Optimum Energy, LLC

Peng Chen

Ian Robert Dempster

Clark Matthys

Thomas Berry Jones

Ben Erpelding

Chris Carter

Chris Boscolo

Andrew Alexander Lechner

Computer-implemented methods and structures deploy a heating ventilation and air conditioning (HVAC) energy optimization program. A standard operating control platform (OCP) is deployed in an energy optimization control engine (EOCE) computing system communicatively coupled to a plurality of HVAC components via a building automation system (BAS). An energy optimization portal (EOP), which receives from the EOCE computing system a first data set identifying the plurality of HVAC components, a second data set including operational control parameters for each of the plurality of HVAC components, and a third data set including measured operations data associated with each of the plurality of HVAC components. The EOP generates an energy optimized operating control platform based on the first, second, and third data sets, which is automatically communicated from the EOP to the EOCE computing system.

Remote automated deployment of HVAC optimization software

The present disclosure describes a solution to monitor, control and share HVAC operation state information and the analysis thereof based on a distributed computing system involving local building automation servers (BAS), a network based (cloud-based) system and client terminals. On the network based system, a client level virtual machine launches a container process for a client, which includes a background sub-process and a foreground sub-process. The background sub-process collects and analyzes HVAC data without client interaction. The foreground sub-process is setup upon the launching of the container process, but is fully activated until suitable client interaction is detected. The foreground sub-process pushes for a more comprehensive set of HVAC operation data through the BAS server and analyzes and presents the data and analysis result in substantially real time to the client through the client terminal with a higher data updating rate than the background sub-process.

Controlling HVAC optimization using real time data

An environmental control system for a building in a geographic location and having a heating, ventilation and air-conditioning (HVAC) system includes at least one computer-readable medium having instructions stored thereon that, when executed by at least one processing device in communication with the external application, enables the at least one processing device to receive weather data characterizing a weather forecast over a predetermined time period for the geographic location, receive free-cooling window data, determine, based on the weather data and free-cooling window data, an available free-cooling time window, and issue to the external application an executable command to the HVAC system to enter free-cooling mode during the available free-cooling time window.

Predictive free cooling

Systems and methods for sequencing HVAC equipment of an HVAC system using data recorded in situ to build a model capable of making predictions about equipment efficiency and using that information, in combination with predictions about building load, to produce an operational sequence for the HVAC equipment that promotes an improved or optimized overall energy efficiency for the HVAC system. In one embodiment, the process is automated and utilizes Bayesian computational models or algorithms to generate an initial sequence. The process reduces engineering hours and may advantageously provide a means to predict potential sequencing problems for similar types of HVAC equipment.

Intelligent equipment sequencing

Systems and methods for sequencing HVAC equipment of an HVAC system using data recorded in situ to build a model capable of making predictions about equipment efficiency and using that information, in combination with predictions about building load, to produce an operational sequence for the HVAC equipment that promotes an improved or optimized overall energy efficiency for the HVAC system. In one embodiment, the process is automated and utilizes Bayesian computational models or algorithms to generate are initial sequence. The process reduces engineering hours and may advantageously provide a means to predict potential sequencing problems for similar types of HVAC equipment.

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Seattle, WA, United States of America

Peng Chen