Workshops & Tutorials



1st ACM Workshop on Advancements in Building Energy Benchmarking Systems (BenchSys'22)

Energy benchmarking is a growing practice in many cities across the world as part of the energy disclosure policy. Many cities have already started to reap the benefits of energy benchmarking with up to 8% energy savings. However, there remains a gap in the widespread adaptation of benchmarking methodologies in terms of their scalability and standardization (data acquisition, analytics, validation, reporting, and automation). This workshop aims to foster a discussion on the widespread adoption of energy benchmarking methods while bringing together researchers and practitioners from diverse backgrounds to discuss related challenges and breakthroughs. The workshop invites papers on the current developments in building energy benchmarking. Researchers and practitioners working on data acquisition technology and processes, data sharing protocols and policies, benchmarking modeling methodologies, standardization and widespread adoption, strategy and collaboration, case studies, open source platforms and crowdsourcing are invited to participate.

General Co-Chairs

  • Chirag Deb (Indian Institute of Technology Bombay, India)
  • Balaji Kalluri (Tamil Nadu e-Governance (TNeGA), India)
  • Prashant Anand (Indian Institute of Technology Kharagpur, India)
  • Jay Dhariwal (Indian Institute of Technology Delhi, India)

1st ACM International Workshop on The Future of Work, Workplaces, and Smart Buildings (FoWSB'22)

In the past decade, building industry has prioritized ‘sustainability’ within the built environment and cities. However, the pandemic has challenged the design-thinking within the industry. It forced reimagining the future of the work and workplaces in many ways. The 1st International Workshop on The Future of Work, Workplaces, and Smart Buildings (FoWSB) aims to bring together researchers and industry practitioners from data science, system sciences, building and urban sciences with particular interest to discuss the roleplay of smart buildings, communities, and cities in shaping the future of our work and workplaces. We aim to provide a scientific forum to delineate new value propositions, challenges, barriers, innovative approaches, and emerging opportunities at the nexus of digitizing work, workplace transformation, and the particular role of smart buildings (post-covid). The overall objective is to translate deeper scientific insights into practice, and in turn, mature the smart building marketplace in a fair and responsible way.

General Co-Chairs

  • Elizabeth Nelson (UTwente, Netherlands)
  • Heather Wray (TNO, Netherlands)
  • Balaji Kalluri (Former Fellow Innovation Fund Denmark, India)
  • Nicholas White (Smart Building Certification, Netherland)

2nd ACM International Workshop on Big Data and Machine Learning for Smart Buildings and Cities (BALANCES'22)

The 2nd ACM International Workshop on Big Data and Machine Learning for Smart Buildings and Cities workshop aims to open up discussions on: 1. Big data modeling paradigms that could be applicable in building and urban science; 2. Requirements on the data collection infrastructure needed for these modeling paradigms; 3. Challenges faced by current modeling approaches; and 4. Future research directions to fully utilize building and urban big data. An important part of the workshop will be dedicated to accelerating the open-world deployment of developed technologies, and highlighting challenges encountered in real-world large-scale pilots. For instance, how can existing and upcoming guidelines on model benchmarking and standardization unlock the potential of big data, help us better understand occupant behavior, and optimize energy consumption on building- and urban-scale.

General Co-Chairs

  • Bing Dong (Syracuse University, USA)
  • Salvatore Carlucci (The Cyprus Institute, Cyprus)
  • Hussain Syed Kazmi (KU Leuven, Belgium)
  • Zhipeng Deng (Syracuse University, USA)

2nd ACM SIGEnergy Workshop on Fair, Accountable, Transparent, and Ethical (FATE) AI for Smart Environments and Energy Systems (FATEsys'22)

Ubiquitous networked sensing, smart control, and efficient communication technologies have enabled “big” data generation which has led to an unprecedented adoption and growth of machine learning (ML) and artificial intelligence (AI) in the transformation of smart energy systems. ML/AI driven solutions are attaining new levels of accuracy and energy-efficiency for several problems related to smart energy systems. For the same reason, AI is anticipated to play an important role in achieving the goals of energy equity and environmental justice. However, while developing these models, it is equally important to hold the models accountable for the impact of their actions on humans in the loop. The second workshop on FATE of AI-Enabled Smart Energy Systems intends to foster discussion on both aspects of this topic and bring together researchers from diverse backgrounds to discuss challenges and breakthroughs in this multidisciplinary area of research.

General Co-Chairs

  • Milan Jain (Pacific Northwest National Laboratory Richland, USA)
  • Pandarasamy Arjunan (Berkeley Education Alliance for Research in Singapore (BEARS), Singapore)

5th International SenSys+BuildSys Workshop on Data: Acquisition to Analysis (DATA'22)

As the enthusiasm for and success of the Internet of Things (IoT), Cyber-Physical Systems (CPS), and Smart Buildings grows, so too does the volume and variety of data collected by these systems. How do we ensure that this data is of high quality, and how do we maximize the utility of collected data such that many projects can benefit from the time, cost, and effort of deployments?

The Data: Acquisition To Analysis (DATA) workshop aims to look broadly at interesting data from interesting sensing systems. The workshop considers problems, solutions, and results from all across the real-world data pipeline. We solicit submissions on unexpected challenges and solutions in the collection of datasets, on new and novel datasets of interest to the community, and on experiences and results—explicitly including negative results—in using prior datasets to develop new insights. The workshop aims to bring together a community of application researchers and algorithm researchers in the sensing systems and building domains to promote breakthroughs from integration of the generators and users of datasets. The workshop will foster cross-domain understanding by enabling both the understanding of application needs and data collection limitations.

General Co-Chairs

  • Gabe Fierro (Colorado School of Mines, USA)
  • Shiwei Fang (University of Massachusetts Amherst, USA)

3rd ACM SIGEnergy Workshop on Reinforcement Learning for Energy Management in Buildings & Cities (RLEM’22)

RLEM brings together researchers and industry practitioners for the advancement of (deep) reinforcement learning (RL) and artificial intelligence (AI) in the built environment as it is applied for managing energy in civil infrastructure systems (energy, water, transportation). A major driver for decarbonization are integration of renewable energy systems (RES) into the grid, and photovoltaics (PV) and solar-thermal collectors as well as thermal and electric storage into residential and commercial buildings. Electric vehicles (EVs), with their storage capacity and inherent connectivity, hold a great potential for integration with buildings.

The integration of these technologies must be done carefully to unlock their full potential. Artificial intelligence is regarded as a possible pathway to orchestrate these complexities of Smart Cities. In particular, (deep) reinforcement learning algorithms have seen an increased interest and have demonstrated human expert level performance in other domains, e.g., computer games. Research in the building and cities domain has been fragmented and with focus on different problems and using a variety of frameworks. The purpose of this Workshop is to build a growing community around this exciting topic, provide a platform for discussion for future research direction, and share common frameworks.

General Co-Chairs

  • Zoltan Nagy (University of Texas at Austin, USA)
  • Jan Drgona (Pacific Northwest National Laboratory, USA)

6th International Workshop on Non-Intrusive Load Monitoring (NILM'22)

NILM (or disaggregation) is a growing research field which began in 1985 with a report written by George W. Hart (MIT) for Electric Power Research Institute (EPRI). NILM is used to discern what electrical loads (e.g., appliances) are running within a home/building using only the aggregate power meter. Why? To help occupants understand how they and their appliance use energy so that they could conserve to either save money, the environment, or both.

The mission of this workshop is to serve as a forum for bringing together all the researchers, practitioners, and students that are working on the topic of energy disaggregation around the world.

General Co-Chairs

  • Lucas Pereira (Técnico Lisboa, Portugal)
  • Stephen Makonin (Simon Fraser University, Canada)
  • Wenpeng Luan (Tianjin University, China)

1st ACM BuildSys 2022 Tutorial on Electricity Demand Forecasting

This tutorial is aimed at energy analysts, engineers, and researchers who want to learn more about machine learning algorithms that can be used to predict energy demand on several aggregation levels, ranging from the building level to districts and entire countries. At each of these aggregation levels, different challenges are faced by the load forecaster. We will demonstrate how aggregation levels as well as different forecasting algorithms and feature pipelines affect forecasting performance, and how these should be utilized in practice for best results. The tutorial will also include a collaborative coding session to offer participants a chance to practice demonstrated skills on datasets at various aggregation scales. The tutorial will also include a brief overview of where to find open-source energy demand datasets. The tutorial will conclude with a brief discussion on downstream tasks where such forecasts can be used in the energy sector.

General Co-Chairs

  • Hussain Kazmi (KU Leuven, Belgium)
  • Chun Fu (National University of Singapore, Singapore)