International Workshop on Applied Machine Learning for Intelligent Energy Systems (AMLIES)
Co-located with ACM e-Energy Conference 2021, Torino, Italy
Overview Call for Papers Key Dates Keynote Speakers Schedule Organizing Committee Technical Program Committee Contact
The capacities of societal-scale infrastructures such as power grids, smart buildings, transportation and other energy systems are rapidly increasing, leading to Cyber-Physical Systems that can deliver human-centric values and energy services while enhancing efficiency and resilience. Technological advancements in sensing, learning, control and optimization hold enormous capacity to deliver intelligent energy systems of the future that are empowered to address pressing societal issues such as energy crisis, climate change and environmental pollution. There has been an increasing interest to use Machine Learning, Data Analytics and Internet of Things (IoT) in diverse energy systems including smart buildings, power systems, transportation systems etc., to drive applications related to modelling, control, fault and anomaly detection and optimization of energy, space and cost amongst others. This has led to improved energy efficiency, occupant comfort and productivity in smart buildings and enhanced system robustness and resilience in power systems.
However, the application of machine learning to these energy systems has also opened up new research challenges. To perform optimally, these algorithms require substantial amount of labelled training data. Collection and labelling of the data in complex systems such as power grids and buildings with complicated energy usage behaviors require considerable amount of expert knowledge and is often prone tosecurity and privacy issues. Efficiently using the available energy data for inference, decision and control can prove beneficial in this scenario. Another challenge is the lack of direct measurements for important drivers of energy consumption, such as occupancy. Data from opportunistic sources (such as images or communication network data) can be adopted to overcome this challenge. This workshop seeks to bring together researchers to discuss such underlying challenges related to the application of Machine Learning to Energy Systems, including smart buildings, smart grid and transportation systems and to present proposed and ongoing work to address them.
The workshop invites original papers that were not previously published and are not currently under review for publication elsewhere.
Topics of interest for this workshop include (but are not limited to) the following:
- Unsupervised Learning and few-shot learning for intelligent energy systems
- Transfer Learning, data-efficient learning and domain adaptation techniques for energy systems
- Generalized learning frameworks for energy systems enabled by Meta Learning and Multitask Learning
- Application of Machine Learning to fault and anomaly detection, performance benchmarking, measurement and verification (M&V), and design and retrofit decision support for energy systems
- Data security and privacy mechanisms in energy data analytics
- Reinforcement Learning in dynamic and uncertain environments (e.g., renewable integration)
- Modeling and decision support for building to grid interaction enabling grid interactive efficient buildings (GEB)
- Vulnerability analysis and robust decision-making against cyberattacks
- Data-efficient occupant behavior, indoor air quality and thermal comfort modeling, monitoring and control for buildings
- Certification and standardization aspects with respect to machine learning applied to energy systems
- Representation Learning for energy societal networks/ social games
Two types of contributions are solicited:
- Full papers, up to 8 pages in 9-point ACM double-column format (excluding references) and unlimited number of pages for appendices and references, single-blind.
- Short papers, up to 4 pages in 9-point ACM double-column format (excluding references) and unlimited number of pages for appendices and references, single-blind.
The submission must be in PDF format and be formatted according to the official ACM Proceedings format. Papers that do not meet the size and formatting requirements may not be reviewed. Word and LaTeX templates are available on the ACM Publications Website. Submission implies the willingness of at least one author to register and present the paper at the workshop.
Submission portal: https://amlies21.hotcrp.com
- April 16, 2021: Paper submission deadline
- May 10, 2021: Author Notification
- May 18, 2021: Camera ready submission deadline
- June 28, 2021: Workshop
Draguna Vrabie, Pacific Northwest National Laboratory
Keynote Title: TBA | Abstract: TBA
Michal Kvasnica, Slovak University of Technology in Bratislava
Keynote Title: TBA | Abstract: TBA
Jie Tan, Google Brain
Keynote Title: Adaptive Power System Emergency Control using Deep Reinforcement Learning | abstract
Deep reinforcement learning (DRL) has achieved super-human performance in a wide variety of games and robotic control problems. Can we apply the same technique to control power grids? In this talk, I will share our experience about the challenges for applying DRL to power grid systems and our latest progress to overcome these challenges. I will give a brief introduction of DRL, and present two case studies on power system emergency control. In the first study, we applied Deep Q Network (DQN) to solve the under-voltage load shedding problem for a small-scale IEEE-39 bus system. The learning is automatic and the learned policies significantly outperform the UVLS relay and the model-predictive control baselines. In the second study, we developed a scalable learning framework for a much larger synthetic Texas 2000 bus system.
Bio: Jie Tan is a staff research scientist and the Tech Lead Manager of the Robot Locomotion and Safety teams at Google Brain. His research is to develop safe machine learning techniques and apply them to real-world systems, such as robots and power grids. His research interests include AI safety, machine learning, robotics, power grids and simulation. Before joining Google, Jie got his PhD at Georgia Institute of Technology.
Tansu Alpcan, University of Melbourne
Keynote Title: Machine Learning for Voltage Estimation and Control in Distribution Grids | abstract
Distributed Energy Resources (DER) such as rooftop solar PV drive the transformation towards greener and smarter power systems in Australia. DER are deeply embedded in low-voltage distribution networks, which were not designed for this purpose. Hence, there is an increasing need for active voltage regulation at the distribution level. Large-scale smart meter deployments enable data-driven machine learning approaches to address this problem. This talk first gives an overview of modern distribution grids and data-driven machine learning methods. Next, recent research on applying deep learning methods to estimate local voltages even when limited information is available on the distribution network parameters is presented. This is followed by research results on active control of voltages using model-free reinforcement learning methods. In both cases, various levels of PV deployment, e.g., in residential settings are considered. The talk concludes with brief remarks on future research directions.
Bio: Tansu Alpcan received the Ph.D. degree in Electrical and Computer Engineering from University of Illinois at Urbana-Champaign (UIUC) in 2006. His research interests include applications of control, optimisation, and game theories, and machine learning to security and resource allocation problems in communications, smart grid, and Internet-of-Things. He chaired or was an Associate Editor, TPC chair, or TPC member of several prestigious IEEE workshops, conferences, and journals. Tansu Alpcan is the (co-)author of more than 150 journal and conference articles as well as the book “Network Security: A Decision and Game Theoretic Approach” published by Cambridge University Press (CUP) in 2011. He co-edited the book “Mechanisms and Games for Dynamic Spectrum Allocation” published by CUP in 2014. He has worked as a senior research scientist in Deutsche Telekom Laboratories, Berlin, Germany (2006-2009), and as Assistant Professor (Juniorprofessur) in Technical University Berlin (2009-2011). He is currently with the Dept. of Electrical and Electronic Engineering at The University of Melbourne as a Professor and Reader.
The coloured entries correspond to 29th June at the dateline mentioned. All other entries correspond to 28th June at the mentioned dateline.
|PDT||EDT||London Time||HKT (29th)||AEST (29th)||Programme|
|9.05-9.50||12.05-12.50||17.05-17.50||00.05-00.50||02.05-02.50||Keynote 1: Draguna Vrabie, Pacific Northwest National Laboratory|
|9.50-10.35||12.50-13.35||17.50-18.35||00.50-01.35||02.50-03.35||Keynote 2: Michal Kvasnica, Slovak University of Technology in Bratislava|
|10.50-11.10||13.50-14.10||18.50-19.10||01.50-02.10||03.50-04.10||Paper 1: Spatio-Temporal Missing Data Imputation for Smart Power Grids|
|11.10-11.30||14.10-14.30||19.10-19.30||02.10-02.30||04.10-04.30||Paper 2: Additive Gaussian process prediction for electrical loads compared with deep learning models|
|11.30-11.40||14.30-14.40||19.30-19.40||02.30-02.40||04.30-04.40||Paper 3: Adapting Surprise Minimizing Reinforcement Learning Techniques for Transactive Control|
|11.40-12.00||14.40-15.00||19.40-20.00||02.40-03.00||04.40-05.00||Paper 4: One model fits all: Individualized household energy demand forecasting with a single deep learning model|
|13.30-14.15||16.30-17.15||21.30-22.15||04.30-05.15||06.30-07.15||Keynote 3: Jie Tan, Google Brain|
|14.15-14.35||17.15-17.35||22.15-22.35||05.15-05.35||07.15-07.35||Paper 5: Hybrid Approach for Digital Twins in the Built Environment|
|14.35-14.45||17.35-17.45||22.35-22.45||05.35-05.45||07.35-07.45||Paper 6: Towards Learning-Based Architectures for Sensor Impact Evaluation in Building Controls|
|15.00-15.45||18.00-18.45||23.00-23.45||06.00-06.45||08.00-08.45||Keynote 4: Tansu Alpcan, University of Melbourne|
|15.45-16.05||18.45-19.05||23.45-00.05 (29th)||06.45-07.05||08.45-09.05||Paper 7: End-to-End Framework for Imputation and State Discovery in Longitudinal Energy Data|
|16.05-16.15||19.05-19.15||00.05-00.15 (29th)||07.05-07.15||09.05-09.15||Paper 8: Using Meta Reinforcement Learning to Bridge the Gap between Simulation and Experiment in Energy Demand Response|
|16.15-16.20||19.15-19.20||00.15-00.20 (29th)||07.15-07.20||09.15-09.20||Closing Remarks|
University of California, Berkeley
Pacific Northwest National Laboratory
IBM Research, Australia
University of California, Berkeley