery is mostly determined by its usage cycles, such embodiments may increase the life of a solar battery by reducing the number of discharge/recharge cycles that occur as a result of air exchange.

In some embodiments, a processor may perform an air exchange plan by sending a notification to the controller of a HVAC system. In these embodiments, the notification may activate the HVAC system to perform the air exchange at an optimal time. For example, at the optimal time (e.g., concerning targeted air conditions, power efficiency, and solar battery operating efficiency), a processor could send a notification that activates the HVAC system to perform air exchange for a particular amount of time. In these embodiments, the activating notification may include how long the ventilation should continue before the HVAC system should be turned off.

In some embodiments, the HVAC system could be configured within a vehicle, such as a car or bus (e.g., enclosed space). For example, a user could be traveling from San Diego to San Francisco in their car. Using methods and techniques contemplated herein, a processor could send a notification to the car's HVAC system to automatically switch between internal ventilation (e.g., recirculating air in the car) and external ventilation (e.g., air exchange/ventilation), based on the optimized air exchange plan using the current and future air conditions (e.g., external and internal air condition index dataset). As discussed herein the optimization and may be based, not only air condition factors (e.g., temperature and humidity differences between inside and outside of the car), but also power consumption and energy efficiency. Such embodiments may allow the user to enjoyed high quality air conditions inside the car throughout the duration of the road trip. In addition, the car will burn less gas to operate the car's HVAC system (e.g., air-conditioner).

In some embodiments, such as those where the enclosed space may be occupied by more than one user or is a public area, a processor may perform the air exchange plan by sending a notification to activate an indicator light. In embodiments, the indicator light may indicate that whether it is an optimal time for air exchange. For example, in office buildings or on a bus, an indicator light may be positioned by a window or fan. If the processor determines that it is an optimal time for air exchange, the indicator light may turn on or be a specific color (e.g., green). But, if the processor determines that it is not an optimal time for air exchange, the light may turn off or change a different color (e.g., red). Such embodiments may reduce conflict caused by differences in personal preferences by clearly indicating whether it is beneficial to open a window to ventilate the enclosed space.

Referring now toFIG.1, a block diagram of a system100for controlling air exchange in an enclosed space, is depicted in accordance with embodiments of the present disclosure.FIG.1provides an illustration of only one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environment may be made by those skilled in the art without departing from the scope of the invention as recited by the claims.

In embodiments, system100include air exchange recommendation service101. In embodiments, air exchange recommendation service101may recommend one or more air exchange plans that may provide an ideal targeted indoor air condition (e.g., air conditions of an enclosed space). For example, a targeted ideal air condition could be 70° F., a particular humidity level, and an AQI of 7. In these embodiments, air exchange recommendation service101may receive data/information from one or more data collection devices102, weather forecasters104, and, in enclosed spaces having solar panel systems, solar power bank battery status data106. Data/information collected from data collection devices102, weather forecasters104, and/or solar power bank battery status data106may be compiled by air exchange recommendation service101into an internal air condition index dataset and an external air condition index dataset. In embodiments, as data/information (e.g., internal air condition index dataset and an external air condition index dataset) are collected and/or compiled, such data/information may be stored within database108. In embodiments, data collection devices102may be configured to relay real-time data, such as real-time KPIs110pertaining to quality air conditions both inside and outside the enclosed space. While in some embodiments, machine learning may use the data/information stored within database108to generate one or more forecasts, in other embodiments, forecasts may be independently configured. These forecasts may include, but are not limited to, forecasting KPI values112(e.g., associated with risk evaluation/analysis and future risk indexes), and forecasting models114associated with determining how different factors, such as environmental situations (e.g., forest wildfire) or the weather (e.g., wind direction or heatwave), may affect air condition quality.

In embodiments, air exchange recommendation service101may further include optimization service116. In some embodiments, optimization service116may utilize optimization software, such as CPLEX®. Optimization service116may be configured to apply one or more optimization criteria to the internal air condition index dataset and an external air condition index dataset. These optimization criteria may include, but are not limited to, obtaining a sufficient air conditions (e.g., low AQI, desired temperature, humidity), low power consumption, and for enclosed spaces configured with a solar panel system, long solar battery life (e.g., by minimizing number of battery charge/discharge cycles). Optimization service116may perform the optimization and determine one or more air exchange plans120. As contemplated herein air exchange plans120may include the optimal time and/or method air exchange should occur for a particular enclosed space in a geographical location.

In embodiments, air exchange recommendation service101may further include notification service118. In embodiments, notification service118may be configured to send, activate, or initiate one or more notifications to a user, to perform air exchange plan120. In these embodiments, notification service118may be configured to send a notification message, such as a text message, email, or mobile phone application, that provides the air exchange plan to a user. In some embodiments, notification service118may be configured to control a HVAC system. In these embodiments, notification service118may control the HVAC system as dictated by air exchange plan120. In other embodiments, notification service118may indicate via an indicator light, located proximate to a window or opening of an enclosed space, that it is or is not an optimal time for air exchange.

Referring now toFIG.2, a flowchart illustrating an example method200for controlling air conditions in an enclosed space, in accordance with embodiments of the present disclosure. In some embodiments, the method200begins at operation202where a processor receives an external air condition index dataset associated with a geographical location.

In some embodiments, the method200proceeds to operation204. At operation204, a processor may receive, from one or more data collection devices in the enclosed space, an internal air condition index dataset. For example, in some embodiments, data/information associated with internal air condition index datasets may also be received from a forecasting module, such as forecasting module112inFIG.1.

In some embodiments, the method200proceeds to operation205. At operation205, a processor may calculate the forecasted air condition index (e.g., external and internal air condition index dataset). In some embodiments, the forecasted air condition index may be utilized during optimization.

In some embodiments, the method200proceeds to operation206. At operation206, the processor applying an optimization criteria to the external air condition index dataset and the internal air condition index dataset.

In some embodiments, the method200proceeds to operation208. At operation208, the processor may determine, responsive to applying the optimization criteria, an air exchange plan. In some embodiments, the method200proceeds to operation210. At operation210, the processor may perform the air exchange plan. In some embodiments, as depicted inFIG.2, after operation210, the method200may end.

As discussed in more detail herein, it is contemplated that some or all of the operations of the method200may be performed in alternative orders or may not be performed at all; furthermore, multiple operations may occur at the same time or as an internal part of a larger process.

Characteristics are as follows:

Resource pooling: the provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of portion independence in that the consumer generally has no control or knowledge over the exact portion of the provided resources but may be able to specify portion at a higher level of abstraction (e.g., country, state, or datacenter).

Service Models are as follows:

Deployment Models are as follows:

Referring now toFIG.3A, illustrative cloud computing environment310is depicted. As shown, cloud computing environment310includes one or more cloud computing nodes300with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone300A, desktop computer300B, laptop computer300C, and/or automobile computer system300N may communicate. Nodes300may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment310to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices300A-N shown inFIG.3Aare intended to be illustrative only and that computing nodes300and cloud computing300and cloud computing environment310can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).

Referring now toFIG.3B, a set of functional abstraction layers provided by cloud computing environment310(FIG.3A) is shown. It should be understood in advance that the components, layers, and functions shown inFIG.3Bare intended to be illustrative only and embodiments of the disclosure are not limited thereto. As depicted below, the following layers and corresponding functions are provided.

Hardware and software layer315includes hardware and software components. Examples of hardware components include: mainframes302; RISC (Reduced Instruction Set Computer) architecture based servers304; servers306; blade servers308; storage devices311; and networks and networking components312. In some embodiments, software components include network application server software314and database software316.

Virtualization layer320provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers322; virtual storage324; virtual networks326, including virtual private networks; virtual applications and operating systems328; and virtual clients330.

Workloads layer360provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation362; software development and lifecycle management364; virtual classroom education delivery366; data analytics processing368; transaction processing370; and controlling air conditions in enclosed places372.

FIG.4, illustrated is a high-level block diagram of an example computer system401that may be used in implementing one or more of the methods, tools, and modules, and any related functions, described herein (e.g., using one or more processor circuits or computer processors of the computer), in accordance with embodiments of the present invention. In some embodiments, the major components of the computer system401may comprise one or more Processor402, a memory subsystem404, a terminal interface412, a storage interface416, an I/O (Input/Output) device interface414, and a network interface418, all of which may be communicatively coupled, directly or indirectly, for inter-component communication via a memory bus403, an I/O bus408, and an I/O bus interface unit410.

The computer system401may contain one or more general-purpose programmable central processing units (CPUs)402A,402B,402C, and402D, herein generically referred to as the CPU402. In some embodiments, the computer system401may contain multiple processors typical of a relatively large system; however, in other embodiments the computer system401may alternatively be a single CPU system. Each CPU402may execute instructions stored in the memory subsystem404and may include one or more levels of on-board cache.

One or more programs/utilities428, each having at least one set of program modules430may be stored in memory404. The programs/utilities428may include a hypervisor (also referred to as a virtual machine monitor), one or more operating systems, one or more application programs, other program modules, and program data. Each of the operating systems, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Programs428and/or program modules430generally perform the functions or methodologies of various embodiments.

Although the memory bus403is shown inFIG.4as a single bus structure providing a direct communication path among the CPUs402, the memory subsystem404, and the I/O bus interface410, the memory bus403may, in some embodiments, include multiple different buses or communication paths, which may be arranged in any of various forms, such as point-to-point links in hierarchical, star or web configurations, multiple hierarchical buses, parallel and redundant paths, or any other appropriate type of configuration. Furthermore, while the I/O bus interface410and the I/O bus408are shown as single respective units, the computer system401may, in some embodiments, contain multiple I/O bus interface units410, multiple I/O buses408, or both. Further, while multiple I/O interface units are shown, which separate the I/O bus408from various communications paths running to the various I/O devices, in other embodiments some or all of the I/O devices may be connected directly to one or more system I/O buses.

It is noted thatFIG.4is intended to depict the representative major components of an exemplary computer system401. In some embodiments, however, individual components may have greater or lesser complexity than as represented inFIG.4, components other than or in addition to those shown inFIG.4may be present, and the number, type, and configuration of such components may vary.