Siemens Aktiengesellschaft

Siemens Corporation

Siemens Industry Software Nv

Ling Shen

Yan Lu

Jianmin Zhu

Arquimedes Martinez Canedo

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 functional visualization of high-level system variables is based on information from a simulation environment. A functional model is imported from the simulation environment, including function nodes and connections. Each function node includes a function name, an associated component from the simulated system, and an associated physical variable. Each connection includes source and destination functions and a connection type. Values for the physical variables are obtained via a subscription with the simulation environment. The functional visualization is created and displayed based on the functional model and the values.

Functional visualization in system-level multi-domain simulators

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.

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Cherry Hill, NJ, United States of America

Ling Shen