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Found 41 papers, 7 papers with code
EV-EcoSim: A grid-aware co-simulation platform for the design and optimization of electric vehicle charging infrastructure
In this work, we present EV-EcoSim, a co-simulation platform that couples electric vehicle charging, battery systems, solar photovoltaic systems, grid transformers, control strategies, and power distribution systems, to perform cost quantification and analyze the impacts of electric vehicle charging on the grid.
CityPulse: Fine-Grained Assessment of Urban Change with Street View Time Series
Urban transformations have profound societal impact on both individuals and communities at large.
SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensing
Remote sensing imagery, despite its broad applications in helping achieve Sustainable Development Goals and tackle climate change, has not yet benefited from the recent advancements of versatile, task-agnostic vision language models (VLMs).
Improving the Load Flexibility of Stratified Electric Water Heaters: Design and Experimental Validation of MPC Strategies
In this work, we analyze how modeling tank stratification in an MPC formulation impacts control performance for stratified electric water heaters under time-of-use (TOU) rates.
Coordination of DERs for Grid Reliability via Day-ahead Demand-Supply Power Bounds
A local controller at each consumer node then determines the DER power injections which satisfy the consumer's objectives while obeying its supply bounds.
TemperatureGAN: Generative Modeling of Regional Atmospheric Temperatures
Stochastic generators are useful for estimating climate impacts on various sectors.
Coordinating Distributed Energy Resources for Reliability can Significantly Reduce Future Distribution Grid Upgrades and Peak Load
Current DER coordination schemes, such as demand-response and VPPs, aim to reduce electricity costs during peak demand events with no consideration of distribution grid reliability.
Empirical Exploration of Zone-by-zone Energy Flexibility: a Non-intrusive Load Disaggregation Approach for Commercial Buildings
Moreover, a small number of zones accounted for a large amount of energy use and energy flexibility; and the most energy-intensive zones are not necessarily the most energy-flexible zones.
Dynamic locational marginal emissions via implicit differentiation
The method is model agnostic; it can compute LMEs for any convex optimization-based dispatch model, including some of the complex dispatch models employed by system operators in real electricity systems.
Detecting Neighborhood Gentrification at Scale via Street-level Visual Data
Neighborhood gentrification plays a significant role in shaping the social and economic well-being of both individuals and communities at large.
Effect of State of Charge Uncertainty on Battery Energy Storage Systems
Battery energy storage systems (BESSs) provide many benefits to the electricity grid, including stability, backup power, and flexibility in introducing more clean energy sources.
Estimating building energy efficiency from street view imagery, aerial imagery, and land surface temperature data
Current methods to determine the energy efficiency of buildings require on-site visits of certified energy auditors which makes the process slow, costly, and geographically incomplete.
Constructing dynamic residential energy lifestyles using Latent Dirichlet Allocation
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
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.
EVGen: Adversarial Networks for Learning Electric Vehicle Charging Loads and Hidden Representations
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
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
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
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
The growing integration of distributed energy resources (DERs) in distribution grids raises various reliability issues due to DER's uncertain and complex behaviors.
An Enriched Automated PV Registry: Combining Image Recognition and 3D Building Data
While photovoltaic (PV) systems are installed at an unprecedented rate, reliable information on an installation level remains scarce.
Generating private data with user customization
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
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.
Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models
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
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.
Effective Data Fusion with Generalized Vegetation Index: Evidence from Land Cover Segmentation in Agriculture
How can we effectively leverage the domain knowledge from remote sensing to better segment agriculture land cover from satellite images?
The 1st Agriculture-Vision Challenge: Methods and Results
The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset.
Urban2Vec: Incorporating Street View Imagery and POIs for Multi-Modal Urban Neighborhood Embedding
Understanding intrinsic patterns and predicting spatiotemporal characteristics of cities require a comprehensive representation of urban neighborhoods.
AR-Net: A simple Auto-Regressive Neural Network for time-series
In this paper we present a new framework for time-series modeling that combines the best of traditional statistical models and neural networks.
Energy Resource Control via Privacy Preserving Data
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
On the Interaction between Autonomous Mobility on Demand Systems and Power Distribution Networks -- An Optimal Power Flow Approach
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.
Generative Adversarial Models for Learning Private and Fair Representations
We present Generative Adversarial Privacy and Fairness (GAPF), a data-driven framework for learning private and fair representations of the data.
Distributed generation of privacy preserving data with user customization
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
This makes the theory on optimal change-point detection suitable to identify line outages via $\mu$PMUs with fast and accurate sampling.
Understanding Compressive Adversarial Privacy
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
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
We present a data-driven framework called generative adversarial privacy (GAP).
Siamese Generative Adversarial Privatizer for Biometric Data
Extensive evaluation on a biometric dataset of fingerprints and cartoon faces confirms usefulness of our simple yet effective method.
Context-Aware Generative Adversarial Privacy
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
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
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.
