Zobrazeno 1 - 10
of 286
pro vyhledávání: '"Kathirgamanathan, P"'
Correlations in streams of multivariate time series data means that typically, only a small subset of the features are required for a given data mining task. In this paper, we propose a technique which we call Merit Score for Time-Series data (MSTS)
Externí odkaz:
http://arxiv.org/abs/2112.03705
In Machine Learning, feature selection entails selecting a subset of the available features in a dataset to use for model development. There are many motivations for feature selection, it may result in better models, it may provide insight into the d
Externí odkaz:
http://arxiv.org/abs/2106.06437
This research is concerned with the novel application and investigation of `Soft Actor Critic' (SAC) based Deep Reinforcement Learning (DRL) to control the cooling setpoint (and hence cooling loads) of a large commercial building to harness energy fl
Externí odkaz:
http://arxiv.org/abs/2104.12125
Publikováno v:
In: Advanced Analytics and Learning on Temporal Data. AALTD 2020. LNCS, vol 12588. Springer, Cham (2020)
Time-series data in application areas such as motion capture and activity recognition is often multi-dimension. In these application areas data typically comes from wearable sensors or is extracted from video. There is a lot of redundancy in these da
Externí odkaz:
http://arxiv.org/abs/2104.11110
Autor:
Wang, Yuchen, Chee, Matthieu Chan, Edher, Ziyad, Hoang, Minh Duc, Fujimori, Shion, Kathirgamanathan, Sornnujah, Bettencourt, Jesse
Black Sigatoka disease severely decreases global banana production, and climate change aggravates the problem by altering fungal species distributions. Due to the heavy financial burden of managing this infectious disease, farmers in developing count
Externí odkaz:
http://arxiv.org/abs/2012.00752
Reinforcement learning is a promising model-free and adaptive controller for demand side management, as part of the future smart grid, at the district level. This paper presents the results of the algorithm that was submitted for the CityLearn Challe
Externí odkaz:
http://arxiv.org/abs/2009.10562
Managing supply and demand in the electricity grid is becoming more challenging due to the increasing penetration of variable renewable energy sources. As significant end-use consumers, and through better grid integration, buildings are expected to p
Externí odkaz:
http://arxiv.org/abs/2007.14866
Autor:
Miller, Clayton, Arjunan, Pandarasamy, Kathirgamanathan, Anjukan, Fu, Chun, Roth, Jonathan, Park, June Young, Balbach, Chris, Gowri, Krishnan, Nagy, Zoltan, Fontanini, Anthony, Haberl, Jeff
Publikováno v:
Science and Technology for the Built Environment, 26:10, 1427-1447, (2020)
In late 2019, ASHRAE hosted the Great Energy Predictor III (GEPIII) machine learning competition on the Kaggle platform. This launch marked the third energy prediction competition from ASHRAE and the first since the mid-1990s. In this updated version
Externí odkaz:
http://arxiv.org/abs/2007.06933
Autor:
Miller, Clayton, Kathirgamanathan, Anjukan, Picchetti, Bianca, Arjunan, Pandarasamy, Park, June Young, Nagy, Zoltan, Raftery, Paul, Hobson, Brodie W., Shi, Zixiao, Meggers, Forrest
Publikováno v:
Scientific Data volume 7, Article number: 368 (2020)
This paper describes an open data set of 3,053 energy meters from 1,636 non-residential buildings with a range of two full years (2016 and 2017) at an hourly frequency (17,544 measurements per meter resulting in approximately 53.6 million measurement
Externí odkaz:
http://arxiv.org/abs/2006.02273
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