Adrian Albert

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.

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.

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.

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.

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.