A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Deep learning detects inaccurate smart meters for resource savings.
Study develops a machine learning-based ramp metering model to improve freeway efficiency.
Due to limited metering infrastructure, distribution grids are currently challenged by observability issues. On the other hand, smart meter data, including local voltage magnitudes and power injections, are communicated to the utility operator from grid buses with renewable generation and demand-response programs. This…
Hierarchical clustering models capture electricity load patterns from smart meters.
Paper assesses the market value of sharing privacy-protected smart meter data.
Simplifying machine learning (ML) application development, including distributed computation, programming interface, resource management, model selection, etc, has attracted intensive interests recently. These research efforts have significantly improved the efficiency and the degree of automation of developing ML mode…
Recognizing a piece of writing as a poem or prose is usually easy for the majority of people; however, only specialists can determine which meter a poem belongs to. In this paper, we build Recurrent Neural Network (RNN) models that can classify poems according to their meters from plain text. The input text is encoded …
This paper proposes a joint energy and data market to handle uncertainty in energy procurement.
Distribution grids currently lack comprehensive real-time metering. Nevertheless, grid operators require precise knowledge of loads and renewable generation to accomplish any feeder optimization task. At the same time, new grid technologies, such as solar photovoltaics and energy storage units are interfaced via invert…
Supervised learning with a deep convolutional neural network is used to identify the QCD equation of state (EoS) employed in relativistic hydrodynamic simulations of heavy-ion collisions from the simulated final-state particle spectra . High-level correlations of learned by the neural network act a…
New method identifies distribution grid outages using smart meter data.
The paper develops a multi-unit soft sensing model for virtual flow meters that improves few-shot learning.
Active learning reduces smart meter data needs for better electric load predictions.
Deep learning detects cyber-attacks in smart grid systems.
Hybrid approach protects privacy while analyzing smart meter data.
There is an increasing need for monitoring and controlling uncertainties brought by distributed energy resources in distribution grids. For such goal, accurate multi-phase topology is the basis for correlating measurements in unbalanced distribution networks. Unfortunately, such topology knowledge is often unavailable …
CROCS clusters consumer behaviour from smart meters, capturing variability and robustness.
Investigations have been performed into using clustering methods in data mining time-series data from smart meters. The problem is to identify patterns and trends in energy usage profiles of commercial and industrial customers over 24-hour periods, and group similar profiles. We tested our method on energy usage data p…
New LSTM model predicts disaggregated electricity loads accurately.
Study shows Bitcoin mining with surplus electricity can boost KEPCO's financial stability.
New indicator detects financial strain through smart meter data.
Power meters are becoming a widely used tool for measuring training and racing effort in cycling, and are now spreading also to other sports. This means that increasing volumes of data can be collected from athletes, with the aim of helping coaches and athletes analyse and understanding training load, racing efforts, t…
Paper proposes a new method for hourly load forecasting using smart meter data.
Active sensing improves energy breakdown without installing sensors.
Adversarial attacks can fool ML energy theft detection models.
Study compares LoRaWAN RSSI methods for indoor device localization.
A new risk measure (FRM) for EM FI returns helps investors protect against volatility and policy instability.
The paper proposes a new method for probabilistic load forecasting using Bernstein-Polynomial Normalizing Flows.
Deep learning model predicts European weather parameters.
Recently, the need of deploying new wireless networks for smart gas metering has raised the problem of radio planning in the169 MHz band. Unluckily, software tools commonly adopted for radio planning in cellular communication systems cannot be employed to solve this problem because of the substantially lower transmissi…
New methods improve electricity load forecasting using hierarchical transfer learning.
Deep neural networks predict electricity consumption accurately.
The increasing penetration of distributed energy resources poses numerous reliability issues to the urban distribution grid. The topology estimation is a critical step to ensure the robustness of distribution grid operation. However, the bus connectivity and grid topology estimation are usually hard in distribution gri…
This paper has been withdrawn by the author due to some inaccurate descriptions in the section of INTRODUCTION and CONCLUSIONS.
Smart Meters (SMs) are able to share the power consumption of users with utility providers almost in real-time. These fine-grained signals carry sensitive information about users, which has raised serious concerns from the privacy viewpoint. In this paper, we focus on real-time privacy threats, i.e., potential attacker…
Big spatio-temporal datasets, available through both open and administrative data sources, offer significant potential for social science research. The magnitude of the data allows for increased resolution and analysis at individual level. While there are recent advances in forecasting techniques for highly granular te…
Paper proposes a novel optimization method for disaggregating smart meter data.
The study examines how many samples are needed to minimize a noisy convex function with inaccurate gradient estimates.
CNN model predicts fluvial floods quickly and accurately.
Actor-critic methods can achieve incredible performance on difficult reinforcement learning problems, but they are also prone to instability. This is partly due to the interaction between the actor and critic during learning, e.g., an inaccurate step taken by one of them might adversely affect the other and destabilize…
Improved GPS accuracy in urban areas.
Study optimizes Sigfox-based RSSI fingerprinting for outdoor localization.
Motivated by electricity consumption metering, we extend existing nonnegative matrix factorization (NMF) algorithms to use linear measurements as observations, instead of matrix entries. The objective is to estimate multiple time series at a fine temporal scale from temporal aggregates measured on each individual serie…
A multiple instance dictionary learning method using functions of multiple instances (DL-FUMI) is proposed to address target detection and two-class classification problems with inaccurate training labels. Given inaccurate training labels, DL-FUMI learns a set of target dictionary atoms that describe the most distincti…
An important problem in sequential decision-making under uncertainty is to use limited data to compute a safe policy, i.e., a policy that is guaranteed to perform at least as well as a given baseline strategy. In this paper, we develop and analyze a new model-based approach to compute a safe policy when we have access …
We study the value of information in sequential compressed sensing by characterizing the performance of sequential information guided sensing in practical scenarios when information is inaccurate. In particular, we assume the signal distribution is parameterized through Gaussian or Gaussian mixtures with estimated mean…
This thesis tackles NILM challenges with a new dataset and efficient edge deployment techniques.
Bayesian neural networks improve uncertainty in data-driven VFMs for oil and gas wells.