The present disclosure relates to a method of predicting air conditioning load based on room temperature and an air conditioner implementing the method, the air conditioner according to one embodiment may include a sensor configured to sense a room temperature or humidity in a suspended section in which an air discharge part is not operated; and a central controller implemented to control the air discharge part and an outdoor unit based on operation mode information that is calculated from a value sensed by the sensor, when the air discharge part and the outdoor unit are switched on.

A heating, ventilation, or air conditioning (HVAC) system for a building includes HVAC equipment configured to provide heating or cooling to one or more building spaces and one or more controllers. The one or more controllers include one or more processing circuits configured to generate energy targets for the one or more building spaces using a thermal capacitance of the one or more building spaces to which the heating or cooling is provided by the HVAC equipment, generate setpoints for the HVAC equipment using the energy targets for the one or more building spaces to which the heating or cooling is provided by the HVAC equipment, and operate the HVAC equipment using the setpoints to provide the heating or cooling to the one or more building spaces.

An HVAC system includes an evaporator, a first sensor coupled to the evaporator at a first position, and a second sensor operably coupled to the evaporator at a second position. The first sensor monitors a first temperature of the refrigerant flowing in the evaporator at the first position, which is adjacent to the evaporator inlet. The second sensor monitors a second temperature of the refrigerant flowing in the evaporator at the second position, which is downstream from the first position. The system includes a controller, which receives a first signal corresponding to the first temperature and a second signal corresponding to the second temperature. The controller determines, based on the received signals, a temperature difference between the second temperature and the first temperature. In response to determining that the temperature difference is greater than a predefined threshold value, the controller determines that a loss of charge has occurred.

A heating, ventilation, and air conditioning (HVAC) control device configured to generate the machine learning model using the first set of weights and the second set of weights. The machine learning model is configured to output a probability that a user is present at the space based on an input that identifies a day of the week and a time of a day. The device is further configured to determine a probability that a user is present at the space for a predicted occupancy schedule using the machine learning model, to determine an occupancy status based on a determined probability that a user is present at the space, and to set a predicted occupancy status in the predicted occupancy schedule based on a determined occupancy status for each time entry. The device is further configured to output the predicted occupancy schedule.

A compressor return air dryness detection method includes: obtaining an exhaust air pressure, a return air pressure, a working frequency, an exhaust air temperature, and a return air temperature of a compressor; determining a return air saturation temperature corresponding to the return air pressure; calculating a temperature difference value based on the return air temperature and the return air saturation temperature; and in accordance with a determination that the temperature difference value is smaller than a predetermined threshold value, calculating a return air dryness of the compressor based on the exhaust air pressure, the return air pressure, the working frequency, and the exhaust air temperature.

Systems and methods for orchestrating the operation of energy-consuming loads, so as to minimize power consumption, are described. In some embodiments, the loads can be HVAC, refrigeration systems, air compressors, and the like, and orchestration is effected either directly or by means of the loads' respective controllers. In some aspects, the controllers can be Smart Thermostats and orchestration is effected through a Cloud-based orchestration platform or “COP.” In certain aspects, a COP uses specifically programmed application programming interfaces, or APIs, to control the operation of a single manufacturer's Smart Thermostats, where the manufacturer provides its own Cloud-based control platform, through which the COP operates. The COP can similarly orchestrate the operation of two or more manufacturers' Smart Thermostats through their respective Cloud-based control platforms. By these and other means, the operation of a variety of energy-consuming loads can be more easily and efficiently orchestrated.

A chiller threshold management system for a building, including one or more memory devices and one or more processors. The one or more memory devices are configured to store instructions to be executed on the one or more processors. The one or more processors are configured to determine whether chiller fault data exists in chiller data used to generate a plurality of chiller prediction models. The one or more processors are further configured to generate a first threshold evaluation value for each of the plurality of chiller prediction models using a first evaluation technique in response to a determination that chiller fault data exists in the chiller data, and generate a second threshold evaluation value for each of the chiller prediction models using a second evaluation technique in response to a determination that chiller fault data does not exist in the chiller data. The one or more processors are configured to select a first threshold for each of the plurality of chiller prediction models based on the first threshold evaluation values in response to the determination that chiller fault data exists in the chiller data, and select a second threshold for each of the plurality of chiller prediction models based on the second threshold evaluation values in response to the determination that chiller fault data does not exist in the chiller data.

The indoor unit includes a plurality of outlet openings. In airflow rotation of the indoor unit, a full blowout mode and a partial blowout mode are executed. In the full blowout mode, all the outlet openings blow conditioned air. In the partial blowout mode, the flow of the blowing air of part of the outlet openings are blocked by the air current blocking mechanism, and thus the blowing wind speeds of the remaining outlet openings increases. As a result, an air temperature difference among parts of the indoor space decreases, and the comfort of the indoor space is improved.

A chiller fault prediction system for a building, including one or more memory devices and one or more processors. The one or more memory devices are configured to store instructions to be executed on the one or more processors. The one or more processors are configured to receive chiller data for a plurality of chillers, the chiller data indicating performance of the plurality of chillers. The one or more processors are configured to generate, based on the received chiller data, a plurality of single chiller prediction models and a plurality of cluster chiller prediction models, the plurality of single chiller prediction models generated for each the plurality of chillers and the plurality of cluster chiller prediction models generated for chiller clusters of the plurality of chillers. The one or more processors are configured to label each of the plurality of single chiller prediction models and the plurality of cluster chiller prediction models as an accurately predicting chiller model or an inaccurately predicting chiller model based on a performance of each of the plurality of single chiller prediction models and a performance of each of the plurality of cluster chiller prediction models. The one or more processors are configured to predict a chiller fault with each of the plurality of single chiller prediction models labeled as the accurately predicting chiller models. The one or more processors are configured to predict a chiller fault for each of a plurality of assigned chillers assigned to one of a plurality of clusters labeled as the accurately predicting chiller model.

An air conditioner includes: a blower configured to discharge air, the blower being connected to an outdoor unit, a parameter generator configured to generate at least one parameter during a time period for which the air conditioner is operated with a first cooling capacity based on a set temperature, a learning unit configured to receive the generated at least one parameter as a learning factor and generate operation mode information, an operation mode controller configured to control at least one of the blower or the outdoor unit based on the generated operation mode information, and a central controller configured to control the parameter generator, the learning unit, and the operation mode controller. The air conditioner is operated with a second cooling capacity after the air conditioner is operated with the first cooling capacity for the time period, the second cooling capacity being different from the first cooling capacity.