Enerallies, Inc.

Robert S Keil

Stephen C Maruyama

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. The particular policy dataset is transformed into HVAC equipment instructions, which the server computer transmits, over a network, to HVAC equipment at the particular structure location which, when executed, cause the HVAC equipment to execute an action in accordance with the particular policy dataset.

Energy efficiency and comfort optimization through extreme weather adaptivity and AI

Systems and methods for monitoring ventilation effects in one or more locations are described herein. In various embodiments, a server computertracks COlevel changes measured for a plurality of locations over a plurality of intervals of time over a plurality of days. When the server computerreceives data indicating performance of a ventilation action at a particular location and time, the system tracks changes in COlevels 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 COlevel changes, the server computermay 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.

Intelligent ventilation monitoring, controls and optimization

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.

Forecast-based automatic scheduling of a distributed network of thermostats with learned adjustment

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.

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Los Altos, CA, United States of America

Robert S Keil