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.
New method predicts heat load in thermal grids using latent variables.
Understanding the heat usage of customers is crucial for effective district heating operations and management. Unfortunately, existing knowledge about customers and their heat load behaviors is quite scarce. Most previous studies are limited to small-scale analyses that are not representative enough to understand the b…
Adaptive probabilistic load forecasting improves performance in power systems.
Bayesian Transformer improves probabilistic load forecasting with calibrated uncertainty estimates.
Model forecasts hourly electricity demand influenced by weather, socio-economic, and political factors.
CityTFT models urban building energy using a data-driven approach.
Enhances load forecasting for multiple entities with dynamic similarities.
Short-term load forecasting (STLF) is essential for the reliable and economic operation of power systems. Though many STLF methods were proposed over the past decades, most of them focused on loads at high aggregation levels only. Thus, low-aggregation load forecast still requires further research and development. Comp…
Active learning reduces smart meter data needs for better electric load predictions.
Heat demand prediction is a prominent research topic in the area of intelligent energy networks. It has been well recognized that periodicity is one of the important characteristics of heat demand. Seasonal-trend decomposition based on LOESS (STL) algorithm can analyze the periodicity of a heat demand series, and decom…
To investigate whether training load monitoring data could be used to predict injuries in elite Australian football players, data were collected from elite athletes over 3 seasons at an Australian football club. Loads were quantified using GPS devices, accelerometers and player perceived exertion ratings. Absolute and …
Model uses GAMs to forecast hourly electricity load weeks to one year ahead.
Machine learning reconstructs aerodynamic forces from noisy data.
As an important part of the power system, power load forecasting directly affects the national economy. The data shows that improving the load forecasting accuracy by 0.01% can save millions of dollars for the power industry. Therefore, improving the accuracy of power load forecasting has always been the pursuing goals…
Airlines optimize fuel loading with better flight time predictions.
TIP-Search optimizes market prediction accuracy and timeliness under uncertain load.
The study improves load forecasting for electricity consumers using advanced machine learning models.
Paper uses econometrics time series model with T-student Distribution for short-term load forecasting.
Paper proposes combining GAM and DNN for accurate peak demand estimation from lower-resolution data.
Accurate short-term load forecasting is essential for the efficient operation of the power sector. Forecasting load at a fine granularity such as hourly loads of individual households is challenging due to higher volatility and inherent stochasticity. At the aggregate levels, such as monthly load at a grid, the uncerta…
Paper presents a method for probabilistic load forecasting using adaptive online learning.
Model predicts EMF of Ni-Mn-Ga MSMA, improved with GRNN.
The paper proposes a new method for probabilistic load forecasting using Bernstein-Polynomial Normalizing Flows.
New methods improve electricity load forecasting using hierarchical transfer learning.
Paper proposes a network framework for prosumers to manage peak loads in Iran.
The assessment of energy expenditure in real life is of great importance for monitoring the current physical state of people, especially in work, sport, elderly care, health care, and everyday life even. This work reports about application of some machine learning methods (linear regression, linear discriminant analysi…
Management and efficient operations in critical infrastructure such as Smart Grids take huge advantage of accurate power load forecasting which, due to its nonlinear nature, remains a challenging task. Recently, deep learning has emerged in the machine learning field achieving impressive performance in a vast range of …
Deep model integrates MRI and DTI for autism severity prediction.
Many post-disaster and -conflict regions do not have sufficient data on their transportation infrastructure assets, hindering both mobility and reconstruction. In particular, as the number of aging and deteriorating bridges increase, it is necessary to quantify their load characteristics in order to inform maintenance …
The study predicts Kronecker coefficients using interpretable machine learning models.
Deep Learning is applied to energy markets to predict extreme loads observed in energy grids. Forecasting energy loads and prices is challenging due to sharp peaks and troughs that arise due to supply and demand fluctuations from intraday system constraints. We propose deep spatio-temporal models and extreme value theo…
Paper benchmarks and customizes energy forecasting methods.
Methane is considered being a good choice as a propellant for future reusable launch systems. However, the heat transfer prediction for supercritical methane flowing in cooling channels of a regeneratively cooled combustion chamber is challenging. Because accurate heat transfer predictions are essential to design relia…
This paper proposes an unsupervised learning method to solve heat equations on chips.
Predicts coherence from quantum heat engine noise using machine learning.
In aircraft industry, market needs evolve quickly in a high competitiveness context. This requires adapting a given aircraft model in minimum time considering for example an increase of range or the number of passengers (cf A330 NEO family). The computation of loads and stress to resize the airframe is on the critical …
Traditional load analysis is facing challenges with the new electricity usage patterns due to demand response as well as increasing deployment of distributed generations, including photovoltaics (PV), electric vehicles (EV), and energy storage systems (ESS). At the transmission system, despite of irregular load behavio…
Proposes a machine learning framework for more efficient economic dispatch.
Framework integrates brain connectivity data for clinical predictions.
We propose a new forecasting method for predicting load demand and generation scheduling. Accurate week-long forecasting of load demand and optimal power generation is critical for efficient operation of power grid systems. In this work, we use a synthetic data set describing a power grid with 700 buses and 134 generat…
SPLICE generates accurate time-series imputations with reliable prediction intervals.
Heat kernel resurgent structure from Picard-Lefschetz theory
Detecting faults and SLA violations in a timely manner is critical for telecom providers, in order to avoid loss in business, revenue and reputation. At the same time predicting SLA violations for user services in telecom environments is difficult, due to time-varying user demands and infrastructure load conditions. In…
Frugal method predicts multiple local electricity loads efficiently.
Distribution feeder long-term load forecast (LTLF) is a critical task many electric utility companies perform on an annual basis. The goal of this task is to forecast the annual load of distribution feeders. The previous top-down and bottom-up LTLF methods are unable to incorporate different levels of information. This…
Develops a method to predict stock returns with time-varying risk premia.
Consumer Demand Response (DR) is an important research and industry problem, which seeks to categorize, predict and modify consumer's energy consumption. Unfortunately, traditional clustering methods have resulted in many hundreds of clusters, with a given consumer often associated with several clusters, making it diff…
Using artificial neural network for the prediction of heat demand has attracted more and more attention. Weather conditions, such as ambient temperature, wind speed and direct solar irradiance, have been identified as key input parameters. In order to further improve the model accuracy, it is of great importance to und…