Method and system for energy control management
AssigneeSiemens Aktiengesellschaft
InventorsYan Lu, Ling Shen, Jianmin Zhu
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Abstract
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.
Claims19
A method for optimizing energy consumption in a system, the method comprising:
- identifying, by a processor, a plurality of candidate solutions for including one or more parameters of the system related to the energy consumption in the system;
- initializing, by the processor, a two-step genetic algorithm with at least one population of the plurality of identified candidate solutions;
- determining, by the processor utilizing the two-step genetic algorithm, an optimum energy consumption based on a cost of the energy for the system;
- and
- determining, by the processor, at least one parameter of the one or more parameters of the system based on the optimum energy consumption.
In a non-transitory computer-readable storage medium that stores instructions executable by one or more processors to optimize parameters for a pre-cooling strategy for a structure, the instructions comprising:
- initializing a first step of a two-step genetic algorithm of a model operable to simulate energy consumption of a system associated with a structure and one or more temperatures in the structure over a period of time with a first population of candidate solutions, each candidate solution of the first population of candidate solutions including a combination of a start time and an end time of a cooling period;
- determining a second population of candidate solutions from the first population of the candidate solutions utilizing the first step of the two-step genetic algorithm;
- initializing a second step of the two-step genetic algorithm utilizing the second population of candidate solutions;
- determining an optimum energy consumption in the structure over the period of time based on a cost of the energy consumption and a variable representing an uncomfortableness of an occupant of the structure utilizing the second step of the genetic algorithm, wherein an optimized start time and end time of the cooling period corresponds to the optimum energy consumption.
A system for optimizing energy consumption in a thermodynamics system associated with a structure, the system comprising:
- a memory, wherein the memory is configured to store computer readable instructions;
- an input device operable to receive data for a plurality of candidate solutions for one or more parameters related to the energy consumption in the thermodynamics system;
- and
- a processor operatively connected to the input device and memory, the processor configured to execute the computer readable instructions, wherein the computer readable instructions are programmed to:
- input a first population of candidate solutions of the plurality of candidate solutions to a first step of a two-step genetic algorithm;
- determine a second population of candidate solutions from the first plurality of candidate solutions utilizing the first step of the two-step genetic algorithm;
- input the second population of candidate solutions to a second step of the two-step genetic algorithm;
- optimize, with the second step of the two-step genetic algorithm, the energy consumption in the structure based on a cost of the energy and a variable representing an uncomfortableness of an occupant of the structure;
- and
- determine the one or more parameters of the thermodynamics system based on the optimized energy consumption.
Dependencies are inferred from the claim text.