[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Zhan et al., 2023 - Google Patents

Comparing model predictive control and reinforcement learning for the optimal operation of building-PV-battery systems

Zhan et al., 2023

View PDF
Document ID
8654497342632119435
Author
Zhan S
Lei Y
Chong A
Publication year
Publication venue
E3S Web of Conferences

External Links

Snippet

The integration of renewable energy, such as solar photovoltaics (PV), is critical to reducing carbon emissions but has exerted pressure on power grid operations. Microgrids with buildings, distributed energy resources, and energy storage systems are introduced to …
Continue reading at www.e3s-conferences.org (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063Operations research or analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/08Learning methods
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/12Computer systems based on biological models using genetic models
    • G06N3/126Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q30/00Commerce, e.g. shopping or e-commerce
    • G06Q30/02Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
    • G06Q30/0202Market predictions or demand forecasting
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q10/00Administration; Management
    • G06Q10/04Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02EREDUCTION OF GREENHOUSE GASES [GHG] EMISSION, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

Similar Documents

Publication Publication Date Title
Weitzel et al. Energy management for stationary electric energy storage systems: A systematic literature review
Ju et al. Multi-objective stochastic scheduling optimization model for connecting a virtual power plant to wind-photovoltaic-electric vehicles considering uncertainties and demand response
CN109508857B (en) Multi-stage planning method for active power distribution network
Motevasel et al. Expert energy management of a micro-grid considering wind energy uncertainty
Xiao et al. A novel energy management method for networked multi-energy microgrids based on improved DQN
Gandhi et al. Review of optimization of power dispatch in renewable energy system
Li et al. Knee Point‐Guided Multiobjective Optimization Algorithm for Microgrid Dynamic Energy Management
CN114723230B (en) Micro-grid double-layer scheduling method and system for new energy power generation and energy storage
Zhou et al. Deep learning-based rolling horizon unit commitment under hybrid uncertainties
KR102707077B1 (en) Demand response management method for discrete industrial manufacturing system based on constrained reinforcement learning
Banu et al. Artificial intelligence with attention based BiLSTM for energy storage system in hybrid renewable energy sources
Kang et al. Multi-objective sizing and real-time scheduling of battery energy storage in energy-sharing community based on reinforcement learning
Chen et al. Optimal control strategy for solid oxide fuel cell‐based hybrid energy system using deep reinforcement learning
Katsigiannis et al. A software tool for capacity optimization of hybrid power systems including renewable energy technologies based on a hybrid genetic algorithm—tabu search optimization methodology
Souabi et al. Data-driven prediction models of photovoltaic energy for smart grid applications
Morin et al. Energy Management of isolated DC microgrids with hybrid batteries-hydrogen storage system using Model Predictive Control and Wavelet Neural Networks based forecasts
Zhan et al. Comparing model predictive control and reinforcement learning for the optimal operation of building-PV-battery systems
Qi et al. Optimal scheduling in IoT-driven smart isolated microgrids based on deep reinforcement learning
Dimitroulis et al. Multimodal energy management system for residential building prosumers utilizing various lifestyles
Guiducci et al. A Reinforcement Learning approach to the management of Renewable Energy Communities
Zhang et al. Two-Step Diffusion Policy Deep Reinforcement Learning Method for Low-Carbon Multi-Energy Microgrid Energy Management
Fang et al. Energy scheduling and decision learning of combined cooling, heating and power microgrid based on deep deterministic policy gradient
Wang et al. Self-organizing maps for scenario reduction in long-term hydropower scheduling
Dolatabadi et al. SFNAS-DDPG: A Biomass-based Energy Hub Dynamic Scheduling Approach via Connecting Supervised Federated Neural Architecture Search and Deep Deterministic Policy Gradient
Vasenin et al. Long-term electrical energy forecasting of the residential sector using the lstm model: The italian use case