International Workshop on Applied Machine Learning for Intelligent Energy Systems (AMLIES)

Co-located with ACM e-Energy Conference 2019, Arizona, USA

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 to security 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.


Call For Papers

Downloadable CFP

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:

  1. Unsupervised Learning and few-shot learning for intelligent energy systems
  2. Transfer Learning, data-efficient learning and domain adaptation techniques for energy systems
  3. Generalized learning frameworks for energy systems enabled by Meta Learning and Multitask Learning
  4. 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
  5. Data security and privacy mechanisms in energy data analytics
  6. Reinforcement Learning in dynamic and uncertain environments (e.g., renewable integration)
  7. Modeling and decision support for building to grid interaction enabling grid interactive efficient buildings (GEB)
  8. Vulnerability analysis and robust decision-making against cyberattacks
  9. Data-efficient occupant behavior, indoor air quality and thermal comfort modeling, monitoring and control for buildings
  10. Certification and standardization aspects with respect to machine learning applied to energy systems
  11. 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:

Key Dates

  • March 20, 2019: Paper submission deadline
  • April 15, 2019: Author Notification
  • May 20, 2019: Camera ready submission deadline
  • June 25, 2019: Workshop

Keynote Speakers

  • Erika Gupta, Sensors and Controls Technology Manager, EERE's Building Technologies Office Department of Energy, USA

    Keynote Title: Grid-Interactive Efficient Buildings (GEB): Vision , State of the Art and Challenges from a Machine Learning Perspective

    Bio: Erika Gupta is the Sensors and Controls technology manager in EERE's Building Technologies Office with the Emerging Technologies program. Her work at BTO leverages her controls background, focusing on building energy management controls and projects supporting controls for grid-integrated efficient buildings.

  • Costas J. Spanos, Andrew S. Grove Distinguished Professor of Electrical Engineering and Computer Sciences, UC Berkeley

    Keynote Title: TBA

    Bio: Costas J. Spanos currently serves as the Director of the CITRIS and the Banatao Institute. He is also the Founding Director and CEO of the Berkeley Education Alliance for Research in Singapore (BEARS), and the Lead Investigator of a large research program on smart buildings based in California and Singapore. His research focuses on Sensing, Data Analytics, Modeling and Machine Learning, with broad applications in semiconductor technologies, and cyber-physical systems. He has published more than 300 papers, 15 patents, and a textbook. He has supervid more than 50 Ph.D. recipients. In 2000 he was elected Fellow of the Institute of Electrical and Electronic Engineers for contributions and leadership in semiconductor manufacturing. He was won several Best Paper Awards and was recently recognized with the Distinguished Berkeley Faculty Mentor Award.

  • Arun Vishwanath, Lead Research Scientist, IBM Research Australia

    Keynote Title: BEACH - Building Energy Analytics for Cooling and Heating | abstract

    The buildings sector is one of the largest energy-consuming entities today, accounting for over 35% of global energy consumption. It is well-known that heating, ventilation and air-conditioning (HVAC) systems dominate energy usage in commercial buildings. Global pressure to improve environmental sustainability, combined with increasing electricity prices across several nations worldwide, are pushing corporations to reduce the energy consumption and costs required to operate their buildings.

    Inspired by the proliferation of IoT sensors in buildings, in this talk I present BEACH, a data-driven framework to reduce energy consumption for cooling associated with building HVAC. I will describe the system architecture of BEACH, which has been implemented as a software overlay solution on the IBM cloud platform. It uses secure RESTful APIs alongside the Project Haystack open source IoT initiative to autonomously actuate cooling start times and zone set-point temperature settings in any on-site building management system. We have deployed BEACH to dynamically control the HVAC of a large office building, housing hundreds of tenants, located in northern Australia. The experimental results demonstrate that BEACH reduces cooling energy consumption and costs by 15-20%, without any thermal discomfort for the occupants.

    BEACH is designed as a low-complexity, lightweight and practical solution for facility managers to use for improving the energy and cost footprints of their buildings.

    Bio: Arun Vishwanath is a lead research scientist at IBM Research Australia working in the area of IoT, specifically on data driven techniques for energy optimisation in commercial buildings. He received the Ph.D. degree in Electrical Engineering from The University of New South Wales, Sydney, Australia in 2011 and was a visiting Ph.D. scholar with the Department of Computer Science at North Carolina State University, USA in 2008. He has served on the program committee of several ACM/IEEE conferences, published over 50 papers in top-tier venues and co-authored 20 US patents. His honours include the Best Paper Award at the ACM e-Energy 2018 conference, being named an ACM Distinguished Speaker and IEEE Senior Member.

  • Jason Hou, Senior Scientist, Pacific Northwest National Laboratory

    Keynote Title: Several Applications of Machine Learning in Power Systems

    Bio: Dr. Z. Jason Hou is a senior scientist and statistician at Pacific Northwest National Laboratory, and has been well recognized for leading pioneering work in developing and applying advanced machine learning, uncertainty quantification, and extreme events analysis, in the areas of earth, energy, and environmental systems. Dr. Hou's research broadly cuts across areas in stochastic operation and planning of energy systems, extreme events in earth systems, carbon sequestration, oil/gas exploration, and environmental remediation.

Workshop Schedule: June 25, 2019

  • Time


    9.00-9.05 AM

    Opening Remarks

    9.05-9.30 AM

    Plenary Lecture:

    Erika Gupta

    Sensors and Controls Technology Manager

    EERE's Building Technologies Office

    Department of Energy, USA

    9.35-10.15 AM

    Keynote Address 1: Title: TBA

    Costas J. Spanos

    University of California, Berkeley

    10.20-10.40 AM

    “A sequential DNN based Baseline Energy Prediction
    Framework with Long term Error Mitigation” - Indrasis
    Chakraborty, Vikas Chandan, Draguna Vrabie

    10.40-11.00 AM

    “Online Learning for Commercial Buildings” - Jin Dong,
    Thiagarajan Ramachandran, Piljae Im, Sen Huang, Vikas
    Chandan, Draguna L Vrabie, Teja Kuruganti

    11.00-11.20 AM

    Coffee Break

    11.20-11.50 AM

    Keynote Address 2: Title: TBA

    Arun Vishwanath

    IBM Research, Australia

    11.50-12.10 PM

    “Coordination of Behind-the-Meter Energy Storage and
    Building Loads: Optimization with Deep Learning Model” -
    Yimin Chen, Vikas Chandan, Yunzhi Huang, M.J.E. Alam, Osman
    Ahmed, Lane Smith

    12.10-12.30 PM

    “Indoor Thermal Management Using Data Driven Learning and
    Predictive Control” - Zaid Bin Tariq, M.H. Toufiq Imam,
    Koushik Kar, Sandipan Mishra

    12.30-2.00 PM


    2.00 -2.30 PM

    Keynote Address 3: Title: TBA

    Jason Hou

    Pacific Northwest National Laboratory

    2.30-2.50 PM

    “Cross-Categorical Transfer Learning based Composite Load
    Protection Modeling” - Indrasis Chakraborty, Soumya Kundu,
    Yuan Liu, Pavel Etingov

    2.50-3.10 PM

    “A Clustering Approach for Consumer Baselining and Anomaly
    Detection in Transactive Control” - Nasheen Nur, Siddharth
    Sridhar, Seemita Pal, Aditya Ashok, Vinay C Amatya

    3.10-3.30 PM

    “Day-Ahead Price Forecasting in ERCOT Market Using Neural
    Network Approaches” - Jian Xu, Ross Baldick

    3.30-4.00 PM

    Coffee Break

    4.00-4.20 PM

    “Adaptive Battery Control with Neural Networks” - Fiodar
    Kazhamiaka, Srinivasan Keshav, Catherine Rosenberg

    4.20-5.15 PM

    Panel Discussion: Panelists: TBA

    "Creating ML Awareness among Energy Researchers and Energy
    Awareness among ML Researchers"

    5.15-5.30 PM

    Closing Remarks and Vote of Thanks

Organizing Committee


Hari Prasanna Das

University of California, Berkeley


Vikas Chandan

Pacific Northwest National Laboratory


Arun Vishwanath

IBM Research, Australia



Ming Jin

University of California, Berkeley


Han Zou

University of California, Berkeley


Costas J. Spanos

University of California, Berkeley



Draguna Vrabie

Pacific Northwest National Laboratory


Piljae Im

Oak Ridge National Laboratory


Guanjing Lin

Lawrence Berkeley National Laboratory

Technical Program Committee

  • Anil Aswani, UC Berkeley
  • Arnab Bhattacharya, Pacific Northwest National Laboratory
  • Arun Vishwanath, IBM Research, Australia
  • Ashok Kumar Pradhan, Indian Institute of Technology, Kharagpur
  • Clayton Miller, National University of Singapore
  • Costas J. Spanos, UC Berkeley
  • Draguna Vrabie, Pacific Northwest National Laboratory
  • Eliot Crowe, Lawrence Berkeley National Laboratory
  • Flora Salim, Royal Melbourne Institute of Technology
  • Gautam Biswas, Vanderbilt University
  • George J. Pappas, University of Pennsylvania
  • Gianfranco Doretto, West Virginia University
  • Guanjing Lin, Lawrence Berkeley National Laboratory
  • Han Zou, UC Berkeley
  • Hari Prasanna Das, UC Berkeley
  • Indrasis Chakraborty, Pacific Northwest National Laboratory
  • Ioannis Konstantakopoulos, Amazon, Seattle
  • Jason Hou, Pacific Northwest National Laboratory
  • Jessica Granderson, Lawrence Berkeley National Laboratory
  • Jianfei Yang, Nanyang Technological University
  • Jin Dong, Oak Ridge National Laboratory
  • Karla Kvatennik, Siemens
  • Laurentiu Marinovici, Pacific Northwest National Laboratory
  • Lucas Spangher, UC Berkeley
  • Madhur Behl, University of Virginia
  • Manfred Morari, University of Pennsylvania
  • Ming Jin, UC Berkeley
  • Nikitha Radhakrishnan, Pacific Northwest National Laboratory
  • Piljae Im, Oak Ridge National Laboratory
  • Qiuhua Huang, Pacific Northwest National Laboratory
  • Ramachandra Rao Kolluri, IBM Research, Australia
  • Ruoxi Jia, UC Berkeley
  • Samir Touzani, Lawrence Berkeley National Laboratory
  • Sanjib Kumar Panda, National University of Singapore
  • Utkarsha Agwan, UC Berkeley
  • Vikas Chandan, Pacific Northwest National Laboratory
  • Yimin Chen, Drexel University
  • Yu-Wen Lin, UC Berkeley
  • Contact