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.
Paper proposes dense average network for improved power load forecasting.
EGPR method forecasts power grid load and generation with high accuracy.
RL applied to TCLs for power consumption control.
Novel method improves load estimation in power grids using anomaly and change point detection.
A new framework uses DDQN to simplify WECC CLM for efficient load modeling.
Study finds cherry-picking load shaping strategies outperforms others in reducing grid CO2 emissions.
A new method uses Gaussian Processes to solve power flow problems with uncertain renewable and load inputs.
Paper proposes a new method for hourly load forecasting using smart meter data.
Paper uses econometrics time series model with T-student Distribution for short-term load forecasting.
As energy markets begin clearing at sub-hourly rates, their interaction with load control systems becomes a potentially important consideration. A simple model for the control of thermal systems using market-based power distribution strategies is proposed, with particular attention to the behavior and dynamics of elect…
DiffOPF solves multi-valued OPF problems by sampling from system history.
Adaptive probabilistic load forecasting improves performance in power systems.
Enhances load forecasting for multiple entities with dynamic similarities.
We propose a new method to efficiently compute load-flows (the steady-state of the power-grid for given productions, consumptions and grid topology), substituting conventional simulators based on differential equation solvers. We use a deep feed-forward neural network trained with load-flows precomputed by simulation. …
Deep learning boosts building energy load forecasting.
Paper benchmarks and customizes energy forecasting methods.
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…
Model uses GAMs to forecast hourly electricity load weeks to one year ahead.
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…
An accurate load forecasting has always been one of the main indispensable parts in the operation and planning of power systems. Among different time horizons of forecasting, while short-term load forecasting (STLF) and long-term load forecasting (LTLF) have respectively got benefits of accurate predictors and probabil…
New principle reduces load imbalance in LLM serving systems, saving up to 52% energy.
Distributed, controllable energy storage devices offer several benefits to electric power system operation. Three such benefits include reducing peak load, providing standby power, and enhancing power quality. These benefits, however, are only realized during peak load or during an outage, events that are infrequent. T…
This paper uses a diffusion model to forecast electrical loads with uncertainty.
Non-intrusive load monitoring or energy disaggregation involves estimating the power consumption of individual appliances from measurements of the total power consumption of a home. Deep neural networks have been shown to be effective for energy disaggregation. In this work, we present a deep neural network architectur…
This two-part work puts forth the idea of engaging power electronics to probe an electric grid to infer non-metered loads. Probing can be accomplished by commanding inverters to perturb their power injections and record the induced voltage response. Once a probing setup is deemed topologically observable by the tests o…
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…
An increase in energy production from renewable energy sources is viewed as a crucial achievement in most industrialized countries. The higher variability of power production via renewables leads to a rise in ancillary service costs over the power system, in particular costs within the electricity balancing markets, ma…
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…
Model predicts EMF of Ni-Mn-Ga MSMA, improved with GRNN.
Winterization of Texas power system profitable but risky, estimated at $11.74bn over 30 years.
Study compares forecasting methods for distribution grid loads.
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 …
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…
Paper proposes combining GAM and DNN for accurate peak demand estimation from lower-resolution data.
Power load forecast with Machine Learning is a fairly mature application of artificial intelligence and it is indispensable in operation, control and planning. Data selection techniqies have been hardly used in this application. However, the use of such techniques could be beneficial provided the assumption that the da…
Active learning reduces smart meter data needs for better electric load predictions.
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.…
Paper proposes a novel optimization method for disaggregating smart meter data.
The recent research report of U.S. Department of Energy prompts us to re-examine the pricing theories applied in electricity market design. The theory of spot pricing is the basis of electricity market design in many countries, but it has two major drawbacks: one is that it is still based on the traditional hourly sche…
Recently there has been significant research on power generation, distribution and transmission efficiency especially in the case of renewable resources. The main objective is reduction of energy losses and this requires improvements on data acquisition and analysis. In this paper we address these concerns by using con…
Statistical depth metrics help identify risky power grid scenarios.
Paper develops a fast method to find near-optimal power solutions.
A new deep learning method for energy disaggregation.
Statistical learning improves reactive power control in distribution systems.
Paper examines vulnerabilities in data-driven pricing schemes.
We analyze the time series of the power loads of the 35 separated countries publicly sharing hourly data through ENTSO-E platform for more than 5 years. We apply the Multifractal Detrended Fluctuation Analysis for the demonstration of the multifractal nature, autocorrelation and the distribution function fundamentals. …
Model forecasts hourly electricity demand influenced by weather, socio-economic, and political factors.
Hybrid model combines LSTM and ETS for mid-term electric load forecasting.