FIELD

This disclosure relates to the field of energy consumption monitoring and, in particular, to improving and optimizing the operation of an energy consuming apparatus of a structure.

BACKGROUND

Commercial and residential structures typically include heating ventilation and air conditioning (HVAC) systems designed to keep occupants comfortable independent of the air temperature outside of the structure. For example, in a multistory commercial building, the HVAC system may include air conditioners, heat pumps, and/or gas furnaces that move conditioned air through each story of the building to ensure that the occupants are maintained at a comfortable temperature.

HVAC systems use electrical energy to produce the desired conditioned output. The amount of electrical energy consumed, however, depends on numerous factors, some of which include, the size of the structure, the number of occupants in the structure, and the outdoor temperature. In a large commercial structure, for example, the indoor air temperature is changed slowly and accounting for rapid changes in the outdoor temperature requires accurate forecasting skills. If the forecast is inaccurate, electrical energy is wasted and the cost to condition the structure is greater than is necessary. Moreover, the occupants may be uncomfortable during periods in which heavy demands are placed on the HVAC to change quickly the air temperature of the structure. Additionally, most commercial structures are relatively unoccupied during certain days of the year and, therefore, it is typically unnecessary to condition the entire structure during these days. Thus, to conserve electrical energy, operators of commercial structures may forecast these unoccupied days and make corresponding adjustments to the HVAC system. Residential structures are also subject to these considerations.

As set forth above, many factors are involved in the conditioning of a commercial structure or residential structure. In order to optimize the amount of electrical energy consumed by building systems, further developments are desired in the ability to forecast energy usage requirements.

SUMMARY

According to an exemplary embodiment of the disclosure, a method of operating a heating ventilation and air conditioning (HVAC) system of a structure, includes collecting first sensor data corresponding to a parameter of the HVAC system, collecting second sensor data that is different than the first sensor data, and generating clustered data by clustering the first sensor data and the second sensor data into a plurality of data clusters with a controller. The method also includes forming a transactional dataset based on at least the first sensor data, the second sensor data, and the clustered data with the controller, performing association rule mining (ARM) on the transactional dataset to generate a plurality of rules for each data cluster of the plurality of data clusters with the controller, and changing an operating characteristic of the HVAC system based on the plurality of rules with the controller to optimize the parameter.

According to another exemplary embodiment of the disclosure, an energy control system for controlling an HVAC system of a structure includes a first sensor, a second sensor, and a controller. The first sensor is operably connected to the HVAC system and is configured to generate first sensor data corresponding to a parameter of the HVAC system. The second sensor is operably connected to the structure and is configured to generate second sensor data that is different than the first sensor data. The controller is operably connected to the first sensor and the second sensor. The controller is configured to execute program instructions to generate clustered data by clustering the first sensor data and the second sensor data into a plurality of data clusters, to form a transactional dataset based on at least the first sensor data, the second sensor data, and the clustered data, to perform association rule mining (ARM) on the transactional dataset to generate a plurality of rules for each data cluster of the plurality of data clusters, and to change an operating characteristic of the HVAC system based on the plurality of rules to optimize the parameter.

