본문 바로가기 주메뉴 바로가기

우수연구성과

우수연구성과

Chung-Ang University Scientists Develop New framework for Home Energy Management Systems

관리자 2022-05-30 조회수 160

Chung-Ang University Scientists Develop New Framework for Home Energy Management Systems

 

This distributed, privacy-aware machine learning approach will help smart homes optimize the scheduling of appliances and reduce electricity bills

 

Home energy management systems (HEMSs) are used in smart homes to calculate optimized schedules for electric home appliances, make energy use more efficient, and reduce electricity bills. In a recent study, Korean scientists developed a framework for training HEMSs using a data-driven machine learning approach called federated reinforced learning. Their strategy allows HEMSs to be trained in a distributed manner without computationally intensive steps nor privacy concerns about the users’ energy consumption data.

 

Training home energy management systems in a federated (distributed) manner can help keep users’ data private while lowering the computational complexity of the machine learning algorithms involved.

Image courtesy: Shutterstock

 

 

As we advance steadily into the era of smart grids and smart homes, new opportunities to use energy more efficiently appear. For example, many cities are implementing demand response programs, in which the cost of electricity is lowered outside of peak hours to incentivize people to use energy when there is less load on the grid. Another example is the coordination of distributed generation resources such as solar panels and their battery systems, which has to be optimized according to weather conditions and the supply and demand of energy in real time.

 

Fortunately, most of the decisions and actions needed to optimize our energy use can be delegated to home energy management systems (HEMSs). These devices are designed to efficiently manage the energy consumption of home appliances by doing things such as scheduling the time when the washing machine should start and strategically turning the air conditioner (AC) on and off. In general, the main objective of a HEMS is to minimize electricity bills while also taking the user’s preferences and comfort into account.

 

One way to program these devices is to use hand-crafted models, which rely on abstract equations that represent the appliances and distributed energy resources. Such models and optimization methods, however, are not very versatile and tend to give suboptimal solutions. Another approach is to use centralized machine learning, where data from thousands of users is collected, sent to a central server, and used to train a model from the ground up. This strategy poses two problems. First, sending and processing large volumes of user data is a challenge in and of itself; it’s a costly and computationally intensive endeavor. Second, the central server could become a juicy target for hackers wanting to steal private information about people’s energy consumption patterns.

 

Against this backdrop, Associate Professor Dae-Hyun Choi and PhD student Sangyoon Lee from Chung-Ang University, South Korea, have come up with a novel data-driven strategy to tackle both of these problems simultaneously. As explained in a recent paper, the researchers developed a framework for HEMS based on federated deep reinforcement learning (F-DRL), combining the advantages of various machine learning techniques. The study was made available online on November 3, 2020, and published in Volume 18, Issue 1 of IEEE Transactions on Industrial Informatics in January 2022.

 

The key word to note in F-DRL is ‘federated,’ which indicates a decentralized form of machine learning. In the proposed framework, each home has a HEMS connected to various appliances and devices, namely a washing machine, a solar panel, an AC, and an energy storage system. Each HEMS collects data about its users’ energy consumption and tries to optimize a schedule for the appliances by creating a local model. These local models are all uploaded to a global server, which averages them to produce a global model. Afterwards, each HEMS replaces its local model with the global model and proceeds to train it once again using local data. This process is repeated several times, progressively improving the accuracy of both global and local models. “In a conventional centralized DRL model, the global server must have access to the data of all local devices to generate the model of the global system. This results in data privacy concerns for local devices,” explains Associate Professor Choi, “However, in our federated DRL method, the system does not require the sharing of user data because only the parameters of local and global model are exchanged. In turn, this helps prevent local data leakages and protect the users’ privacy.

 

The researchers tested their approach through simulations, showcasing its optimal performance when scheduling the operation of various appliances at different homes. “To the best of our knowledge, this is the first HEMS framework based on federated DRL that can manage the energy consumption of multiple smart homes and ensure the comfort of the consumers while taking their preferences into account in a distributed manner,” remarks Associate Professor Choi, satisfied with the results. In addition to its low computational complexity and relatively fast training process, the proposed framework can easily support the addition of more appliances at each house.

 

Associate Professor Choi envisions a more comprehensive version of this framework that also takes into account electric cars and energy trading between households. Let us hope he meets this goal so that, in the future, we can let HEMSs optimize our energy consumption without ever having to worry about our privacy.

 

Reference

 

Authors

 

Title of original paper

 

Journal

 

Sangyoon Lee and Dae-Hyun Choi

 

Federated Reinforcement Learning for Energy Management of Multiple Smart Homes With Distributed Energy Resources

IEEE Transactions on Industrial Informatics

 

 

DOI

 

Affiliation

10.1109/TII.2020.3035451

 

School of Electrical and Electronics Engineering, Chung-ang University

 

 

 

About Chung-Ang University

Chung-Ang University is a private comprehensive research university located in Seoul, South Korea. It was started as a kindergarten in 1918 and attained university status in 1953. It is fully accredited by the Ministry of Education of Korea. Chung-Ang University conducts research activities under the slogan of “Justice and Truth.” Its new vision for completing 100 years is “The Global Creative Leader.” Chung-Ang University offers undergraduate, postgraduate, and doctoral programs, which encompass a law school, management program, and medical school; it has 16 undergraduate and graduate schools each. Chung-Ang University’s culture and arts programs are considered the best in Korea.

 

Website: https://neweng.cau.ac.kr/index.do

 

About Associate Professor Dae-Hyun Choi

Dae-Hyun Choi is an Associate Professor at the School of Electrical and Electronics Engineering, Chung-Ang University, Seoul, South Korea. His group researches smart grid application using deep reinforcement learning, cyber-physical security aspects of smart grids, and the theory and applications of cyber-physical energy systems. Before joining Chung-Ang University, he was a Researcher at Korea Telecom, South Korea, where he worked on designing and implementing home network systems. He also worked at LG Electronics, South Korea, where he developed home energy management systems. In 2014, Dae-Hyun Choi received a PhD in electrical and computer engineering from Texas A&M University, College Station, TX, USA.