Zupančič et al., 2020 - Google Patents
Genetic-programming-based multi-objective optimization of strategies for home energy-management systemsZupančič et al., 2020
View HTML- Document ID
- 13766057872287265424
- Author
- Zupančič J
- Filipič B
- Gams M
- Publication year
- Publication venue
- Energy
External Links
Snippet
Home energy-management systems can optimize performance either by computing the next step dynamically–online, or rely on a precomputed strategy used to introduce the next decision–offline. Further, such systems can optimize based on only one or several …
- 238000005457 optimization 0 title abstract description 114
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0205—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system
- G05B13/024—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system in which a parameter or coefficient is automatically adjusted to optimise the performance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B15/00—Systems controlled by a computer
- G05B15/02—Systems controlled by a computer electric
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Zupančič et al. | Genetic-programming-based multi-objective optimization of strategies for home energy-management systems | |
Schmidt et al. | Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency | |
Nutakki et al. | Review on optimization techniques and role of Artificial Intelligence in home energy management systems | |
Touzani et al. | Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency | |
Claessens et al. | Convolutional neural networks for automatic state-time feature extraction in reinforcement learning applied to residential load control | |
Charbonnier et al. | Scalable multi-agent reinforcement learning for distributed control of residential energy flexibility | |
Shrivastava et al. | A multiobjective framework for wind speed prediction interval forecasts | |
Abedi et al. | Battery energy storage control using a reinforcement learning approach with cyclic time-dependent Markov process | |
Raza et al. | Smart home energy management systems: Research challenges and survey | |
Pinto et al. | Enhancing energy management in grid-interactive buildings: A comparison among cooperative and coordinated architectures | |
Lu et al. | A multi‐objective multi‐agent deep reinforcement learning approach to residential appliance scheduling | |
Santos et al. | Deep learning and transfer learning techniques applied to short-term load forecasting of data-poor buildings in local energy communities | |
Mousavi et al. | A real-time energy management strategy for pumped hydro storage systems in farmhouses | |
Tai et al. | A real-time demand-side management system considering user preference with adaptive deep Q learning in home area network | |
Das et al. | Approximate dynamic programming with policy-based exploration for microgrid dispatch under uncertainties | |
Chu et al. | Optimal home energy management strategy: A reinforcement learning method with actor-critic using Kronecker-factored trust region | |
Wang et al. | Scalable energy management approach of residential hybrid energy system using multi-agent deep reinforcement learning | |
Lee et al. | Novel architecture of energy management systems based on deep reinforcement learning in microgrid | |
Kefer et al. | Simulation-based optimization of residential energy flows using white box modeling by genetic programming | |
Alghassab | Fuzzy-based smart energy management system for residential buildings in Saudi Arabia: A comparative study | |
Zhao et al. | Integrated management of urban resources toward Net-Zero smart cities considering renewable energies uncertainty and modeling in Digital Twin | |
Vijayalakshmi et al. | An ensemble learning model for estimating the virtual energy storage capacity of aggregated air-conditioners | |
Hosseini et al. | Meta-heuristics and deep learning for energy applications: Review and open research challenges (2018–2023) | |
Wang et al. | Carbon peak management strategies for achieving net-zero emissions in smart buildings: Advances and modeling in digital twin | |
Vaikund | Cost mitigation strategy for microgrid using an advanced energy management system with an intelligent controller |