Siemens Corporation

Siemens Aktiengesellschaft

Yan Lu

George Lo

Zhen Song

Gerhard Zimmermann

Dong Wei

Kun Ji

Ling Shen

Siyuan Zhou

Jianmin Zhu

Lingyun Wang

Livio Dalloro

Mohammad Abdullah Al Faruque

Xianjun Sam Zheng

Peng Zhao

Yanzi Chen

Shaopeng Zhang

Mohsen Jafari

In order to reduce computation time and cost involved with detecting and diagnosing a fault in a system, simplified representations of components of the system are used to estimate valid intervals for state variables at the components. Generic failure rules are configured to compare the estimated valid intervals to related intervals for the same state variables, from either observations or propagations, for overlap. Failure output vectors are generated based on the comparison, and the failure output vectors are compared to diagnostic matrices to determine a source of the fault.

Simulation based fault diagnosis using extended heat flow models

A method of controlling energy consumption in a building includes receiving occupancy data including at least one of occupant request data and occupant schedule data, receiving weather data including at least one of current weather measurement data and weather forecast data, generating an output control signal based on the occupancy data and the weather data, and transmitting the output control signal to a building automation system (BAS) of the building. The occupant request data includes a current request, and the occupant schedule data includes a predicted occupant schedule. The output control signal adjusts a building control device in a zone in the building.

System and method of energy management control

A computer-implemented method of optimizing demand-response (DR) of a heating, ventilation, and air-conditioning (HVAC) system of a building, includes determining () a value of an objective function Fof a HVAC system for each of a plurality of DR strategies j for each of a plurality of weather patterns i that is a weighted sum of an energy cost of the HVAC system and a thermal comfort loss of the HVAC system, assigning () a likelihood score Lto each of a selected subset of near-optimal DR strategies j for each weather pattern i, and selecting () those near-optimal DR strategies with large overall likelihood scores Lto create an optimal strategy pool of DR strategies. An optimal strategy pool can be searched () in real-time for an optimal DR strategy for a given weather pattern.

On-line optimization scheme for HVAC demand response

A building has climate control equipment which controls a temperature at different locations. Different locations may be in different control zones controlled by different control devices. An occupant of a location submits a desired location temperature through a user interface on a computing device to a networked server. Setting of a desired temperature is constrained by energy saving policies and by conditions of surrounding locations. An arbitrator device determines based on constraints a new temperature setting. The new temperature setting is accompanied by an energy saving feedback. The occupants confirms the new setting. A climate control device is instructed to apply a device setting to achieve the new temperature. A climate profile of the occupant is learned from previous temperature settings by the occupant.

Social learning softthermostat for commercial buildings

A method including: computing a total cost for a first zone in a building, wherein the total cost is equal to an actual energy cost of the first zone plus a thermal discomfort cost of the first zone; and heuristically optimizing the total cost to identify temperature setpoints for a mechanical heating/cooling system and a start time and an end time of the mechanical heating/cooling system, based on external weather data and occupancy data of the first zone.

Cost optimization for buildings with hybrid ventilation systems

A facility is connected to an electricity utility and is responsive to Demand Response Events. A plurality of devices is each individually connected to the electricity grid via an addressable switch connected to a secure network that is enabled to be individually switched off by a server. An occupant of a room in control of the plurality of devices provides via a Human Machine Interface on a tablet a preferred order of switching off the plurality of devices in case of a Demand Response Event. A configuration file based at least partially on the preferred order and on a severity of the Demand Response Events determines which devices which of the plurality devices will be switched off. The server accesses the configuration file and switches off the devices included in the configuration file.

Tablet based distributed intelligent load management

Adaptive demand response based on distributed load control may be provided. A demand response request indicative of a demand response event for an area may be received. A maximum power consumption for the area during the demand response event may be determined based on the demand response request. One or more set points for the area during the demand response event may be determined prior to the demand response event. The one or more set points are determined based on the maximum power consumption. Prior to or during the demand response event, a real-time power consumption for the area may be calculated. When the real-time power consumption exceeds the maximum power consumption, one or more loads in the area may be controlled to maintain the real-time power consumption at or below the maximum power consumption.

Adaptive demand response based on distributed load control

In order to reduce computation time and cost involved with determining one or more optimal parameters for a pre-cooling strategy, for a modeled system, a two-step genetic algorithms is used to optimize energy consumption of the modeled system with respect to cost of the energy consumption. A first step of the two-step genetic algorithms determines a population of potential solutions that are used to initialize a second step of the two-step genetic algorithm. The second step of the two-step genetic algorithm determines the one or more optimal parameters for the pre-cooling strategy from the population output by the first genetic algorithm.

Method and system for energy control management

A system for collaborative energy management and control in a building, including an energy management controller, one or more occupant HMIs that supports two-way communication between building occupants and a facility manager, and between building occupants and the energy management controller, and a facility manager HMI that supports two-way communication between the facility manager and the building occupants, and between the facility manager and the energy management controller, in which the occupant HMI allows building occupants to provide temperature preferences to the facility manager and the energy management controller, and the facility manager HMI allows the facility manager to configure an energy policy for the building as a set of rules and to view occupants' aggregated temperature preferences, and the energy management controller determines an optimum temperature range that resolves conflicting occupant temperature preferences and occupant temperature preferences that conflict with the facility manager's energy policy for the building.

Advanced human-machine interface for collaborative building control

Embodiments provide an application layer security proxy that protects substation automation systems. The application layer security proxy inspects a received, inbound data packet at the application layer, and either drops the data packet, forwards the data packet, or processes the data packet rather than dropping it in order to maintain the communications network connection, the later two according to a predefined role-based access control policy. The application layer security proxy calculates a round trip time for each reply to a received, inbound data packet and observes the bandwidth usage from the amount of bytes transmitted. Round trip time and bandwidth usage are used to detect abnormal communication traffic.

Application layer security proxy for automation and control system networks

Systems and methods are described that provide a Heat Flow Model (HFM) graph modeling methodology. Embodiments automatically translate formal HVAC system descriptions from a Building Information Model (BIM) into HFM graphs, and compile the graphs into executable FDD systems. During an engineering phase, a user interface is used to enter parameters, conditions, and switches not found in the BIM. During a runtime phase, real-time data from an HVAC control system is input to the generated FDD system (HFM graph) for fault detection and diagnosis.

Heat flow model for building fault detection and diagnosis

Methods and architectures are described that provide function block mobility between PLCs. Each PLC configured with Mobile Function Blocks (MFBs) may forward and execute MFBs from each other. To enable MFB mobility over a communication bus, each PLC having one or more configured MFBs requires a Communication Function Block (CFB) and an MFB directory data block. Each PLC maintains an MFB directory data block to store its MFB directory which is used when other PLCs send requests to check the availability of MFBs they need to run new tasks.

Mobile function block for a PLC based distributed control system

An on-line fault detection and avoidance method is provided for industrial control systems that include multiple interacting process controllers. The method addresses the problem that not all faults can be determined and removed at the time of system design and testing. When a fault translates into a time-out condition in one or more controllers, symptoms are identified, persistence is measured, other involved controllers are identified, the fault condition is identified and control laws are reconfigured to avoid the fault condition in the future.

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West Windsor, NJ, United States of America

Yan Lu