- 5th International Workshop on Non-Intrusive Load Monitoring (NILM)
- 3rd International SenSys+BuildSys Workshop on Data: Acquisition to Analysis (DATA)
- 2nd ACM Workshop on Device-Free Human Sensing
- 1st International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities (RLEM)
On behalf of the organizing committee, we would like to invite you to participate in the 5th International Workshop on Non-Intrusive Load Monitoring (NILM), which will be held in November of 2020, this time in conjunction with the ACM International Conference on Systems for Energy-Efficient Buildings, Cities and Transportation (BuildSys).
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.
- David Irwin (University of Massachusetts, Amherst)
- Mario Berges (Carnegie Mellon University)
- Stephen Makonin (Simon Fraser University)
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.
- Gabe Fierro (UC Berkeley, USA)
- Mostafa Mirshekari (Stanford University, USA)
- Pat Pannuto (UCSD, USA)
- Yang Zhao (General Electric Research, USA)
The advent of human sensing has enabled many applications in smart buildings and cities, including healthcare, energy management, and marketing. Considerable work has been done in the recent past to sense the human using devices which they carry or wear, such as smartphones and wearables. These approaches, although potentially accurate, have high installation and maintenance requirement which limits their application in some real-life applications. For example, in eldercare monitoring, requiring the elderly to wear or carry a device at all times is an important limitation. This workshop aims to attract novel research papers which enhance device-free human sensing via either developing device-free sensing hardware or designing data-driven, physics-based, or physics-guided data-driven algorithms to extract meaningful information about human status, activities, and behavioral patterns.
- Mostafa Mirshekari (Stanford University, USA)
- Jonathon Fagert (Carnegie Mellon University, USA)
- Adeola Bannis (Carnegie Mellon University, USA)
Buildings account for 40% of the global energy consumption and 30% of the associated greenhouse gas emissions, while also offering a 50–90% CO2 mitigation potential. The transportation sector is responsible for an additional 30%. Optimal decarbonization requires electrification of end-uses and concomitant decarbonization of electricity supply, efficient use of electricity for lighting, space heating, cooling and ventilation (HVAC), and domestic hot water generation, and upgrade of the thermal properties of buildings. 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.
- Zoltan Nagy (University of Texas at Austin, USA)
- Mario Berges (Carnegie Mellon University, USA)