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…
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48 results for “generalized power method”
A mechanism to derive multi-power law functions: an application in the econophysics frameworkphysics.data-an
It is generally recognized that economical systems, and more in general complex systems, are characterized by power law distributions. Sometime, these distributions show a changing of the slope in the tail so that, more appropriately, they show a multi-power law behavior. We present a method to derive analytically a tw…
We accelerate the power method for strong low-rank approximation using fast sketching.
problem Efficiency bottleneck in power method for large target ranks.
method Developed an algorithmic and theoretical framework for accelerating the power method using fast sketching.
result Simple and provably efficient methods for singular value decomposition, low-rank factorization, and Nyström approximation.
Paper uses tensor completion to estimate HVAC fan power baselines.
problem Estimating HVAC fan power without demand response.
method Tensor completion for multi-dimensional data analysis.
result Tensor completion outperforms existing baselining methods.
Optimizes power systems with energy storage under uncertainty using scenario-based method.
problem Optimizing power systems with energy storage, intermittent renewable generation, and uncontrollable loads under uncertainty.
method Developed a novel solution method based on scenario optimization and strategic sampling to solve the chance-constrained optimal power system operation problem.
result The strategic sampling method significantly improves computational efficiency and data-driven convex approximation of power flow.
Paper extends prediction-powered inference using conformal prediction for robust and valid imputation.
problem Safe use of black-box ML models for imputing missing data with strong guarantees.
method Connecting prediction-powered inference with conformal prediction for valid and additional guarantees.
result First general prediction-powered procedure for e-values operating off-line.
Paper proposes a new method for PCA using generative models.
problem Principal component analysis with generative modeling assumptions.
method Quadratic estimator and power method variant.
result Near-matching statistical rate of order .
Paper proposes a method for weather-informed probabilistic forecasting and scenario generation in power systems.
problem Challenges of integrating renewable energy sources into power grids due to their stochasticity and uncertainty.
method Combines probabilistic forecasting and Gaussian copula for day-ahead prediction and scenario generation of load, wind, and solar power.
result Demonstrates superior performance of the proposed weather-informed Temporal Fusion Transformer (WI-TFT) model.
This work develops a novel power control framework for energy-efficient power control in wireless networks. The proposed method is a new branch-and-bound procedure based on problem-specific bounds for energy-efficiency maximization that allow for faster convergence. This enables to find the global solution for all of t…
PhI-GPR improves power grid state estimation and forecasting.
problem Accurate state estimation and forecasting in power grids with sparse measurements.
method Physics-informed Gaussian process regression (PhI-GPR) for stochastic differential equations.
result PhI-GPR provides more accurate forecasts and estimates of power grid states compared to ARIMA.
Reshef & Reshef recently published a paper in which they present a method called the Maximal Information Coefficient (MIC) that can detect all forms of statistical dependence between pairs of variables as sample size goes to infinity. While this method has been praised by some, it has also been criticized for its lack …
Many problems in machine learning and statistics can be formulated as (generalized) eigenproblems. In terms of the associated optimization problem, computing linear eigenvectors amounts to finding critical points of a quadratic function subject to quadratic constraints. In this paper we show that a certain class of con…
Develops a more powerful selective inference method for stepwise feature selection.
problem Loss of power in existing conditional SI methods due to over-conditioning.
method Uses homotopy continuation approach to overcome over-conditioning.
result Shows improved power and efficiency in selective inference for feature selection.
The increasing penetration level of energy generation from renewable sources is demanding for more accurate and reliable forecasting tools to support classic power grid operations (e.g., unit commitment, electricity market clearing or maintenance planning). For this purpose, many physical models have been employed, and…
New method minimizes robust density power-based divergences for general parametric densities.
problem Computational complexity of minimizing DPD for general parametric densities.
method Stochastic approach to minimize DPD for general parametric density models.
result Proposed method can be applied to minimize other density power-based γ-divergences.
Study evaluates post-processing methods for improving solar power forecasts.
problem Improving accuracy of probabilistic solar energy forecasts through model chain approaches.
method Systematically evaluates different post-processing strategies for ensemble weather forecasts and direct solar power forecasting.
result Post-processing significantly improves solar power generation forecasts, especially when applied to power predictions.
In this paper, we present a method to determine the global horizontal irradiance (GHI) from the power measurements of one or more PV systems, located in the same neighborhood. The method is completely unsupervised and is based on a physical model of a PV plant. The precise assessment of solar irradiance is pivotal for …
Enhances power of covariance matrix tests for high-dimensional data.
problem Testing large covariance matrices in high-dimensional data.
method Proposes a new Fisher's combined probability test for quadratic form and maximum form statistics.
result Boosts power against more general alternatives.
Identifying a potentially large number of simultaneous line outages in power transmission networks in real time is a computationally hard problem. This is because the number of hypotheses grows exponentially with the network size. A new "Learning-to-Infer" method is developed for efficient inference of every line statu…
Study non-integer power-law potentials for Schrödinger operators using Lie-Rinehart algebras.
problem Analyzing Schrödinger operators with non-integer power-law potentials.
method Using Lie-Rinehart algebras and microlocal analysis.
result Microlocal analysis can be applied to Schrödinger operators with non-integer power-law potentials.
This paper deals with a multichannel audio source separation problem under underdetermined conditions. Multichannel Non-negative Matrix Factorization (MNMF) is one of powerful approaches, which adopts the NMF concept for source power spectrogram modeling. This concept is also employed in Independent Low-Rank Matrix Ana…
TEAM generates more powerful adversarial examples for DNNs.
problem Vulnerability of DNNs to imperceptible adversarial examples.
method TEAM uses Taylor expansion and Lagrangian multiplier method to craft adversarial examples.
result TEAM generates adversarial examples with 100% attack success rate using smaller perturbations.
New method proves topological Tverberg problem for all q, not just primes.
problem Establish sufficient conditions for simplicial complexes to map q points to the same point.
method General method that yields results beyond prime powers.
result Proves previously conjectured upper bounds for topological Tverberg problem for all q.
Study solves inverse problems for equations with fractional nonlinearities.
problem Solving inverse problems for semilinear elliptic equations with fractional power nonlinearities.
method Higher order linearization method adapted for fractional order.
result Results of previous studies remain valid for general power nonlinearities.
Deep learning models, especially CNNs, can predict radio frequency power faster than traditional methods.
problem Accurate radio frequency power prediction for optimal transmitter location.
method Empirical analysis of deep learning models including CNNs and UNET variations for power prediction.
result Deep learning models, particularly CNNs, are effective and generalize well to new regions for power prediction.
Improved wind speed forecasts for power generation using machine learning.
problem Improving the accuracy and reliability of wind speed predictions for power generation.
method A novel machine learning approach for calibrating wind speed ensemble forecasts.
result The proposed method improves the calibration and accuracy of probabilistic and point forecasts.
Signed graphs encode positive (attractive) and negative (repulsive) relations between nodes. We extend spectral clustering to signed graphs via the one-parameter family of Signed Power Mean Laplacians, defined as the matrix power mean of normalized standard and signless Laplacians of positive and negative edges. We pro…
Power plant is a complex and nonstationary system for which the traditional machine learning modeling approaches fall short of expectations. The ensemble-based online learning methods provide an effective way to continuously learn from the dynamic environment and autonomously update models to respond to environmental c…
Regression-based Inverter Control for Decentralized Optimal Power Flow and Voltage Regulationeess.SY
Electronic power inverters are capable of quickly delivering reactive power to maintain customer voltages within operating tolerances and to reduce system losses in distribution grids. This paper proposes a systematic and data-driven approach to determine reactive power inverter output as a function of local measuremen…
Paper refutes conjecture on tensor power iteration convergence in overcomplete models.
problem Understanding convergence of tensor power iteration in overcomplete random tensors.
method Analysis of tensor power iteration dynamics from random initialization.
result Polynomially many steps are necessary for convergence, refutes logarithmic conjecture.
Paper proposes a fast data-driven AC-OPF method using sparse hybrid Gaussian processes.
problem Optimizing electricity generation and delivery under generation uncertainty in modern power grids.
method Data-driven approach using sparse hybrid Gaussian processes to model power flow equations.
result Shows up to two times faster and more accurate solutions compared to state-of-the-art methods.
The paper compares DL models to WP curve modeling for forecasting with irregular shutdowns.
problem Forecasting wind power with irregular shutdowns due to redispatching.
method Compared autoregressive DL models to WP curve modeling.
result WP curve modeling achieves lower forecasting errors and is more computationally efficient.
Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challengeseess.SY
Survey of RL methods for optimizing power grid topologies.
problem Optimizing power grid operation with adaptive control strategies.
method Reinforcement Learning (RL) for dynamic and uncertain environments.
result Comprehensive evaluation of RL-based methods for power grid topology optimization.
Enhanced Gaussian process regression for multi-fidelity data fusion.
problem Combining data of varying fidelity levels for accurate predictions.
method Gradient-enhanced Cokriging method (GE-Cokriging) for QoI and its gradients.
result GE-Cokriging outperforms conventional multi-fidelity Cokriging in predicting QoI and gradients.
More Powerful Conditional Selective Inference for Generalized Lasso by Parametric Programmingstat.ML
Enhances selective inference for generalized lasso using parametric programming.
problem Low statistical power in selective inference for generalized lasso.
method Parametric programming to compute solution paths and identify model selection events.
result Improves selective inference power and practicality for various problems.
Develops European power option pricing under correlated interest rate and asset processes.
problem Pricing European power options under correlated interest rate and asset processes.
method Martingale method and Girsannov transform.
result Derives European power option pricing formulae under two market assumptions.
Paper compares ML models for fast power system contingency case identification.
problem Identifying critical power system states quickly.
method Comparison of regression and classification ML models.
result MLPs most suitable for fast contingency case prediction.
Extended CSGE improves power and cyclist movement forecasting.
problem Power and cyclist movement forecasting challenges.
method Extended Coopetitive Soft Gating Ensemble (XCSGE) with flexible weighting.
result Improves prediction performance by up to 30% for solar power forecasting.
Proposes a new method for predicting uncertain net electricity demand.
problem Uncertain net electricity demand and asymmetric cost structure.
method Bilevel program using clustering to tailor prescriptions.
result Substantial cost savings compared to standard methods.
New factorial power constants improve optimization convergence rates.
problem Optimization convergence rates depend on various constants.
method Proposes using factorial powers for defining these constants.
result Factorial powers simplify or improve convergence rates of optimization methods.
The Optimal Power Flow (OPF) problem is a fundamental building block for the optimization of electrical power systems. It is nonlinear and nonconvex and computes the generator setpoints for power and voltage, given a set of load demands. It is often needed to be solved repeatedly under various conditions, either in rea…
Paper proposes GPM for simultaneous community detection and group synchronization.
problem Simultaneous community detection and group synchronization in networks.
method Generalized Power Method (GPM) for non-convex optimization.
result GPM achieves exact recovery in time, outperforming SDP.
A robust model handles up to 25% of outliers in time-series data for power flow calculations.
problem Handling outliers in time-series data for accurate power flow calculations.
method Robust data-driven process model with Schweppe-type generalized maximum likelihood estimator and projection statistics for outlier weighting.
result The model can handle up to 25% of outliers in the training data set.
Bayesian method models multivalued power data from wind farms.
problem Accurate modeling of power curves with multivalued relationships.
method Overlapping mixture of probabilistic regression models.
result Model accurately represents practical power data.
BEAUTY Powered BEASTstat.ME
A new test method improves goodness-of-fit tests for copulas.
problem Developing robust tests for copula goodness-of-fit.
method Binary Expansion Approximation of UniformiTY (BEAUTY) and Binary Expansion Adaptive Symmetry Test (BEAST).
result The BEAST method improves empirical power against various alternatives.
Identifying the location of a disturbance and its magnitude is an important component for stable operation of power systems. We study the problem of localizing and estimating a disturbance in the interconnected power system. We take a model-free approach to this problem by using frequency data from generators. Specific…
AdaPT-GMM improves multiple testing power with covariates.
problem Powerful and robust multiple testing with covariates.
method Covariate-assisted Gaussian mixture model with adaptive thresholding.
result AdaPT-GMM delivers high power in various scenarios.
Looped transformers with LN converge to power method for principal component prediction.
problem Understanding how transformers learn algorithmic procedures.
method Study of principal component prediction with looped linear transformers and layer normalization.
result Gradient descent trains looped transformers with LN to implement the power method for principal component prediction.