A
Adrian Albert
Researcher at Lawrence Berkeley National Laboratory
Publications - 38
Citations - 1992
Adrian Albert is an academic researcher from Lawrence Berkeley National Laboratory. The author has contributed to research in topics: Energy consumption & Smart meter. The author has an hindex of 16, co-authored 38 publications receiving 1448 citations. Previous affiliations of Adrian Albert include Jacobs University Bremen & Massachusetts Institute of Technology.
Papers
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Journal ArticleDOI
Is disaggregation the holy grail of energy efficiency? The case of electricity
TL;DR: In this article, a set of statistical approaches for extracting end-use and/or appliance level data from an aggregate, or whole-building, energy signal is presented. And the authors explain how appliance-level data affords numerous benefits and why using the algorithms in conjunction with smart meters is the most cost-effective and scalable solution for getting this data.
Journal ArticleDOI
Smart Meter Driven Segmentation: What Your Consumption Says About You
Adrian Albert,Ram Rajagopal +1 more
TL;DR: This work proposes to infer occupancy states from consumption time series data using a hidden Markov model framework and uses this framework to argue that there is a large degree of individual predictability in user consumption at a population level.
Proceedings ArticleDOI
Towards Physics-informed Deep Learning for Turbulent Flow Prediction
TL;DR: This paper proposes a hybrid approach to predict turbulent flow by learning its highly nonlinear dynamics from spatiotemporal velocity fields of large-scale fluid flow simulations of relevance to turbulence modeling and climate modeling by marrying two well-established turbulent flow simulation techniques with deep learning.
Journal ArticleDOI
HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community
Chaopeng Shen,Eric Laloy,Amin Elshorbagy,Adrian Albert,Jerad D. Bales,Fi-John Chang,Sanmay Ganguly,Kuolin Hsu,Daniel Kifer,Zheng Fang,Kuai Fang,Dongfeng Li,Xiaodong Li,Wen-Ping Tsai +13 more
TL;DR: This paper suggests that DL-based methods can open up a complementary avenue toward knowledge discovery in hydrologic sciences, and suggests that integrating process-based models with DL models will help alleviate data limitations.
Posted Content
Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale
TL;DR: This work analyzes patterns in land use in urban neighborhoods using large-scale satellite imagery data and state-of-the-art computer vision techniques based on deep convolutional neural networks and shows that the deep representations extracted from satellite imagery of urban environments can be used to compare neighborhoods across several cities.