Kang et al., 2019 - Google Patents
Real-time control for power cost efficient deep learning processing with renewable generationKang et al., 2019
View PDF- Document ID
- 14115917578503506638
- Author
- Kang D
- Youn C
- Publication year
- Publication venue
- IEEE Access
External Links
Snippet
The explosive increase in deep learning (DL) deployment has led GPU power usage to become a major factor in operational cost of modern HPC clusters. The complex mixture of DL processing, fluctuated renewable generation, and dynamic electricity price impedes the …
- 230000005611 electricity 0 abstract description 33
Classifications
-
- 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
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
- G06Q10/063—Operations research or analysis
-
- 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
- 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
- G05B15/00—Systems controlled by a computer
- G05B15/02—Systems controlled by a computer electric
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
-
- 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
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US10873209B2 (en) | System and method for dynamic energy storage system control | |
Iacovella et al. | Cluster control of heterogeneous thermostatically controlled loads using tracer devices | |
Jawad et al. | Genetic algorithm‐based non‐linear auto‐regressive with exogenous inputs neural network short‐term and medium‐term uncertainty modelling and prediction for electrical load and wind speed | |
EP3576029B1 (en) | Method and device for determining energy system operating scenarios | |
US20230228446A1 (en) | Scalable control of heat pumps with limited smart-home devices | |
Ma et al. | Hybrid particle swarm optimization and differential evolution algorithm for bi-level programming problem and its application to pricing and lot-sizing decisions | |
Chen et al. | Learning a distributed control scheme for demand flexibility in thermostatically controlled loads | |
US11669060B2 (en) | Hybrid machine learning and simulation based system for forecasting in electricity systems | |
Wang et al. | Approximating multi-purpose AC optimal power flow with reinforcement trained artificial neural network | |
Wei et al. | Model-based and data-driven approaches for building automation and control | |
Dogan et al. | A review on machine learning models in forecasting of virtual power plant uncertainties | |
Kang et al. | Real-time control for power cost efficient deep learning processing with renewable generation | |
Bao et al. | Hybrid short-term load forecasting using principal component analysis and mea-elman network | |
Zhang et al. | Building load control using distributionally robust chance-constrained programs with right-hand side uncertainty and the risk-adjustable variants | |
Safari et al. | NeuroQuMan: quantum neural network-based consumer reaction time demand response predictive management | |
Agouzoul et al. | Synthesis of model predictive control based on neural network for energy consumption enhancement in building | |
Kang et al. | Deep learning-based sustainable data center energy cost minimization with temporal MACRO/MICRO scale management | |
Xu et al. | Adaptive feature selection and GCN with optimal graph structure-based ultra-short-term wind farm cluster power forecasting method | |
Li | Application of economical building management system for Singapore commercial building | |
Lin et al. | Risk-averse robust interval economic dispatch for power systems with large-scale wind power integration | |
CN113298329A (en) | Training and strategy generating method, system, computer device and storage medium | |
Kousounadis-Knousen et al. | A New Co-Optimized Hybrid Model Based on Multi-Objective Optimization for Probabilistic Wind Power Forecasting in a Spatio–Temporal Framework | |
Lee et al. | Virtual storage-based DSM with error-driven prediction modulation for microgrids | |
Senthil Kumar et al. | Enhancing grid stability and efficiency in buildings through forecasting and intelligent energy management of distributed energy resources | |
Tungom et al. | SWOAM: Swarm optimized agents for energy management in grid-interactive connected buildings |