[go: up one dir, main page]

Seyedyazdi et al., 2024 - Google Patents

Intelligent charging station recommendations for electric vehicles in the charging market: a fuzzy− deep learning approach

Seyedyazdi et al., 2024

Document ID
8957022838858572485
Author
Seyedyazdi M
Razmi P
Khooban M
Publication year
Publication venue
Applications of Deep Machine Learning in Future Energy Systems

External Links

Snippet

The global imperative to address environmental concerns, particularly in the transport sector, has intensified efforts toward the widespread adoption of electric vehicles (EVs). However, challenges such as range anxiety, insufficient charging infrastructure, and their …
Continue reading at www.sciencedirect.com (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
    • 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"
    • 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
    • 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
    • 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
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/04Inference methods or devices

Similar Documents

Publication Publication Date Title
Zhang et al. Multistep multiagent reinforcement learning for optimal energy schedule strategy of charging stations in smart grid
Liang et al. Mobility-aware charging scheduling for shared on-demand electric vehicle fleet using deep reinforcement learning
US20210110323A1 (en) Optimizing charging, fueling, and parking overheads of fleet vehicles in a maas architecture
Bozanta et al. Courier routing and assignment for food delivery service using reinforcement learning
Chu et al. A multiagent federated reinforcement learning approach for plug-in electric vehicle fleet charging coordination in a residential community
Wang et al. Optimization of ride-sharing with passenger transfer via deep reinforcement learning
Zou et al. Online food ordering delivery strategies based on deep reinforcement learning
Alansari et al. Optimal placement of electric vehicle charging infrastructures utilizing deep learning
Adetunji et al. A two-tailed pricing scheme for optimal EV charging scheduling using multiobjective reinforcement learning
Fan A hybrid adaptive large neighborhood search for time-dependent open electric vehicle routing problem with hybrid energy replenishment strategies
Abbas et al. An improved optimal forecasting algorithm for comprehensive electric vehicle charging allocation
Ni et al. Mobility and energy management in electric vehicle based mobility-on-demand systems: Models and solutions
Wang et al. Energy management of plug-in hybrid electric vehicle based on trip characteristic prediction
Rele et al. Hybrid algorithm for large scale in electric vehicle routing and scheduling optimization
Rajeh et al. A clustering-based multi-agent reinforcement learning framework for finer-grained taxi dispatching
Narayana Gowda et al. Short-term aggregate electric vehicle charging load forecasting in diverse conditions with minimal data using transfer and meta-learning
Erüst et al. Deep reinforcement learning-based navigation strategy for a mobile charging station in a dynamic environment
Chen et al. Two-step genetic algorithm for dynamic route optimization of electric vehicles based on demand analysis
CN117196166A (en) Demand response potential assessment method for cluster aggregation of electric vehicle charging facilities
Seyedyazdi et al. Intelligent charging station recommendations for electric vehicles in the charging market: a fuzzy− deep learning approach
Qian et al. Model improvement and scheduling optimization for multi-vehicle charging planning in IoV
Yang et al. A novel demand dispatching model for autonomous on-demand services
Xiao et al. A reinforcement-learning-based bus scheduling model
CN119009940A (en) Layered energy system optimal scheduling method based on LSTM neural network
Schofield et al. Handling rebalancing problem in shared mobility services via reinforcement learning-based incentive mechanism