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.
A new framework uses DDQN to simplify WECC CLM for efficient load modeling.
Asynchronous parallel optimization algorithms for solving large-scale machine learning problems have drawn significant attention from academia to industry recently. This paper proposes a novel algorithm, decoupled asynchronous proximal stochastic gradient descent (DAP-SGD), to minimize an objective function that is the…
FEA-Net uses physics knowledge to predict material responses efficiently.
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…
Paper uses econometrics time series model with T-student Distribution for short-term load forecasting.
Data loading can dominate deep neural network training time on large-scale systems. We present a comprehensive study on accelerating data loading performance in large-scale distributed training. We first identify performance and scalability issues in current data loading implementations. We then propose optimizations t…
New method predicts heat load in thermal grids using latent variables.
Deep learning boosts building energy load forecasting.
This paper uses a diffusion model to forecast electrical loads with uncertainty.
Deep learning improves weather modeling for electricity load forecasting.
A new method calculates risk loadings in classification ratemaking without subjective parameters.
Paper proposes dense average network for improved power load forecasting.
Paper presents a method for probabilistic load forecasting using adaptive online learning.
New method reduces high-dimensional data to key features.
HIV RNA viral load (VL) is an important outcome variable in studies of HIV infected persons. There exists only a handful of methods which classify patients by viral load patterns. Most methods place limits on the use of viral load measurements, are often specific to a particular study design, and do not account for com…
Machine learning reconstructs aerodynamic forces from noisy data.
Model uses GAMs to forecast hourly electricity load weeks to one year ahead.
Adaptive probabilistic load forecasting improves performance in power systems.
Develops a method to analyze SARS-CoV-2 viral load vs. age, finding a significant increase.
Enhances load forecasting for multiple entities with dynamic similarities.
Short-term load forecasting is a critical element of power systems energy management systems. In recent years, probabilistic load forecasting (PLF) has gained increased attention for its ability to provide uncertainty information that helps to improve the reliability and economics of system operation performances. This…
We present a methodology for probabilistic load forecasting that is based on lasso (least absolute shrinkage and selection operator) estimation. The model considered can be regarded as a bivariate time-varying threshold autoregressive(AR) process for the hourly electric load and temperature. The joint modeling approach…
The study improves load forecasting for electricity consumers using advanced machine learning models.
We present in this paper a model for forecasting short-term power loads based on deep residual networks. The proposed model is able to integrate domain knowledge and researchers' understanding of the task by virtue of different neural network building blocks. Specifically, a modified deep residual network is formulated…
Improves distributed SGD convergence speed with reduced computation load.
Paper proposes a new method for hourly load forecasting using smart meter data.
Improved electrical load forecasting model using Fourier-enhanced RNN.
In (exploratory) factor analysis, the loading matrix is identified only up to orthogonal rotation. For identifiability, one thus often takes the loading matrix to be lower triangular with positive diagonal entries. In Bayesian inference, a standard practice is then to specify a prior under which the loadings are indepe…
Simple 1D-CNN network predicts electricity loads 36 hours ahead.
Model for open, decentralized network with task load balancing.
Several statistical and machine learning methods are proposed to estimate the type and intensity of physical load and accumulated fatigue . They are based on the statistical analysis of accumulated and moving window data subsets with construction of a kurtosis-skewness diagram. This approach was applied to the data gat…
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…
New method infers viral load from pooled tests.
We use online convex optimization (OCO) for setpoint tracking with uncertain, flexible loads. We consider full feedback from the loads, bandit feedback, and two intermediate types of feedback: partial bandit where a subset of the loads are individually observed and the rest are observed in aggregate, and Bernoulli feed…
Improved non-intrusive load monitoring with a novel neural network.
New principle reduces load imbalance in LLM serving systems, saving up to 52% energy.
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 …
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 forecasts hourly electricity demand influenced by weather, socio-economic, and political factors.
Recent studies have shown that the aggregated dynamic flexibility of an ensemble of thermostatic loads can be modeled in the form of a virtual battery. The existing methods for computing the virtual battery parameters require the knowledge of the first-principle models and parameter values of the loads in the ensemble.…
Active learning reduces smart meter data needs for better electric load predictions.
Proposes ML-RBM for non-intrusive load monitoring without appliance-level data.
The autoencoder is an effective unsupervised learning model which is widely used in deep learning. It is well known that an autoencoder with a single fully-connected hidden layer, a linear activation function and a squared error cost function trains weights that span the same subspace as the one spanned by the principa…
The paper is motivated by a problem concerning the monotonicity of insurance premiums with respect to their loading parameter: the larger the parameter, the larger the insurance premium is expected to be. This property, usually called loading monotonicity, is satisfied by premiums that appear in the literature. The inc…
RL applied to TCLs for power consumption control.
Study shows cognitive load impacts financial market efficiency, especially for less sophisticated investors.
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…
This paper monetizes customer load data to boost energy retailer profits.