Importantly, E2DC concentrates on a broader context of data centre ecosystem by investigation of interactions with smart cities and smart grids. The main goal of the E2DC workshop is to study innovative methods to improve efficiency and sustainability of data centres - important and ever growing group of energy consumers. Spanos, University of California, Berkeley ( Please find more information (e.g., scope, submission instructions, and workshop schedule) on the workshop website: /amlies/2022 Energy-Efficient Data Centres (E2DC2022)ġ0th International Workshop on Energy-Efficient Data Centres (E2DC2022)
Word and LaTeX templates are available on theĬontact: For any questions, please contact one of the organisers: Papers that do not meet the size and formatting requirements may not be reviewed. Short papers, up to 4 pages in 9-point ACM double-column format (excluding references) and an unlimited number of pages for appendices and references, single-blind.
Latex for word acm full#
Full papers, up to 8 pages in 9-point ACM double-column format (excluding references) and an unlimited number of pages for appendices and references, single-blind.Two types of contributions are solicited: Paper submission deadline: April 11, 2022 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. Data from opportunistic sources (such as images or communication network data) can be adopted to overcome this challenge.
Latex for word acm drivers#
Another challenge is the lack of direct measurements for important drivers of energy consumption, such as occupancy.
Efficiently using the available energy data for inference, decision and control can prove beneficial in this scenario. Collection and labeling of the data in complex systems such as power grids and buildings with complicated energy usage behaviors require a considerable amount of expert knowledge and is often prone to security and privacy issues. To perform optimally, these algorithms require a substantial amount of labeled training data. However, the application of machine learning to these energy systems has also opened up new research challenges. This has led to improved energy efficiency, occupant comfort, and productivity in smart buildings and enhanced system robustness and resilience in power systems. 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 modeling, control, fault, and anomaly detection and optimization of energy, space, and cost amongst others. Technological advancements in sensing, learning, control, and optimization hold the 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. 4th International Workshop on Applied Machine Learning for Intelligent Energy Systems (AMLIES) 2022Ībstract: 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.
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