Abstract: Systems and methods for monitoring ventilation effects in one or more locations are described herein. In various embodiments, a server computer 120 tracks CO2 level changes measured for a plurality of locations over a plurality of intervals of time over a plurality of days. When the server computer 120 receives data indicating performance of a ventilation action at a particular location and time, the system tracks changes in CO2 levels to determine whether the device is operating optimally and, if the device is not operating optimally, sends a notification to a client computing device. Using tracked CO2 level changes, the server computer 120 may identify ventilation actions to perform at different points in time and send instructions to a ventilation system which, when executed, cause performance of the ventilation actions.

Abstract: Techniques for providing a machine learning-enhanced distributed energy resource management system are provided. In one technique, a machine-learning (ML) model is trained based on a training dataset that comprises historical demand response (DR) event data and historical weather data. The trained ML model is used to predict a load capacity to be made available for an upcoming DR event based, at least in part, on current DR event data and weather data. The predicted load capacity made available for an upcoming DR event is determined to be not sufficient to balance energy supply and demand during the upcoming DR event. Responsive to this determination, one or more load capacity increasing actions are automatically performed. Examples of such actions include increasing a level of participation of a set of dynamically-enrolled customers and causing a request for additional participation in load-shedding to be sent to one or more customers.

Abstract: Techniques for providing an adaptive energy management system for responding to extreme weather conditions are described herein. In an embodiment, a server computer stores multiple policy datasets each representing HVAC control policy for different structure locations and one or more extreme weather conditions. The server computer receives weather condition data comprising a condition identifier of a then-current extreme weather condition in association with a location identifier specifying a particular geographical region. Based on the location identifier, a particular structure location, being within the particular geographic region, is identified. Based on the particular structure location, a particular policy dataset from among the policy datasets is identified.

Abstract: Techniques for providing an adaptive energy management system for responding to extreme weather conditions are described herein. In an embodiment, a server computer stores multiple policy datasets each representing HVAC control policy for different structure locations and one or more extreme weather conditions. The server computer receives weather condition data comprising a condition identifier of a then-current extreme weather condition in association with a location identifier specifying a particular geographical region. Based on the location identifier, a particular structure location, being within the particular geographic region, is identified. Based on the particular structure location, a particular policy dataset from among the policy datasets is identified.

Abstract: Heating and cooling systems at various geographical locations are controlled by a central energy management service unit to maintain comfortable indoor temperatures. In some weather conditions, people may intuitively prefer a slightly warmer or cooler indoor temperature. In systems equipped with environmental learning capabilities, an apparent outdoor temperature is determined based on the geographic location itself, the season at the geographic location, the forecasted actual temperature, and one or more seasonal weather factors such as wind velocity or humidity. The apparent temperature and a trained machine learning system are used to select an automated schedule for the geographic location to be directly transmitted to devices at the location. The automated schedule can vary from typical schedules by causing the heating and cooling systems to maintain a temperature that is slightly warmer or cooler than typical indoor temperatures.

Abstract: Systems and methods for monitoring ventilation effects in one or more locations are described herein. In various embodiments, a server computer 120 tracks CO2 level changes measured for a plurality of locations over a plurality of intervals of time over a plurality of days. When the server computer 120 receives data indicating performance of a ventilation action at a particular location and time, the system tracks changes in CO2 levels to determine whether the device is operating optimally and, if the device is not operating optimally, sends a notification to a client computing device. Using tracked CO2 level changes, the server computer 120 may identify ventilation actions to perform at different points in time and send instructions to a ventilation system which, when executed, cause performance of the ventilation actions.

Abstract: The disclosure provides an energy management system that is based on a distributed architecture that includes networked energy management devices located at a plurality of sites and a collection of energy management program applications and modules implemented by a centralized energy management service unit. The energy management program applications and modules are responsible for facilitating customer access to the system, configuring energy management devices, and collecting, storing, and analyzing energy management data collected from the plurality of sites. The energy management system is adaptable to a wide variety of energy usage requirements and enables customers to configure energy management devices at customer sites using scheduling templates, to define and customize site groupings for device configuration and data analysis purposes, and to request and view various statistical views of collected energy usage data.

Abstract: Heating and cooling systems at various geographical locations are controlled by a central energy management service unit to maintain comfortable indoor temperatures. In some weather conditions, people may intuitively prefer a slightly warmer or cooler indoor temperature. In systems equipped with environmental learning capabilities, an apparent outdoor temperature is determined based on the geographic location itself, the season at the geographic location, the forecasted actual temperature, and one or more seasonal weather factors such as wind velocity or humidity. The apparent temperature and a trained machine learning system are used to select an automated schedule for the geographic location to be directly transmitted to devices at the location. The automated schedule can vary from typical schedules by causing the heating and cooling systems to maintain a temperature that is slightly warmer or cooler than typical indoor temperatures.

Abstract: Heating and cooling systems at various geographical locations are controlled by a central energy management service unit to maintain comfortable indoor temperatures. In some weather conditions, people may intuitively prefer a slightly warmer or cooler indoor temperature. In systems equipped with environmental learning capabilities, an apparent outdoor temperature is determined based on the geographic location itself, the season at the geographic location, the forecasted actual temperature, and one or more seasonal weather factors such as wind velocity or humidity. The apparent temperature and a trained machine learning system are used to select an automated schedule for the geographic location to be directly transmitted to devices at the location. The automated schedule can vary from typical schedules by causing the heating and cooling systems to maintain a temperature that is slightly warmer or cooler than typical indoor temperatures.

Abstract: Heating and cooling systems at various geographical locations are controlled by a central energy management service unit to maintain comfortable indoor temperatures. In some weather conditions, people may intuitively prefer a slightly warmer or cooler indoor temperature. In systems equipped with environmental learning capabilities, an apparent outdoor temperature is determined based on the geographic location itself, the season at the geographic location, the forecasted actual temperature, and one or more seasonal weather factors such as wind velocity or humidity. The apparent temperature and a trained machine learning system are used to select an automated schedule for the geographic location to be directly transmitted to devices at the location. The automated schedule can vary from typical schedules by causing the heating and cooling systems to maintain a temperature that is slightly warmer or cooler than typical indoor temperatures.

Abstract: Systems and methods for providing an adaptive energy management system for responding to extreme weather conditions are described herein. In an embodiment, a server computer stores multiple different policy datasets each representing HVAC control policy for a plurality of different structure locations and one or more different extreme weather conditions. The server computer receives weather condition data comprising a condition identifier of a then-current extreme weather condition in association with a location identifier specifying a particular geographical region. Based on the location identifier, the server computer identifies a particular structure location from among the plurality of different structure locations, the particular structure location being within the particular geographic region. Based on the particular structure location, the server computer selects a particular policy dataset from among the policy datasets.

Abstract: Heating and cooling systems at various geographical locations are controlled by a central energy management service unit to maintain comfortable indoor temperatures. In some weather conditions, people may intuitively prefer a slightly warmer or cooler indoor temperature. In systems equipped with environmental learning capabilities, an apparent outdoor temperature is determined based on the geographic location itself, the season at the geographic location, the forecasted actual temperature, and one or more seasonal weather factors such as wind velocity or humidity. The apparent temperature and a trained machine learning system are used to select an automated schedule for the geographic location to be directly transmitted to devices at the location. The automated schedule can vary from typical schedules by causing the heating and cooling systems to maintain a temperature that is slightly warmer or cooler than typical indoor temperatures.

Abstract: The disclosure provides an energy management system that is based on a distributed architecture that includes networked energy management devices located at a plurality of sites and a collection of energy management program applications and modules implemented by a centralized energy management service unit. The energy management program applications and modules are responsible for facilitating customer access to the system, configuring energy management devices, and collecting, storing, and analyzing energy management data collected from the plurality of sites. The energy management system is adaptable to a wide variety of energy usage requirements and enables customers to configure energy management devices at customer sites using scheduling templates, to define and customize site groupings for device configuration and data analysis purposes, and to request and view various statistical views of collected energy usage data.

Abstract: The disclosure provides an energy management system that is based on a distributed architecture that includes networked energy management devices located at a plurality of sites and a collection of energy management program applications and modules implemented by a centralized energy management service unit. The energy management program applications and modules are responsible for facilitating customer access to the system, configuring energy management devices, and collecting, storing, and analyzing energy management data collected from the plurality of sites. The energy management system is adaptable to a wide variety of energy usage requirements and enables customers to configure energy management devices at customer sites using scheduling templates, to define and customize site groupings for device configuration and data analysis purposes, and to request and view various statistical views of collected energy usage data.

Abstract: The disclosure provides an energy management system that is based on a distributed architecture that includes networked energy management devices located at a plurality of sites and a collection of energy management program applications and modules implemented by a centralized energy management service unit. The energy management program applications and modules are responsible for facilitating customer access to the system, configuring energy management devices, and collecting, storing, and analyzing energy management data collected from the plurality of sites. The energy management system is adaptable to a wide variety of energy usage requirements and enables customers to configure energy management devices at customer sites using scheduling templates, to define and customize site groupings for device configuration and data analysis purposes, and to request and view various statistical views of collected energy usage data.

Abstract: Heating and cooling systems at various geographical locations are controlled by a central energy management service unit to maintain comfortable indoor temperatures. In some weather conditions, people may intuitively prefer a slightly warmer or cooler indoor temperature. In systems equipped with environmental learning capabilities, an apparent outdoor temperature is determined based on the geographic location itself, the season at the geographic location, the forecasted actual temperature, and one or more seasonal weather factors such as wind velocity or humidity. The apparent temperature and a trained machine learning system are used to select an automated schedule for the geographic location to be directly transmitted to devices at the location. The automated schedule can vary from typical schedules by causing the heating and cooling systems to maintain a temperature that is slightly warmer or cooler than typical indoor temperatures.

Abstract: The disclosure provides an energy management system that is based on a distributed architecture that includes networked energy management devices located at a plurality of sites and a collection of energy management program applications and modules implemented by a centralized energy management service unit. The energy management program applications and modules are responsible for facilitating customer access to the system, configuring energy management devices, and collecting, storing, and analyzing energy management data collected from the plurality of sites. The energy management system is adaptable to a wide variety of energy usage requirements and enables customers to configure energy management devices at customer sites using scheduling templates, to define and customize site groupings for device configuration and data analysis purposes, and to request and view various statistical views of collected energy usage data.

Abstract: The disclosure provides an energy management system that is based on a distributed architecture that includes networked energy management devices located at a plurality of sites and a collection of energy management program applications and modules implemented by a centralized energy management service unit. The energy management program applications and modules are responsible for facilitating customer access to the system, configuring energy management devices, and collecting, storing, and analyzing energy management data collected from the plurality of sites. The energy management system is adaptable to a wide variety of energy usage requirements and enables customers to configure energy management devices at customer sites using scheduling templates, to define and customize site groupings for device configuration and data analysis purposes, and to request and view various statistical views of collected energy usage data.

Abstract: The disclosure provides an energy management system that is based on a distributed architecture that includes networked energy management devices located at a plurality of sites and a collection of energy management program applications and modules implemented by a centralized energy management service unit. The energy management program applications and modules are responsible for facilitating customer access to the system, configuring energy management devices, and collecting, storing, and analyzing energy management data collected from the plurality of sites. The energy management system is adaptable to a wide variety of energy usage requirements and enables customers to configure energy management devices at customer sites using scheduling templates, to define and customize site groupings for device configuration and data analysis purposes, and to request and view various statistical views of collected energy usage data.

Abstract: The disclosure provides an energy management system that is based on a distributed architecture that includes networked energy management devices located at a plurality of sites and a collection of energy management program applications and modules implemented by a centralized energy management service unit. The energy management program applications and modules are responsible for facilitating customer access to the system, configuring energy management devices, and collecting, storing, and analyzing energy management data collected from the plurality of sites. The energy management system is adaptable to a wide variety of energy usage requirements and enables customers to configure energy management devices at customer sites using scheduling templates, to define and customize site groupings for device configuration and data analysis purposes, and to request and view various statistical views of collected energy usage data.