Air purification and dehumidification apparatus includes a first cooler that cools air introduced through a first inlet, a first rotor that primarily adsorbs and absorbs VOCs and moisture contained in the air cooled by the first cooler, an air conditioning unit that cools or heats the air primarily purified and dehumidified by the first rotor, a blower that moves the air cooled or heated by the air conditioning unit, a second rotor that adsorbs and absorbs VOCs and moisture remaining in the air moved by the blower, a second cooler that re-cools the air secondarily purified and dehumidified by the second rotor, a first heating unit that heats air that is introduced through a second inlet and is then supplied to the first rotor, using sequentially solar energy and electric energy, and a third cooler that condenses air containing the VOCs and moisture that are released from the first rotor.

Provided are a system and a method for predicting an occupancy time of a user in a city building based on big data for auto control of heating or cooling for energy saving. The user occupancy time prediction system according to an embodiment includes: a sensor configured to collect data regarding whether a user occupies a predetermined space in a building; a database configured to store the collected data; a data pre-processing unit configured to process the stored data into a format suitable for machine learning; and a prediction unit configured to input the processed data into a machine learning model, and to predict an expected unoccupancy time of the user regarding the predetermined space in the building. Accordingly, a user occupancy/unoccupancy time may be predicted by analyzing big data which uses previous occupancy data of a user in a city building, and energy may be saved by adjusting a temperature of a heating or cooling device before the unoccupancy time. In addition, when the temperature of the heating or cooling device is adjusted before unoccupancy is predicted, the temperature is only changed to the extent that the user does not recognize inconvenience, and comfortability may be maintained or improved.

A system for climate control comprises a controller, comprising a processor and a non-transitory computer-readable medium with instructions stored thereon, a plurality of sensors communicatively connected to the controller, and at least one HVAC component communicatively connected to the controller, wherein the instructions, when executed by the processor, perform steps comprising receiving sensor data from at least one sensor of the plurality of sensors, executing a machine learning model using the received sensor data as inputs, calculating a predicted temperature, humidity, or occupancy state from the machine learning model, and sending a control instruction to the at least one HVAC component based on the calculated temperature, humidity, or occupancy state. A method for training a machine learning algorithm for a climate control system and a method for HVAC control in a building are also described.

A Heating, Ventilating Air Conditioning (HVAC) fan control method to detect an HVAC fan is controlled by at least one fan-on duration control selected by a user and provide at least one fan-on alarm message prior to overriding the at least one fan-on duration control. The method monitors an occupancy sensor signal to determine an occupancy in a conditioned space served by an HVAC system and automatically overrides the at least one fan-on duration control to save energy when the conditioned space is unoccupied. The overriding may comprise operating the HVAC fan based only on a thermostat call for cooling or a thermostat call for heating, operating the HVAC fan for less time than the at least one fan-on duration control selected by the user, and operating the HVAC fan for less frequency than the at least one fan-on duration control selected by the user.

An air-conditioning system includes a plurality of air-conditioning apparatuses that, in an indoor space that is divided into a plurality of air-conditioned areas, each condition air in a corresponding one of the plurality of air-conditioned areas, a wireless communication module that communicates with an information communication terminal to which pieces of setting information that are each set for a corresponding one of the plurality of air-conditioning apparatuses and represent operational details of the air-conditioning apparatus are input, and a controller that controls the plurality of air-conditioning apparatuses on the basis of the pieces of setting information communicated via the wireless communication module.

Environmental characteristics of habitable environments (e.g., hotel or motel rooms, spas, resorts, cruise boat cabins, offices, hospitals and/or homes, apartments or residences) are controlled to eliminate, reduce or ameliorate adverse or harmful aspects and introduce, increase or enhance beneficial aspects in order to improve a “wellness” or sense of “wellbeing” provided via the environments. Control of intensity and wavelength distribution of passive and active Illumination addresses various issues, symptoms or syndromes, for instance to maintain a circadian rhythm or cycle, adjust for “jet lag” or season affective disorder, etc. Air quality and attributes are controlled. Scent(s) may be dispersed. Hypoallergenic items (e.g., bedding, linens) may be used. Water quality is controlled. Noise is reduced and sounds (e.g., masking, music, natural) may be provided. Passive and active pathogen controls are employed. Controls are provided for the occupant and/or facility personnel, as is instruction, and surveys, including assessing wellness.

A visible light sensor may be configured to sense environmental characteristics of a space using an image of the space. The visible light sensor may be controlled in one or more modes, including a daylight glare sensor mode, a daylighting sensor mode, a color sensor mode, and/or an occupancy/vacancy sensor mode. In the daylight glare sensor mode, the visible light sensor may be configured to decrease or eliminate glare within a space. In the daylighting sensor mode and the color sensor mode, the visible light sensor may be configured to provide a preferred amount of light and color temperature, respectively, within the space. In the occupancy/vacancy sensor mode, the visible light sensor may be configured to detect an occupancy/vacancy condition within the space and adjust one or more control devices according to the occupation or vacancy of the space. The visible light sensor may be configured to protect the privacy of users within the space via software, a removable module, and/or a special sensor.

A control system for controlling consumption of energy in an environment including a controller having a processor and a real time clock, the real time clock being configured to coordinate operations of the processor with timing of the designated peak period, the processor being configured to determine a minimum pre-operating period and to signal an air conditioning and/or space heating system to enter an operating state for at least the minimum pre-operating period before the designated peak period begins and enter a non-operating state after the designated peak period begins; an operating program having processing parameters and processing procedure for determining the minimum pre-operating period, wherein the processing parameters and processing procedure comprise determining the minimum pre-operating period using thermal mass of the environment, costs for the consumption of energy, and efficiency of the air conditioning and/or space heating system.

A system for assessing an indoor space for favorable conditions for the transmission of a viral particle is disclosed. A set of sensors measure a set of environmental parameters, and a processor is configured to combine the measured set of environmental parameters into a single scale of numerical or descriptive value representing the favorability of viral transmission in the indoor space.