J
Jessica Granderson
Researcher at Lawrence Berkeley National Laboratory
Publications - 110
Citations - 1803
Jessica Granderson is an academic researcher from Lawrence Berkeley National Laboratory. The author has contributed to research in topics: Efficient energy use & Measurement and Verification. The author has an hindex of 19, co-authored 98 publications receiving 1316 citations. Previous affiliations of Jessica Granderson include University of California, Berkeley.
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Gradient boosting machine for modeling the energy consumption of commercial buildings
TL;DR: The results show that using the gradient boosting machine model improved the R‐squared prediction accuracy and the CV(RMSE) in more than 80 percent of the cases, when compared to an industry best practice model that is based on piecewise linear regression, and to a random forest algorithm.
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Robust on-line fault detection diagnosis for HVAC components based on nonlinear state estimation techniques
TL;DR: In this article, a robust and computationally efficient algorithm for both whole-building and component-level energy fault detection and diagnosis (FDD) is presented, which is able to provide reliable estimation of multiple and simultaneous fault conditions, even in the presence of noisy and sometimes erroneous sensor data.
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Building energy information systems: user case studies
TL;DR: In this article, the authors present case studies of energy information systems (EIS) at four enterprises and university campuses, focusing on the attained energy savings, and successes and challenges in technology use and integration.
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Fuel use and design analysis of improved woodburning cookstoves in the Guatemalan Highlands
TL;DR: In this paper, the authors examined the fuel use and design of an improved wood-burning cookstove (plancha), in comparison to traditional cooking over an open woodfire.
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Accuracy of automated measurement and verification (M&V) techniques for energy savings in commercial buildings
Jessica Granderson,Samir Touzani,Claudine Custodio,Michael D. Sohn,David Jump,Samuel Fernandes +5 more
TL;DR: In this article, the authors present a testing procedure and metrics to assess the performance of whole-building M&V methods, and illustrate the test procedure by evaluating the accuracy of ten baseline energy use models, against measured data from a large dataset of 537 buildings.