A building energy control system and method are provided. The building energy control system may include a user interface unit configured to display an energy control situation and an energy control guide that corresponds to the energy control situation to manage a building and a facility, and to receive an input according to the energy control guide and an energy control unit configured to control energy consumption in the building and the facility through control of the building and facility according to the input.

A computer-implemented system and method are disclosed for performing engineering review of energy consumption by one or more equipment. One or more computers are connected to a specifications database. The computers are configured to receive end-user configurations corresponding to first equipment; retrieve one or more second equipment, as well as second equipment specifications from the specifications database; calculate an invoicing statement for the second equipment; and provide an engineering review report of the energy savings realized h implementing the second equipment. The first equipment may be an existing equipment, while the second equipment is one or more energy-efficient replacement alternatives to the first equipment. Each step is performed in real-time, using automated software modules and sub-modules. The engineering review report is applicable as an instant approval or pre-approval, qualifying the end-user for purchase incentives, which apply when the energy-efficient equipment alternatives are adopted by the end-user.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining causal models for controlling environments. One of the methods includes repeatedly selecting control settings for the environment based on (i) a causal model that identifies causal relationships between possible settings for controllable elements in the environment and environment responses that reflect a performance of the control system in controlling the environment and (ii) current values of a set of internal parameters; and during the repeatedly selecting: monitoring environment responses to the selected control settings; determining, based on the environment responses, an indication that one or more properties of the environment have changed; and in response, modifying the current values of one or more of the internal parameters.

A first comparison-period specifier specifies a first to-be-compared period in a pre-implementation period before implementation of an energy saving control. A second comparison-period specifier specifies as a second to-be-compared period a to-be-compared candidate period having the highest similarity level between: parameters in a first parameter comparison period including a first comparison period and a first period immediately before or after the first comparison period, and parameters in a second parameter comparison period including a to-be-compared candidate period and a second period immediately before or after the to-be-compared candidate period. An energy saving diagnoser diagnoses the level of energy saving derived from the implementation of an energy saving control based on a power consumption amount in the first to-be-compared period and a power consumption amount in the second to-be-compared period.

A method includes, by a scheduling controller, receiving from a user a request for an application to be executed by a computing system associated with a data center, wherein the application includes a plurality of tasks, and wherein the request includes an estimated execution time corresponding to an estimated amount of real-world time that the tasks will be actively running on the computing system to fully execute the application. The method includes receiving from the user a service level objective indicating a target percentage of a total amount of real-world time that the tasks will be actively running on the computing system and generating, in response to determining that the job can be completed according to the service level objective and the estimated execution time, a notification indicating acceptance of the job.

A system and approach for diagnostic visualizations of, for example, building control systems data. A focus may be on a similarity metric for comparing operations among sites relative to energy consumption. Normalizing factors may be used across sites with varying equipment consumption levels to be compared automatically. There may also be a high level overview of an enterprise of sites. For instance, consumption totals of the sites may be normalized by site size and length of time of a billing period to identify such things as outlier sites. One may use a main view of geographic distribution dynamically linked to subviews showing distribution by size, by aggregated climate, and so on. With these views, one may quickly drill through the enterprise and identify sites of interest for further investigation. A key metric may be intensity which invokes viewing virtually all sites by normalized consumption for a unit amount of time.

Provided is a production control system of a factory including: a plurality of machines; an air conditioner; a power computation unit for monitoring power consumption of an entire factory; a temperature information generation unit for generating information on temperature inside the factory; wherein running status and processing condition of the machines and running status of the air conditioner are controlled to produce the products dictated by the production plan by a delivery deadline; and a machine learning unit that learns a relationship of operational status including the running status and the processing condition of the machines and the running status of the air conditioner to environmental status including production completion time according to the operational status, the temperature inside the factory, and the power consumption of the factory, and outputs operational status that brings the environmental status to a desired condition in accordance with the production plan.

The invention provides control systems and methodologies for controlling a process having computer-controlled equipment, which provide for optimized process performance according to one or more performance criteria, such as efficiency, component life expectancy, safety, emissions, noise, vibration, operational cost, or the like. More particularly, the subject invention provides for employing machine diagnostic and/or prognostic information in connection with optimizing an overall business operation over a time horizon.

Systems and methods dynamically assess energy efficiency by obtaining a minimum energy consumption of a system, receiving in a substantially continuous way a measurement of actual energy consumption of the system, and comparing the minimum energy consumption to the measurement of actual energy consumption to calculate a substantially continuous energy performance assessment. The system further provides at least one of a theoretical minimum energy consumption based at least in part on theoretical performance limits of system components, an achievable minimum energy consumption based at least in part on specifications for high energy efficient equivalents of the system components, and the designed minimum energy consumption based at least in part on specifications for the system components.

A real-time demand control system includes an analysis control module, a plurality of device management modules, and a plurality of electronic devices. The device management modules integrate electricity consumption of the electronic devices and provide the electricity consumption to the analysis control module. The analysis control module predicts whether the total electricity consumption at the next time period exceeds a contract capacity according to the present total electricity consumption. The analysis control module computes and announces reduction of electricity consumption to the device management modules when the predicted total electricity consumption is possible to exceed the contract capacity. Each device management module computes one or more state adjustment options to the analysis control module according to the reduction of electricity consumption. The analysis control module selects one or more state adjustment options to switch operation states of the electronic devices so as to reduce the total electricity consumption.