Search Results for author: Ram Rajagopal

Found 29 papers, 6 papers with code

Constructing dynamic residential energy lifestyles using Latent Dirichlet Allocation

no code implementations22 Apr 2022 Xiao Chen, Chad Zanocco, June Flora, Ram Rajagopal

In this research, we propose and test a new framework for understanding residential electricity demand by using a dynamic energy lifestyles approach that is iterative and highly extensible.

NeuralProphet: Explainable Forecasting at Scale

1 code implementation29 Nov 2021 Oskar Triebe, Hansika Hewamalage, Polina Pilyugina, Nikolay Laptev, Christoph Bergmeir, Ram Rajagopal

NeuralProphet is a hybrid forecasting framework based on PyTorch and trained with standard deep learning methods, making it easy for developers to extend the framework.

Decision Making Time Series

EVGen: Adversarial Networks for Learning Electric Vehicle Charging Loads and Hidden Representations

no code implementations9 Aug 2021 Robert Buechler, Emmanuel Balogun, Arun Majumdar, Ram Rajagopal

The nexus between transportation, the power grid, and consumer behavior is more pronounced than ever before as the race to decarbonize the transportation sector intensifies.

Joint Optimization of Autonomous Electric Vehicle Fleet Operations and Charging Station Siting

no code implementations1 Jul 2021 Justin Luke, Mauro Salazar, Ram Rajagopal, Marco Pavone

Charging infrastructure is the coupling link between power and transportation networks, thus determining charging station siting is necessary for planning of power and transportation systems.

Large-Scale Scenarios of Electric Vehicle Charging with a Data-Driven Model of Control

1 code implementation25 May 2021 Siobhan Powell, Gustavo Vianna Cezar, Elpiniki Apostolaki-Iosifidou, Ram Rajagopal

We illustrate the methodology by generating scenarios for California's 2030 charging demand including multiple charging segments and controls, with scenarios run locally in under 50 seconds, and for assisting rate design modeling the large-scale impact of a new workplace charging rate.

Learning Neighborhood Representation from Multi-Modal Multi-Graph: Image, Text, Mobility Graph and Beyond

no code implementations6 May 2021 Tianyuan Huang, Zhecheng Wang, Hao Sheng, Andrew Y. Ng, Ram Rajagopal

Recent urbanization has coincided with the enrichment of geotagged data, such as street view and point-of-interest (POI).

Quick Line Outage Identification in Urban Distribution Grids via Smart Meters

no code implementations1 Apr 2021 Yizheng Liao, Yang Weng, Chin-Woo Tan, Ram Rajagopal

The growing integration of distributed energy resources (DERs) in distribution grids raises various reliability issues due to DER's uncertain and complex behaviors.

Change Point Detection Time Series +1

Generating private data with user customization

no code implementations2 Dec 2020 Xiao Chen, Thomas Navidi, Ram Rajagopal

Personal devices such as mobile phones can produce and store large amounts of data that can enhance machine learning models; however, this data may contain private information specific to the data owner that prevents the release of the data.

Activity Detection And Modeling Using Smart Meter Data: Concept And Case Studies

no code implementations26 Oct 2020 Hao Wang, Gonzague Henri, Chin-Woo Tan, Ram Rajagopal

Electricity consumed by residential consumers counts for a significant part of global electricity consumption and utility companies can collect high-resolution load data thanks to the widely deployed advanced metering infrastructure.

Action Detection Activity Detection

Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models

no code implementations9 Oct 2020 Eric Zelikman, Sharon Zhou, Jeremy Irvin, Cooper Raterink, Hao Sheng, Anand Avati, Jack Kelly, Ram Rajagopal, Andrew Y. Ng, David Gagne

Advancing probabilistic solar forecasting methods is essential to supporting the integration of solar energy into the electricity grid.

FedGAN: Federated Generative Adversarial Networks for Distributed Data

no code implementations12 Jun 2020 Mohammad Rasouli, Tao Sun, Ram Rajagopal

We propose Federated Generative Adversarial Network (FedGAN) for training a GAN across distributed sources of non-independent-and-identically-distributed data sources subject to communication and privacy constraints.

Time Series

Effective Data Fusion with Generalized Vegetation Index: Evidence from Land Cover Segmentation in Agriculture

no code implementations7 May 2020 Hao Sheng, Xiao Chen, Jingyi Su, Ram Rajagopal, Andrew Ng

How can we effectively leverage the domain knowledge from remote sensing to better segment agriculture land cover from satellite images?

Urban2Vec: Incorporating Street View Imagery and POIs for Multi-Modal Urban Neighborhood Embedding

no code implementations29 Jan 2020 Zhecheng Wang, Haoyuan Li, Ram Rajagopal

Understanding intrinsic patterns and predicting spatiotemporal characteristics of cities require a comprehensive representation of urban neighborhoods.

Document Embedding Semantic Similarity +1

AR-Net: A simple Auto-Regressive Neural Network for time-series

2 code implementations27 Nov 2019 Oskar Triebe, Nikolay Laptev, Ram Rajagopal

In this paper we present a new framework for time-series modeling that combines the best of traditional statistical models and neural networks.

Time Series

Energy Resource Control via Privacy Preserving Data

1 code implementation4 Oct 2019 Xiao Chen, Thomas Navidi, Ram Rajagopal

Although the frequent monitoring of smart meters enables granular control over energy resources, it also increases the risk of leakage of private information such as income, home occupancy, and power consumption behavior that can be inferred from the data by an adversary.

Systems and Control Computers and Society Systems and Control

Cloud Storage for Multi-Service Battery Operation (Extended Version)

no code implementations17 May 2019 Mohammad Rasouli, Tao Sun, Camille Pache, Patrick Panciatici, Jean Maeght, Ramesh Johari, Ram Rajagopal

The methodology consists in modelling the problem as a two-stage stochastic optimization between high priority stochastic grid services and low priority cloud storage for stochastic end users.

Stochastic Optimization

On the Interaction between Autonomous Mobility on Demand Systems and Power Distribution Networks -- An Optimal Power Flow Approach

1 code implementation1 May 2019 Alvaro Estandia, Maximilian Schiffer, Federico Rossi, Justin Luke, Emre Can Kara, Ram Rajagopal, Marco Pavone

Specifically, we extend previous results on an optimization-based modeling approach for electric AMoD systems to jointly control an electric AMoD fleet and a series of PDNs, and analyze the benefit of coordination under load balancing constraints.

Self-Driving Cars

Generative Adversarial Models for Learning Private and Fair Representations

no code implementations ICLR 2019 Chong Huang, Xiao Chen, Peter Kairouz, Lalitha Sankar, Ram Rajagopal

We present Generative Adversarial Privacy and Fairness (GAPF), a data-driven framework for learning private and fair representations of the data.

Fairness

Distributed generation of privacy preserving data with user customization

no code implementations20 Apr 2019 Xiao Chen, Thomas Navidi, Stefano Ermon, Ram Rajagopal

Distributed devices such as mobile phones can produce and store large amounts of data that can enhance machine learning models; however, this data may contain private information specific to the data owner that prevents the release of the data.

Fast Distribution Grid Line Outage Identification with $μ$PMU

no code implementations14 Nov 2018 Yizheng Liao, Yang Weng, Chin-Woo Tan, Ram Rajagopal

This makes the theory on optimal change-point detection suitable to identify line outages via $\mu$PMUs with fast and accurate sampling.

Change Point Detection Time Series +1

Understanding Compressive Adversarial Privacy

no code implementations21 Sep 2018 Xiao Chen, Peter Kairouz, Ram Rajagopal

Designing a data sharing mechanism without sacrificing too much privacy can be considered as a game between data holders and malicious attackers.

Unbalanced Multi-Phase Distribution Grid Topology Estimation and Bus Phase Identification

no code implementations18 Sep 2018 Yizheng Liao, Yang Weng, Guangyi Liu, Zhongyang Zhao, Chin-Woo Tan, Ram Rajagopal

Then, this paper proves that the Chow-Liu algorithm finds the topology by utilizing power flow equations and the conditional independence relationships implied by the radial multi-phase structure of distribution grids with the presence of incorrect bus phase labels.

Generative Adversarial Privacy

no code implementations ICLR 2019 Chong Huang, Peter Kairouz, Xiao Chen, Lalitha Sankar, Ram Rajagopal

We present a data-driven framework called generative adversarial privacy (GAP).

Siamese Generative Adversarial Privatizer for Biometric Data

no code implementations23 Apr 2018 Witold Oleszkiewicz, Peter Kairouz, Karol Piczak, Ram Rajagopal, Tomasz Trzcinski

Extensive evaluation on a biometric dataset of fingerprints and cartoon faces confirms usefulness of our simple yet effective method.

Emotion Recognition

Context-Aware Generative Adversarial Privacy

no code implementations26 Oct 2017 Chong Huang, Peter Kairouz, Xiao Chen, Lalitha Sankar, Ram Rajagopal

On the one hand, context-free privacy solutions, such as differential privacy, provide strong privacy guarantees, but often lead to a significant reduction in utility.

Urban MV and LV Distribution Grid Topology Estimation via Group Lasso

no code implementations6 Nov 2016 Yizheng Liao, Yang Weng, Guangyi Liu, Ram Rajagopal

The increasing penetration of distributed energy resources poses numerous reliability issues to the urban distribution grid.

A Sparse Linear Model and Significance Test for Individual Consumption Prediction

no code implementations5 Nov 2015 Pan Li, Baosen Zhang, Yang Weng, Ram Rajagopal

Accurate prediction of user consumption is a key part not only in understanding consumer flexibility and behavior patterns, but in the design of robust and efficient energy saving programs as well.

Two-sample testing

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