A fan system may include at least one fan unit configured to be mounted in a window opening, the at least one fan unit including a blower unit configured to blow air through the window opening. The fan system may further include a controller including a device processor and a computer readable medium having stored thereon instructions, executable by the device processor, for performing the following steps: receiving weather forecast data; and controlling operation of the at least one fan unit based on the received weather forecast data. Operation of the at least one fan unit may be controlled to effectuate healthy air exchange within the household envelope.

An HVAC control system having a cloud-based optimization engine in communication with a local building hub that interfaces with the building HVAC system and room units. The cloud-based optimization engine implements an optimal and predictive control strategy to integrate occupancy prediction, weather forecasting, and modeling of indoor infection risk, indoor air quality, and building energy consumptions. The control strategy includes a model-based predictive control and a model-free reinforcement learning approach. The control strategy considers outdoor weather (both thermal and air quality) conditions, indoor occupancy and requirements for IAQ and infectious risk reduction to decide whether outdoor air should be introduced and how much fresh air will be introduced into the space. Communications with the building hub allow the local HVAC unit to be driven according to the optimization plan. Individual room sensing units can provide local sensor data to the cloud-based optimization engine.

An electronic apparatus is provided. The electronic apparatus includes a communication interface, a memory, and a processor. The processor according to the disclosure is configured to acquire weather information and information on a space where an air conditioner is installed, train a neural network model based on the weather information and the information on the space, based on acquiring driving information of the air conditioner and a measured temperature of the space through the communication interface, input the measured temperature of the space and the external temperature into the neural network model and acquire predicted temperature information per time for the space, determine whether a defect exists in the air conditioner based on the predicted temperature information and the measured temperature of the space, and based on determining that a defect exists in the air conditioner, generate a notification signal.

A Thermal Performance Forecast approach is described that can be used to forecast heating and cooling fuel consumption based on changes to user preferences and building-specific parameters that include indoor temperature, building insulation, HVAC system efficiency, and internal gains. A simplified version of the Thermal Performance Forecast approach, called the Approximated Thermal Performance Forecast, provides a single equation that accepts two fundamental input parameters and four ratios that express the relationship between the existing and post-change variables for the building properties to estimate future fuel consumption. The Approximated Thermal Performance Forecast approach marginally sacrifices accuracy for a simplified forecast. In addition, the thermal conductivity, effective window area, and thermal mass of a building can be determined using different combinations of utility consumption, outdoor temperature data, indoor temperature data, internal heating gains data, and HVAC system efficiency as inputs.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for providing an estimate of cost of a claim to a homeowner. One of the methods includes receiving information about damage to a home of a homeowner. The method includes retrieving a home insurance policy associated with the homeowner and home insurance claims that have been submitted by other homeowners. The method also includes analyzing the information about the damage, the home insurance policy, and the home insurance claims to determine an estimate of cost of a claim that would cover the damage. In addition, the method includes providing the homeowner with the estimate of cost of the claim.

A thermostat for a building zone includes at least one of a model predictive controller and an equipment controller. The model predictive controller is configured to obtain a cost function that accounts for a cost of operating HVAC equipment during each of a plurality of time steps, use a predictive model to predict a temperature of the building zone during each of the plurality of time steps, and generate temperature setpoints for the building zone for each of the plurality of time steps by optimizing the cost function subject to a constraint on the predicted temperature. The equipment controller is configured to receive the temperature setpoints generated by the model predictive controller and drive the temperature of the building zone toward the temperature setpoints during each of the plurality of time steps by operating the HVAC equipment to provide heating or cooling to the building zone.

A heating, ventilation, and air conditioning (HVAC) control device is configured to record a plurality of actual occupancy statuses, to determine a plurality of corresponding predicted occupancy statuses, and to compare the plurality of predicted occupancy statuses to the plurality of actual occupancy statuses. The device is further configured to identify conflicting occupancy statuses based on the comparison. A conflicting occupancy status indicates a difference between an actual occupancy status and a corresponding predicted occupancy status. The device is further configured to identify timestamps corresponding with the conflicting occupancy statuses, to identify historical occupancy statuses corresponding with the identified timestamps, and to update the conflicting occupancy statuses in the predicted occupancy schedule with the historical occupancy statuses.

The present disclosure relates to a thermal management system configured to control a temperature of a building and having a controller configured to determine an adjustment to a window setting of the building based on current weather data, forecasted weather data, or both. The thermal management system also includes a display configured to display instructions related to the adjustment.

Controlling heating and cooling in a conditioned space utilizes a fluid circulating in a thermally conductive structure in fluid connection with a hydronic-to-air heat exchanger and a ground heat exchanger. Air is moved past the hydronic-to-air heat exchanger, the air having fresh air supply and stale air exhaust. Sensors located throughout the conditioned space send data to a controller. User input to the controller sets the desired set point temperature and humidity. Based upon the set point temperature and humidity and sensor data, the controller sends signals to various devices to manipulate the flow of the fluid and the air in order to achieve the desired set point temperature and humidity in the conditioned space. The temperature of the fluid is kept less than the dew point at the hydronic-to-air heat exchanger and the temperature of the fluid is kept greater than the dew point at the thermally conductive structure.

A device detects that a first wearable device has connected to a network. The device receives user preferences from the first wearable device. The device identifies a set point corresponding to a climate parameter. The device receives climate information corresponding to the climate parameter from one or more sensors. The device determines whether the climate information is within a threshold percentage of the set point, and based on the determination that the climate information is not within the threshold percentage of the set point, the device adjusts the set point.