Provided are methods and apparatus that determine a benchmark of energy consumption for an energy system and utilize the determined benchmark in a variety of ways. Once determined, the determined benchmark may be compared to current energy consumption and current outdoor temperature to verify proper operation of the system and identify deviations in system operation. The benchmark may be used to determine the impact of an implemented change in the operation of the system. The determined benchmark may also be used to contract for a term in an agreement and to verify the satisfaction of a term in an agreement.

A system that allows a contractor to remotely monitor and/or interact with its customers' building control systems, such as heating, ventilating and air conditioning (HVAC) systems, and analyze information obtained from the building control systems over time. Such a system may help the contractor monitor and diagnosis customer building control systems, setup service calls, achieve better customer relations, create more effective marketing opportunities, as well as other functions. In some cases, the disclosed system may include a controller that analyzes data from HVAC systems, determines a thermal model of a space environmentally controlled by an HVAC system, and provides an energy audit of the space that is environmentally controlled by the HVAC system. The controller may output a result of the energy audit to a user.

Processing of data relating to energy usage. First data relating to energy usage is loaded for analysis by an energy management platform. Second data relating to energy usage is stream processed by the energy management platform. Third data relating to energy usage is batch parallel processed by the energy management platform. Additional computing resources, owned by a third party separate from an entity that owns the computer system that supports the energy management platform, are provisioned based on increasing computing demand. Existing computing resources owned by the third party are released based on decreasing computing demand.

Disclosed are exemplary process and system for managing a mix design catalog of a concrete producer that involves collecting slump curve data obtained during in-transit monitoring of delivered concrete loads made from a plurality of various mix designs, wherein each mix design is identified by a different identification code (regardless of whether components are different), clustering slump curve data having same movement characteristics according to assigned strength value, and selecting a mix design to produce, to display, or both to produce and to display, from among the two or more slump data curves of individual mix designs within the same data curve cluster. The selection is based on same movement characteristic and assigned strength value, and at least one factor relative to cost, performance, physical aspect, quality, or other characteristic of the concrete mix or its components. Exemplary methods for generating new mix designs are also disclosed.

Provided is an energy-efficiency enhancement platform system based on submetering data. The system includes a platform server through which a demand source that needs to enhance energy efficiency and a supply source that provides at least one of energy-efficiency enhancement equipment, an energy management system, and an energy analysis consulting service share submetering data. The platform server includes a data collection module that collects the submetering data that is data actually measured on a per-equipment basis from the demand source, a data provision module that provides the supply source with the collected submetering data, an analysis module that analyzes an energy-efficiency enhancement proposal received from the supply source and the collected submetering data, and an efficiency-enhancement proposal module that provides the demand source with an energy-efficiency enhancement plan on the basis of a result of the analysis by the analysis module.

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.

The present disclosure provides a computer-implemented method for ranking one or more control schemes for controlling peak loading conditions and abrupt changes in energy pricing of one or more built environments associated with renewable energy sources. The computer-implemented method includes analysis of a first set of statistical data, a second set of statistical data, a third set of statistical data, a fourth set of statistical data and a fifth set of statistical data. Further, the computer-implemented method includes identification and execution of the one or more control schemes. In addition, the computer-implemented method includes scoring the one or more control schemes by evaluating a probabilistic score. Further, the computer-implemented method includes ranking the one or more control schemes to determine relevant control schemes for controlling real time peak loading conditions and abrupt changes in energy pricing associated with the one or more built environments.

A computer-implemented method and system is provided. The system manipulates load curves corresponding to time-variant energy demand and consumption of a built environment. The system analyzes a first, second, third, fourth and a fifth set of data. The first set of data is associated with energy consuming devices. The second set of data is associated with an occupancy behavior of users. The third set of data is associated with energy storage and supply means. The fourth set of data is associated with environmental sensors. The fifth set of data is associated with energy pricing models. The system executes control routines for controlling peak loading conditions associated with the built environment. The system manipulates an optimized operating state of the energy consuming devices. The system integrates the energy storage and supply means for optimal reduction of the peak level of energy demand concentrated over the limited period of time.

A method monitors the energy cost for the production of a product lot using a manufacturing execution system (MES) that enables the operator of a production facility to optimize the production process in terms of energy costs. The method includes a) executing a production process being scheduled and controlled by the MES to produce the product lot; b) for each individual production step measuring the energy consumption over the course of the execution of the individual production step; c) creating a data model within the MES that correlates production specific data and the energy consumption data related to the product lot; d) defining commands to manage the production specific data and the energy consumption data wherein the commands are web APIs; and e) evaluating the production specific data and the energy consumption data and creating an energy consumption profile for the production process related to the product lot.

The present disclosure provides a computer-implemented method for prioritizing one or more instructional control strategies to reduce time-variant energy demand of a built environment associated with renewable energy sources. The computer-implemented method includes collection of a first set of statistical data, fetching of a second set of statistical data, accumulation of a third set of statistical data, reception of a fourth set of statistical data and gathering of fifth set of statistical data. Further, the computer-implemented method includes parsing and comparison of the first set of statistical data, the second set of statistical data, the third set of statistical data, the fourth set of statistical data and the fifth set of statistical data. In addition, the computer-implemented method includes identification and prioritization of one or more instructional control strategies to reduce the time-variant energy demand associated with the built environment.