During an initial period of time, an HVAC controller stores a record of an energy demand of the HVAC system that corresponds to an amount of energy used to operate the HVAC system. For a future time period, an anticipated energy demand of the HVAC system is determined. The controller then recursively determines, for each of a plurality of time points within the future time period, an anticipated indoor humidity value using the anticipated energy demand and the record of the energy demand. The HVAC system is operated based at least in part on the anticipated indoor humidity value.

Methods and apparatus for an exhaust demand control system for measuring one or more contaminants at one or more exhaust locations within one or a plurality of exhaust ducts or plenums served by an exhaust fan system. Example systems and methods can include sensing the one or more contaminants within the one or more exhaust duct locations using a multipoint air sampling system having one or more sensors and comparing contaminant concentration measurements from the one or more of said exhaust duct or plenum locations against an action level to create a fan setback signal.

A method of controlling a heating, ventilation, and air-conditioning, HVAC, system. The method includes: controlling indoor environmental conditions using the HVAC system, detecting a load of the HVAC system, inputting detected indoor environmental conditions and detected outdoor environmental conditions into a load prediction model to generate a predicted load, and training the load prediction model to reduce a difference between the predicted load and the detected load of the HVAC system; determining requested indoor environmental conditions associated with a future time period; determining predicted outdoor environmental conditions within the future time period using a weather forecast; inputting the requested indoor environmental conditions and the predicted outdoor environmental conditions into the trained load prediction model to determine a predicted load for the future time period; controlling the HVAC system to reduce a load of the HVAC system within the future time period using the determined predicted load.

An integrated system and method measures building characteristics and user behavior to provide real-time and forecasted utility usages and costs. The system gathers current and historical heating and cooling load data, compares the data with current and historical weather data and a building system set point, and calculates the heating or cooling load needed for the building based on the user's call for heat or cooling and the ambient environmental conditions. The system additionally analyzes individual device usage using usage signatures and user inputted tracking to create a comprehensive real-time and forecast of utility usages with the estimated costs. Through history of selections with usage changes corresponding to user input of individual devices, the system will be able to learn various devices' usage. The system then creates a comprehensive, real-time forecast of utility costs including the foregoing characteristics.

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.

An operation method for reducing energy consumption and an electronic apparatus thereof are provided. The operation method includes obtaining, by the electronic apparatus, weather forecast information, inputting, by the electronic apparatus, the weather forecast information to an artificial intelligence model for predicting an amount of power to be consumed by a first air conditioner, and displaying, by the electronic apparatus, the predicted power consumption amount of the first air conditioner output from the artificial intelligence model, wherein the artificial intelligence model is trained to obtain correlation information between a weather condition and a power consumption amount of an air conditioner, based on a weather history and operations of a plurality of air conditioners related to the weather history, and predict the amount of power to be consumed by the first air conditioner based on the correlation information and the weather forecast information.

A building system for a building including one or more storage devices storing instructions thereon that, when executed by one or more processors, cause the one or more processors to operate one or more pieces of building equipment associated with a building space based on a first operating mode. The instructions cause the one or more processors to receive an indication to update operation of the building space based on an emergency situation and responsive to receiving the indication to update operation of the building space based on the emergency situation, operate the one or more pieces of building equipment based on a second operating mode, wherein the second operating mode defines one or more second parameters for the one or more pieces of building equipment and is adapted to reconfigure the operation of the building space to address or mitigate the emergency situation.

An information processing apparatus according to the present application includes an acquisition unit, a determination unit, and an appliance control unit. The acquisition unit acquires recipe information which is information regarding cooking performed by the user. Based on the recipe information acquired by the acquisition unit, the determination unit determines a non-cooking appliance that is an appliance used by the user and is not used for cooking to be an appliance being a control target. The appliance control unit controls the non-cooking appliance determined by the determination unit at a timing before a predetermined process based on a predetermined condition.

Techniques for performing an emissions demand response event are described. In an example, a cloud-based HVAC control server system obtains a history of emissions rates. Based on the history of emissions rates, a future time period of predicted high emissions is identified. An emission demand response event participation level of an account mapped to a thermostat is determined for the future time period of predicted high emissions. The emissions demand response event participation level may be one of a plurality of emissions demand response event participation levels. based on the emissions demand response event participation level of the account, an emissions demand response event is generated during the future time period of predicted high emissions. The cloud-based HVAC control server system then causes a thermostat to control an HVAC system in accordance with the generated emissions demand response event.

Techniques for performing an emissions demand response event are described. In an example, a cloud-based HVAC control server system obtains an emissions rate forecast for a predefined future time period. Using the emissions rate forecast, a future emissions rate event during the predefined future time period is identified. The future emissions rate event comprises an indication of predicted magnitude and a time period when a predicted emissions rate will be at an increased or decreased level. A confidence value indicating a certainty of the future emissions rate event occurring as predicted is determined. Based on the identified future emissions rate event and the confidence value, an emissions demand response event having a start time and an end time during the future emissions rate event is generated. The cloud-based HVAC control server system then causes a thermostat to control an HVAC system in accordance with the generated emissions demand response event.