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BACKGROUND

The disclosure is directed to libraries used in conjunction with integrated ventilation and temperature controls for enclosed structures. Specifically, the disclosure is directed to libraries, systems and methods for minimizing pollution inside an enclosed structure in an optimal manner, combined with minimizing energy requirement and maintaining required levels of fresh air, utilizing dynamic and adaptive, user-defined ventilation criteria.

Climate change, environmental standards and diminishing land resources compelled modern architecture standards to design and build ever higher structures. These structures retain their value based on superior structural integrity and, as energy prices continue to rise, are more energy efficient. Due to the construction of the building envelope, these buildings may or may not shield the occupant from negative infiltration of outdoor pollutants, for example, pollen, dust, humidity and the like.

However, occupants, operators and owners of such structures also want to be comfortable and free from the risk of exposure to any indoor pollutants that may cause, for example, health problems. Buildings having superior structural integrity, require frequent ventilation of outside air to maintain, for example levels of fresh O2, and reduce levels of CO2within the structures. Ventilation inside buildings (opening of windows and door, active ventilation, etc.), occurs chiefly during the working hours, times when the urban air is most polluted. Thus, air pollution from the outside is introduced into the buildings.

Moreover, in the effort to operate the buildings at efficient heating and cooling parameters, these buildings and enclosed structures are being constructed as ever-increasing insulated systems, with fixed regulation-based parameters on timing and various pollutants' concentrations within the structures.

Hence, there is a need for more effective and efficient means for determining the optimal conditions for ventilating these enclosed structures.

SUMMARY

Disclosed, in various embodiments, are libraries, systems, methods and computer readable media for minimizing pollution inside an enclosed structure, combined with minimizing energy requirement and maintaining required levels of fresh air in an optimal manner, utilizing dynamic and adaptive, user-defined ventilation criteria. More specifically, provided herein are libraries and methods of providing instructions to an integral HVAC system in a multi-storied, enclosed structure for ventilating the structure in a manner that will maintain preselected parameters, whether user defined or regulation based.

The present invention provides means for integrating and optimizing different and sometimes conflicting requirements in real time: Maintenance of fresh air supply to the building, reduction of indoor air pollution concentrations and energy conservation. The system can calculate a quantified weighted combination of different conflicting requirements. In some embodiments provided herein, the system can calculate a single value which takes into account all the considerations and requirements. Furthermore, in some embodiments of the present invention, the system and method utilize threshold values. Instead of utilizing static/fixed predetermined thresholds, the present system and method utilize dynamic thresholds which take into account the dynamic changes of air pollution levels in real time and combine them with fresh air and energy requirements in order to calculate a value or values that control the ventilation system. These calculated optimal values determine the increasing/decreasing of fresh air ventilation into the multi-storied structure. Moreover, in some embodiments provided herein the system can estimate and calculate the dynamic threshold values without using indoor air pollution measurements, but only using historical data of outdoor air pollution measurements, ventilation history, models of gas dynamics, e.g., diffusion and rate of decomposition, heuristics and/or machine learning methods, and/or statistical methods.

In certain exemplary implementation. provided herein is a processor-accessible library comprising control information for a multi-storied structure's heating, ventilation and air conditioning (HVAC) process, wherein the library contains data and executable commands configured, when executed, to identify optimized period for ventilation and/or heating and air conditioning, and wherein the data incorporate external and internal HVAC parameters.

In another embodiment, provided herein is a computerized method for optimizing heating, ventilation and air conditioning (HVAC) process in a multi-storied structure implementable in a system comprising the multi-storied structure, a heating, ventilation and air conditioning (HVAC) system, a processing module in communication with a non-volatile memory having thereon a processor-readable media and a library comprising: a first ventilation-associated parameter (VAP1), related to the inside of the multi-storied structure, a second ventilation-associated parameter (VAP2), related to the outside of the multi-storied structure, a third ventilation-associated parameter (VAP3), related to temporal ventilation history, air-conditioning associated parameter (HACAP6), related to an internal temperature of the multi-storied structure and an air-conditioning associated parameter (HACAP7), related to an external temperature of the multi-storied structure, wherein the library further comprises a plurality of master process objects, linked to the first, second, and third ventilation-associated parameters as well as to the heating and air-conditioning associated parameter related to the internal and external temperature of the multi-storied structure; the method comprising: responsive to a ventilation prompt, selecting a set of operations configured to achieve a predetermined master process' optimization objective from the plurality of objectives in the library; associating the selected set of operations to create a set of process commands within the ventilation request and forming a ventilation command, wherein the set of master process objects in the library are linked to the ventilation command without copying the set of master process objects into the ventilation command; and executing the ventilation command.

In yet another embodiment, provided herein is a processor-readable media in communication with and a library comprising: a first ventilation-associated parameter (VAP1), related to the inside of the multi-storied structure, a second ventilation-associated parameter (VAP2), related to the outside of the multi-storied structure, a third ventilation-associated parameter (VAP3), related to temporal ventilation history, air-conditioning associated parameter (HACAP6), related to an internal temperature of the multi-storied structure and an air-conditioning associated parameter (HACAP7), related to an external temperature of the multi-storied structure, wherein the library further comprises data and information associated with a single objective associated with a plurality of master process sub-goals, linked to the first, second, and third ventilation-associated parameters as well as to the heating and air-conditioning associated parameter related to the internal and external temperature of the multi-storied structure, the processor-readable media having a set of executable instructions, which, when executed, are configured to cause a processor to: receive a ventilation request prompt from a heating, ventilation and air conditioning (HVAC) system; responsive to the ventilation request, select a set of operations configured to achieve a predetermined master process' optimization objective from the plurality of master process' optimization objectives; associate the selected set of operations with the ventilation request; create a set of process commands within the ventilation request; form a ventilation command, wherein the set of master process objects in the library are linked to the ventilation command without copying the master process objects into the ventilation command; and execute the set of ventilation associated process objects in the ventilation command.

In another embodiment, provided herein is a method to control the state of an HVAC system in order to actuate a predefined optimization strategy designed to pursue a predefined optimization objective while subject to predefined prerequisites, utilizing the content of linked data bases featuring various parameters, among which at least one is continuously updated through an input channel.

The prerequisites can be defined according to the custom requirements of the user. These may be, for example, that the HVAC system will comply with certain regulation or standard of ventilation; that the average outdoor airflow within some custom period of time will be no less than some custom value; that the concentration of CO2will not exceed some defined value, that certain air pressure difference will be maintained between different zones of the structure and between the indoor and outdoor air and so forth. At any case some prerequisite is defined related to a minimal mandatory supply of outdoor air to the multi-storied enclosed structure.

The data bases can be configured to contain one ventilation-associated parameter (VAP3) representing the temporal history of states of the HVAC system, as well as one ventilation-associated parameter (VAP2) related to the temporal history of concentration of one contaminant of interest or a combination of several contaminants, measured or estimated, in the outdoor air which was supplied to the building by the HVAC system. Furthermore, the library comprises a complete set of parameters required to carry the computation defined by the optimization strategy and to verify compliance with each of the defined prerequisites. These may comprise, for example, various parameters of the building itself and of the characteristic activity in it.

The optimization objective can be a weighted combination of multiple sub-goals, for example, minimizing the contamination of the indoor air by outdoor sources and minimizing the energy requirement by the HVAC system.

The optimization strategy used in the methods provided can be configured to calculate a dynamic threshold value (DTV), which is based on a weighted combination of values of VAP2 during previous ventilation events indicated by VAP3. That value is further operated on based on an estimate of the expected instantaneous energy requirement by the HVAC system in case of ventilation of the multi-storied enclosed structure, with a monotonic decrease of the DTV with that energy requirement. The magnitude of the decrease is determined by the respective weights of the sub-goals within the optimization objective.

In addition, in the methods provided, a comparison is made in certain operable examples, between the current value of VAP2, and the DTV. Ventilation will occur when VAP2 is, for example, smaller than or equals to DTV. Otherwise, ventilation will stop providing that this stoppage is not expected to cause violation of certain defined prerequisites.

The directives implemented by the optimization strategy, and executed by the processing module, can be actuated by a control unit which is physically connected to the HVAC system and with which the processing unit has a local or remote communication channel.

Data created during the processing and actuation of the optimization objective may continuously be fed back into the data bases, in order to serve in the following processing or in the monitoring of the system.

In certain embodiments, the dynamic threshold can be further operated on according to the prerequisites (e.g. increase of the threshold as current conditions in the multi-storied enclosed structure are approaching conditions where ventilation is mandatory according to the prerequisites).

Furthermore, in certain embodiments the weights of the sub-goals within the optimization objective are adjusted dynamically according to user-defined criteria (for example, lower weight to indoor air quality and higher weight to energy saving when the occupancy is low and the opposite when it is high).

In certain exemplary implementation, the dynamic threshold can further be operated on according to a prediction of expected values of, for example, VAP2 in the near future (for example, within the next minute, hour, day, etc.).

In certain exemplary implementations, the dynamic threshold can further be operated on according to expected energy requirement by the fresh air system in case of ventilation in each instant in the near future, considering the forecasted weather parameters such as temperature and relative humidity, and a model of the effect of these parameters on energy requirement in the structure.

In another embodiment, another ventilation associated parameter (VAP1) can be defined in the library, and be associated with a measurement or estimation of the concentration of one or more substances (e.g., NOx) in the indoor air. The DTV can then be calculated and determined based on the current value of VAP1 and other prediction tools.

In yet another embodiment, the dynamic threshold can further be calculated based on chemical, physical or computational model(s) of the flow dynamics and disintegration kinetics of gas and other airborne materials inside the building, considering the influence of at least one of the HVAC system activity, the influence of biological activity, and the influence of any other kind of activity inside the building.

In certain exemplary implementation, the optimization objective further comprises a sub-goal of minimization of erosion and/or contamination of the indoor air as a result of indoor processes or indoor gas components, such as accumulation of CO2, dwindling of O2, accumulation of indoor pollutants such as volatile organic compounds (VOC), appearance of offensive odors or some combination comprising the foregoing.

In such case, the dynamic threshold can further be calculated, for example, based on an estimation of the indoor air contamination and/or erosion due to indoor processes, based on the recent values of the parameter VAP3 and on some parameters of the multi storied enclosed structure, and on the human activity within, as appear in the data bases.

In another embodiment, the optimization objective can further comprise more than one sub-goal of minimizing average concentration of contaminant of outdoor source. Accordingly, the DTV is then calculated based on combination of recent values of VAP2 for all the contaminants of interest. In addition, the DTV is then compared to an index calculated based on a weighted combination of the current values of VAP2 for all the contaminants of interest. The weights in both combinations can be determined according to the corresponding weights in the optimization objective.

In yet another embodiment, additional parameters can be defined in the data library, associated with data such as at least one of pollution levels at locations that are remote to the enclosed structure, occupancy of the building, meteorological, traffic, etc. and possibly updated through additional input channels. These parameters can be used by the optimization strategy uploaded to the processing module of the system.

In certain exemplary implementation where the decision space is binary (i.e. where the outdoor airflow can only be turned on or off), the outdoor airflow at the ‘on’ configuration can further be adapted dynamically according to variable conditions, for example changes in the outdoor humidity and temperature and the indoor set temperature. The flow can then be set to the maximum possible levels at the current conditions, where the system is still safe from excessive accumulation of liquid water in it.

In another embodiment, the HVAC system may modulate the outdoor air flow among multiple values. Accordingly, the prerequisites can further comprise the maximum outdoor airflow allowed as a function of outdoor temperature and humidity.

In yet another embodiment, the optimization strategy logic is not based on a comparison between a threshold value and VAP2 but rather on a predictive search strategy. Thus the dynamic ventilation criteria are not limited to the DTV.

In certain exemplary implementation, the system will activate ventilation into unoccupied zones, also during times when the entire structure is unoccupied. This, in cases when the optimization strategy finds that such ventilation may lead to net reduction of energy requirement or to reduction of indoor air pollution levels during occupancy times.

In certain exemplary implementation, the system will activate, deactivate and tune various components of thermal treatment in the HVAC system, so as to improve energy efficiency of the HVAC system. Such components can be, for example, chillers, heat-pumps, fancoils, heating coils, heat exchangers, cooling towers, water pumps, engines, fans, compressors etc. For example, at times when it is detected that certain components are operating under partial load, redundant components may be turned off (for example, when low cooling capacity is required from a battery of several water chillers, some of these chillers may be turned off, until the required cooling capacity becomes high again).

As another example, the system may automatically tune the temperature set point of the fresh air system when operating during times when the structure is not occupied, in a manner that may lead to better outcome with respect to the optimization objective. For example, (during seasons when the indoor environment is kept warmer than the outdoor) heating of cold fresh air may be reduced and, in some cases even altogether stopped during night times, while still maintaining the required indoor temperature during occupancy times.

In certain exemplary implementation, the system will control baffles and dumpers affecting outdoor air flow distribution between different sub-zones in the structure, leading to better outcome with respect to the optimization objective. As an example, outdoor airflow to a zone may be tuned in response to the occupancy of the zone, reducing the energy requirement by the system when occupancy reduces and restoring it as occupancy grows again. As another example, outdoor airflow into zones where the occupancy is found to be low may be increased when it is predicted that the outdoor air pollution is about to increase and before the pollution levels rise above a dynamic threshold. This allows longer stoppage or reduction of outdoor air supply to the entire structure when the outdoor pollution subsequently rises.

In certain exemplary implementation, the system may control exhaust ventilation air flow while controlling the fresh air airflow, in order to comply with a prerequisite of a required air pressure differences between certain zones in the structure (e.g. toilets, kitchens etc.), the rest of the structure and the outdoor environment.

In certain exemplary implementation, the system may detect a faulty component of the HVAC system or an improper distribution of fresh air flow in the structure or in a sub space of it. Is such case, the system may issue an alert.

In certain exemplary implementation, the system further comprises a graphic user interface (GUI) configured to allow the user to selectably adjust the logic determining the weighting of the sub-goals within the selected optimization objective.

In certain exemplary implementation, the systems provided herein, using the libraries provided to implement the methods disclosed further comprises at least one of a multi-directional, and a direction-adjustable air-inlet module, adapted to provide selectable inflow of air from a discrete direction

Further and in certain exemplary implementation, provided herein is a method for adaptive optimization of heating, ventilation and air conditioning (HVAC) process in a multi-storied structure implementable in a system comprising the multi-storied structure, a heating, ventilation and air conditioning (HVAC) system, a processing module in communication with a non-volatile memory having thereon a processor-readable media and a library comprising: a first ventilation-associated parameter (VAP1), related to the inside of the multi-storied structure, a second ventilation-associated parameter (VAP2), related to the outside of the multi-storied structure, a third ventilation-associated parameter (VAP3), related to temporal ventilation history, an air-conditioning associated parameter (HACAP6), related to an internal temperature of the multi-storied structure and an air-conditioning associated parameter (HACAP7), related to an external temperature of the multi-storied structure, wherein the library further comprises a plurality of master process objects comprised of a plurality of sub-goals, with a dynamic threshold value, the dynamic threshold value linked to the first, second, and third ventilation-associated parameters as well as to the heating and air-conditioning associated parameter related to the internal and external temperature of the multi-storied structure; the method comprising: selecting a historical dataset comprising a first set of forecast pollutants' values received from one or more predictive forecast statistical models and a first set of actual pollutants' values received from one or more measurements of the pollutants; generating one or more variants of machine learning models to model performance of the one or more predictive forecast models by training the one or more variants of the machine learning models on the historical dataset; receiving a current dataset comprising a second set of forecast pollutants' values derived from the one or more predictive forecast models and a second set of actual pollutants' values derived from the one or more measurements of the pollutants; correlating the current dataset with the historical dataset to adaptively obtain a filtered historical dataset; selecting the one or more variants of the machine learning models trained on the historical dataset and evaluating them on the filtered historical dataset to assign weights to each of the one or more variants of the machine learning models and their outputs; and deriving a statistical model in the form of an optimal combination function to determine at least one combined forecast pollutants' value by combining weights assigned to each of the one or more variants of the machine learning models trained based on the evaluating of the one or more variants of the machine learning models on the filtered historical dataset and the outputs of the each of the one or more variants of machine learning models trained on the historical dataset, wherein the selecting, the generating, the receiving, the correlating, the evaluating and the deriving are performed by the processor using computer-readable instructions stored in the memory.

These and other features of the libraries, systems, methods and computer readable media for minimizing pollution inside an enclosed structure in an optimal manner, utilizing dynamic, user-defined threshold values, will become apparent from the following detailed description when read in conjunction with the figures and examples, which are exemplary, not limiting.

