4.6 Article

Optimal home energy management strategy: A reinforcement learning method with actor-critic using Kronecker-factored trust region

Related references

Note: Only part of the references are listed.
Article Green & Sustainable Science & Technology

Short-term Solar Power Prediction Learning Directly from Satellite Images With Regions of Interest

Lilin Cheng et al.

Summary: This study proposes an end-to-end short-term forecasting model that uses satellite images to predict solar power generation by learning cloud motion characteristics. With its optimized deep learning architecture, the model outperforms other methods in prediction results and learning capability, making it suitable for PV plants in different areas.

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY (2022)

Article Engineering, Multidisciplinary

Day-Ahead Optimal Economic Dispatching of Integrated Port Energy Systems Considering Hydrogen

Xiaobo Wang et al.

Summary: Developing integrated port energy systems (IPES) is crucial in addressing energy consumption and carbon emissions in ports. IPES utilizes surplus renewable energy to generate hydrogen, which can then be converted to natural gas and used to power equipment. A closed-loop system is formed using fuel cells, gas turbines, hydrogen boilers, and gas boilers to supply electrical and heat loads in the port area. The establishment of an optimal economic dispatching model takes energy conversion loss and carbon dioxide emissions into account. Results demonstrate that the model effectively reduces energy cost and carbon dioxide emissions in IPES.

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS (2022)

Article Thermodynamics

Residential load forecasting based on LSTM fusing self-attention mechanism with pooling

Haixiang Zang et al.

Summary: This paper proposes a novel day-ahead residential load forecasting method based on feature engineering, pooling, and a hybrid deep learning model. Case studies on a practical dataset demonstrate the effectiveness and superiority of the proposed method.

ENERGY (2021)

Article Energy & Fuels

AlphaBuilding ResCommunity: A multi-agent virtual testbed for community-level load coordination

Zhe Wang et al.

Summary: Training and validating algorithms in a simulation testbed can accelerate research and applications of optimal control of residential loads. The AlphaBuilding ResCommunity environment offers more realistic model settings and ease of use, demonstrating applications with various control strategies.

ADVANCES IN APPLIED ENERGY (2021)

Article Computer Science, Information Systems

Deep Reinforcement Learning for Smart Home Energy Management

Liang Yu et al.

IEEE INTERNET OF THINGS JOURNAL (2020)

Article Green & Sustainable Science & Technology

Differential Dynamic Programming Based Home Energy Management Scheduler

Babak Jeddi et al.

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY (2020)

Article Engineering, Electrical & Electronic

Real-Time Residential Demand Response

Hepeng Li et al.

IEEE TRANSACTIONS ON SMART GRID (2020)

Article Green & Sustainable Science & Technology

Short-term global horizontal irradiance forecasting based on a hybrid CNN-LSTM model with spatiotemporal correlations

Haixiang Zang et al.

RENEWABLE ENERGY (2020)

Article Computer Science, Information Systems

Real-Time Scheduling of Operational Time for Smart Home Appliances Based on Reinforcement Learning

Murad Khan et al.

IEEE ACCESS (2020)

Article Engineering, Electrical & Electronic

Online Energy Management for a Sustainable Smart Home With an HVAC Load and Random Occupancy

Liang Yu et al.

IEEE TRANSACTIONS ON SMART GRID (2019)

Article Engineering, Electrical & Electronic

On-Line Building Energy Optimization Using Deep Reinforcement Learning

Elena Mocanu et al.

IEEE TRANSACTIONS ON SMART GRID (2019)

Review Energy & Fuels

Reinforcement learning for demand response: A review of algorithms and modeling techniques

Jose R. Vazquez-Canteli et al.

APPLIED ENERGY (2019)

Article Construction & Building Technology

Energy optimization associated with thermal comfort and indoor air control via a deep reinforcement learning algorithm

William Valladares et al.

BUILDING AND ENVIRONMENT (2019)

Article Engineering, Electrical & Electronic

Model-Free Real-Time EV Charging Scheduling Based on Deep Reinforcement Learning

Zhiqiang Wan et al.

IEEE TRANSACTIONS ON SMART GRID (2019)

Article Green & Sustainable Science & Technology

Real-Time Scheduling of Demand Response Options Considering the Volatility of Wind Power Generation

Saber Talari et al.

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY (2019)

Article Computer Science, Information Systems

A Real-Time Electricity Scheduling for Residential Home Energy Management

Shenglin Li et al.

IEEE INTERNET OF THINGS JOURNAL (2019)

Article Engineering, Electrical & Electronic

Demand Response for Home Energy Management Using Reinforcement Learning and Artificial Neural Network

Renzhi Lu et al.

IEEE TRANSACTIONS ON SMART GRID (2019)

Article Engineering, Electrical & Electronic

Chance Constrained Optimization in a Home Energy Management System

Yantai Huang et al.

IEEE TRANSACTIONS ON SMART GRID (2018)

Article Engineering, Electrical & Electronic

Convolutional Neural Networks for Automatic State-Time Feature Extraction in Reinforcement Learning Applied to Residential Load Control

Bert J. Claessens et al.

IEEE TRANSACTIONS ON SMART GRID (2018)

Article Chemistry, Multidisciplinary

Energy Management Strategy for a Hybrid Electric Vehicle Based on Deep Reinforcement Learning

Yue Hu et al.

APPLIED SCIENCES-BASEL (2018)

Article Engineering, Electrical & Electronic

Efficient energy management for a grid-tied residential microgrid

Amjad Anvari-Moghaddam et al.

IET GENERATION TRANSMISSION & DISTRIBUTION (2017)

Article Engineering, Electrical & Electronic

Residential Demand Response of Thermostatically Controlled Loads Using Batch Reinforcement Learning

Frederik Ruelens et al.

IEEE TRANSACTIONS ON SMART GRID (2017)

Article Engineering, Electrical & Electronic

Efficient Computation for Sparse Load Shifting in Demand Side Management

Chaojie Li et al.

IEEE TRANSACTIONS ON SMART GRID (2017)

Article Energy & Fuels

A multi-agent based energy management solution for integrated buildings and microgrid system

Amjad Anvari-Moghaddam et al.

APPLIED ENERGY (2017)

Article Engineering, Electrical & Electronic

A real-time evaluation of energy management systems for smart hybrid home Microgrids

Mousa Marzband et al.

ELECTRIC POWER SYSTEMS RESEARCH (2017)

Review Green & Sustainable Science & Technology

Smart home energy management systems: Concept, configurations, and scheduling strategies

Bin Zhou et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2016)

Article Engineering, Electrical & Electronic

An Optimal and Learning-Based Demand Response and Home Energy Management System

Dong Zhang et al.

IEEE TRANSACTIONS ON SMART GRID (2016)

Article Construction & Building Technology

Cost-effective and comfort-aware residential energy management under different pricing schemes and weather conditions

Amjad Anvari-Moghaddam et al.

ENERGY AND BUILDINGS (2015)

Article Automation & Control Systems

Optimal Household Appliances Scheduling Under Day-Ahead Pricing and Load-Shaping Demand Response Strategies

Nikolaos G. Paterakis et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2015)

Article Engineering, Electrical & Electronic

Automated Demand Response From Home Energy Management System Under Dynamic Pricing and Power and Comfort Constraints

Sereen Althaher et al.

IEEE TRANSACTIONS ON SMART GRID (2015)

Article Engineering, Electrical & Electronic

Optimal Smart Home Energy Management Considering Energy Saving and a Comfortable Lifestyle

Amjad Anvari-Moghaddam et al.

IEEE TRANSACTIONS ON SMART GRID (2015)

Article Engineering, Electrical & Electronic

Optimal Demand Response Using Device-Based Reinforcement Learning

Zheng Wen et al.

IEEE TRANSACTIONS ON SMART GRID (2015)

Article Construction & Building Technology

Demand response for residential buildings based on dynamic price of electricity

Ji Hoon Yoon et al.

ENERGY AND BUILDINGS (2014)