Kofinas et al., 2018 - Google Patents
Energy management in solar microgrid via reinforcement learning using fuzzy rewardKofinas et al., 2018
View PDF- Document ID
- 10129005388486113825
- Author
- Kofinas P
- Vouros G
- Dounis A
- Publication year
- Publication venue
- Advances in building energy research
External Links
Snippet
This paper proposes a single-agent system towards solving energy management issues in solar microgrids. The proposed system consists of a photovoltaic (PV) source, a battery bank, a desalination unit (responsible for providing the demanded water) and a local …
- 230000002787 reinforcement 0 title description 36
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
- 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
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- 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
- 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
- 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
- G06Q30/00—Commerce, e.g. shopping or e-commerce
- G06Q30/02—Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
- G06Q30/0202—Market predictions or demand forecasting
-
- 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
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Kofinas et al. | Energy management in solar microgrid via reinforcement learning using fuzzy reward | |
Ruelens et al. | Demand response of a heterogeneous cluster of electric water heaters using batch reinforcement learning | |
Zupančič et al. | Genetic-programming-based multi-objective optimization of strategies for home energy-management systems | |
Huang et al. | Mixed deep reinforcement learning considering discrete-continuous hybrid action space for smart home energy management | |
Xie et al. | Multi-agent attention-based deep reinforcement learning for demand response in grid-responsive buildings | |
Crisostomi et al. | Prediction of the Italian electricity price for smart grid applications | |
El Bourakadi et al. | Intelligent energy management for micro-grid based on deep learning LSTM prediction model and fuzzy decision-making | |
Lu et al. | A multi‐objective multi‐agent deep reinforcement learning approach to residential appliance scheduling | |
Brodowski et al. | A hybrid system for forecasting 24-h power load profile for Polish electric grid | |
Eapen et al. | Performance analysis of combined similar day and day ahead short term electrical load forecasting using sequential hybrid neural networks | |
Raza et al. | Smart home energy management systems: Research challenges and survey | |
Moustris et al. | Medium, short and very short-term prognosis of load demand for the Greek Island of Tilos using artificial neural networks and human thermal comfort-discomfort biometeorological data | |
Atef et al. | A new fuzzy logic based approach for optimal household appliance scheduling based on electricity price and load consumption prediction | |
Tai et al. | A real-time demand-side management system considering user preference with adaptive deep Q learning in home area network | |
Xiong et al. | Meta-reinforcement learning-based transferable scheduling strategy for energy management | |
Wen et al. | Data-driven energy management system for flexible operation of hydrogen/ammonia-based energy hub: A deep reinforcement learning approach | |
Oh et al. | Impact of demand and price uncertainties on customer-side energy storage system operation with peak load limitation | |
El Bourakadi et al. | Multi-agent system based sequential energy management strategy for Micro-Grid using optimal weighted regularized extreme learning machine and decision tree | |
Yang et al. | Adaptive data decomposition based quantile-long-short-term memory probabilistic forecasting framework for power demand side management of energy system | |
Kofinas et al. | Energy management in solar microgrid via reinforcement learning | |
Leo et al. | Multi agent reinforcement learning based distributed optimization of solar microgrid | |
Vijayalakshmi et al. | An ensemble learning model for estimating the virtual energy storage capacity of aggregated air-conditioners | |
Li et al. | Deep Reinforcement Learning-Based Explainable Pricing Policy for Virtual Storage Rental Service | |
Smith et al. | Control of Multicarrier Energy Systems from Buildings to Networks | |
Zhang et al. | Networked Multiagent-Based Safe Reinforcement Learning for Low-Carbon Demand Management in Distribution Networks |