This paper proposes a submodular load clustering method for transmission-level load areas.
problem Traditional load analysis challenges with new electricity usage patterns.
method Robust Principal Component Analysis (R-PCA) and submodular cluster center selection.
result The proposed method efficiently clusters load areas and demonstrates effectiveness in PJM load data.
This paper analyzes load predictability at different aggregation levels and improves forecasting accuracy.
problem Challenges in short-term load forecasting, especially at low aggregation levels.
method Characterized SME and residential loads, quantified predictability using approximate entropy, compared various STLF techniques.
result Improved forecasting accuracy for low-aggregation loads, validated with data processing techniques.
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…
Study finds cherry-picking load shaping strategies outperforms others in reducing grid CO2 emissions.
problem Lack of detailed counterfactual data makes it hard to assess load shaping strategies' effectiveness.
method Calibrated granular ERCOT simulations for counterfactual analysis of load shaping strategies.
result LMP-based load shaping outperforms other strategies in reducing grid CO2 emissions.
Quantum algorithm speeds up pricing of financial derivatives.
problem Pricing autocallable options efficiently.
method Integration-based exponential amplitude loading technique.
result 50x reduction in circuit depth for payoff component.
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…
Machine learning predicts exercise load from heart rate data post-exercise.
problem Monitoring energy expenditure in real life.
method Machine learning methods (linear regression, etc.) applied to heart rate data.
result Random forest and k-nearest neighbors classifiers predict load levels accurately.
Model uses GAMs to forecast hourly electricity load weeks to one year ahead.
problem Accurate mid-term hourly load forecasting for power plant operation and energy management.
method Generalized Additive Models (GAMs) with P-splines and autoregressive post-processing.
result Significantly enhanced forecasting accuracy compared to state-of-the-art methods.
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. …
A new method uses Gaussian Processes to solve power flow problems with uncertain renewable and load inputs.
problem Solving power flow problems with uncertain renewable and load inputs.
method Non-parametric Bayesian inference-based uncertainty propagation using Gaussian Processes.
result The method provides reasonably accurate solutions with fewer samples and time compared to Monte-Carlo simulations.
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…
Optimal tensor PCA for estimating factors and loadings in high-dimensional panel data.
problem Estimating factors and loadings in high-dimensional panel data with non-negligible correlations.
method Tensor Principal Component Analysis (TPCA) for estimating factors and loadings in a tensor factor model.
result Simple TPCA is optimal for strong factors and can be improved for weak factors with alternating least-squares iterations.
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…
Proposes a machine learning framework for more efficient economic dispatch.
problem Temporal and spatial correlations between system cost and load prediction errors.
method End-to-end machine learning approach with task-specific learning criteria and an efficient optimization kernel.
result Demonstrates the effectiveness and efficiency of the proposed learning framework.
Method identifies dual cognitive system interactions using eye-tracking data.
problem Quantify interactions between System 1 and System 2 cognitive processes.
method Modified Stroop test, machine learning, eye-tracking data.
result Machine learning can distinguish between different tasks and characterize attentional load.
Accelerates data loading in deep neural network training by 30x.
problem Data loading is a bottleneck in deep neural network training.
method Locality-aware data loading method using software caches.
result More than 30x speedup in data loading.
Model forecasts hourly electricity demand influenced by weather, socio-economic, and political factors.
problem Accurate hourly electricity demand forecasting in the face of multifaceted uncertainties.
method Interpretable probabilistic mid-term forecasting model using Generalized Additive Models (GAMs).
result Highlights vulnerability of countries to extreme weather scenarios under electric heating adoption.
Enhances load forecasting for multiple entities with dynamic similarities.
problem Inaccurate probabilistic load predictions due to uncertainties and dynamic changes.
method Online multi-task learning for probabilistic load forecasting.
result Significantly enhances load forecasting accuracy across various scenarios.
In this paper, we provide explicit formulas, in terms of the covariances of sample covariances or sample correlations, for the asymptotic covariances of unrotated factor loading estimates and unique variance estimates. These estimates are extracted from least square, principal, iterative principal component, alpha or i…
New method relaxes PCA orthogonality constraints using explained variance of correlated components.
problem Difficulty in using PCA for sparse design due to orthogonality constraints and non-differentiable penalty.
method Introduce expvar(Y) to measure variance explained by correlated components, relax orthogonality constraints.
result Two expvar(Y) definitions suitable for block PCA formulations without orthogonality constraints.
New methods improve electricity load forecasting using hierarchical transfer learning.
problem Improving electricity load forecasts at national scale using smart meter data.
method Developed two hierarchical transfer learning methods based on stacking and aggregation of experts.
result Significant improvement in predictions compared to benchmark algorithms.
Paper examines vulnerabilities in data-driven pricing schemes.
problem Vulnerability of clustering-oriented pricing schemes to malicious user behavior.
method Defined a notion of disguising to identify strategic behaviors of malicious users, characterized sensitivity zones to evaluate malicious user percentages, conducted cost benefit analysis.
result Concluded with a vulnerability analysis of data-driven pricing schemes.
New FGSPCA method captures grouping and sparse structures in PCA without prior info.
problem Capture grouping and sparse structures in PCA without prior info.
method Truncated regularization with alternating algorithm.
result FGSPCA method reduces model complexity and increases interpretability.
Paper proposes a new method for hourly load forecasting using smart meter data.
problem Challenges in short-term load forecasting at fine granularity.
method Forecasting using Matrix Factorization (fmf) for hourly load forecasting.
result Significantly outperforms state-of-the-art methods in load forecasting.
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…
Simple 1D-CNN network predicts electricity loads 36 hours ahead.
problem Forecasting electricity loads for future time periods.
method Used a one-dimensional CNN with parameter scanning to optimize kernel size, filters, and dense size.
result Good forecast quality achieved with basic CNN architectures.
A new framework uses DDQN to simplify WECC CLM for efficient load modeling.
problem Complexity and high parameter count in WECC CLM.
method Two-stage approach with DDQN for load composition and parameter selection.
result The framework efficiently approximates WECC CLM transient responses.
The paper finds stocks with higher dynamic network risk have lower returns.
problem Understanding and pricing short-term and long-term dynamic network risk in stock returns.
method Examined the relationship between stock sensitivities to dynamic network risk and expected returns, using economic theory and empirical analysis.
result A one-standard deviation increase in long-term network risk loadings associates with a 7.66% drop in annualized expected returns.
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…
Paper uses econometrics time series model with T-student Distribution for short-term load forecasting.
problem Accurate short-term load forecasting for optimizing electrical sources and protecting energy.
method Uses SARIMA-GARCH model with T-student Distribution to forecast electric load.
result The proposed model outperforms the ARIMA model with Normal Distribution.
Deep learning boosts building energy load forecasting.
problem Short-term load forecasting in buildings.
method Stacked Boosters Network architecture with sparse interactions, parameter sharing, and equivariant representations.
result Outperforms state-of-the-art models in short-term load forecasting tasks.
New method infers viral load from pooled tests.
problem Inefficient viral load inference in pooled testing.
method Message passing algorithm with PCR noise function.
result Accurate viral load inference possible.
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…
This paper uses a diffusion model to forecast electrical loads with uncertainty.
problem Uncertainties in electrical load forecasting due to renewable energy and external events.
method Diffusion-based Seq2Seq structure for epistemic uncertainty and robust additive Cauchy distribution for aleatoric uncertainty.
result Ability to separate and quantify both types of uncertainties in load forecasting.
CP-factorization for high-dimensional tensor time series and double projection iterations
problem Identifying and estimating factor loadings in CP decomposition for high-dimensional tensor time series
method One-pass estimation procedure using standard eigen-analysis for matrix constructed based on serial dependence
result Asymptotic properties established under general settings, adapt to sparsity, accommodates weak factors
Paper presents a method for probabilistic load forecasting using adaptive online learning.
problem Inability to assess intrinsic uncertainties and capture dynamic changes in consumption patterns.
method Adaptive online learning of hidden Markov models for recursive parameter updates and sequential prediction.
result Significant improvement in performance compared to existing techniques across various scenarios.
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. …
New method predicts heat load in thermal grids using latent variables.
problem Predicting heat load in district energy systems.
method Combines nominal model for outdoor temperature with latent variable model for residual heat load.
result Proposed method achieves better prediction accuracy than artificial neural networks.
Sparse GCA finds linear relationships in multiple datasets, using gradient descent.
problem Finding linear relationships across multiple datasets with sparse loading vectors.
method Formulated as generalized eigenvalue problems, used a thresholded gradient descent algorithm.
result Proposed algorithm yields tight estimation error bounds and demonstrates effectiveness on synthetic datasets.
Paper proposes dense average network for improved power load forecasting.
problem Improving power load forecasting accuracy to save millions for the power industry.
method Introduces dense average connection and constructs dense average network for power load forecasting.
result Proposed model outperforms existing methods on public datasets.
We present a new model for the electricity spot price dynamics, which is able to capture seasonality, low-frequency dynamics and the extreme spikes in the market. Instead of the usual purely deterministic trend we introduce a non-stationary independent increments process for the low-frequency dynamics, and model the la…
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…
Paper proposes a framework for probabilistic load forecasting by integrating point forecasts.
problem Short-term load forecasting for power systems energy management.
method Two-stage framework: first stage for point forecasting, second stage for probabilistic forecasting using feature integration.
result Numerical results show effectiveness of the proposed approach in hour-ahead load forecasting.
A new method calculates risk loadings in classification ratemaking without subjective parameters.
problem Subjective risk loading parameters in classification ratemaking.
method Bootstrap method to calculate total risk premium, then determine risk loading parameters using quantile regression models.
result Risk premiums calculated by the new method reasonably differentiate different risk classes.
Deep learning improves weather modeling for electricity load forecasting.
problem Accurate load and renewable energy forecasting requires complex spatio-temporal weather modeling.
method Automated spatio-temporal feature extraction using deep neural networks.
result Deep learning outperforms traditional methods in French national load forecasting.
This paper monetizes customer load data to boost energy retailer profits.
problem Improving load forecasts to reduce energy imbalance costs.
method Cooperative game theory approach to quantify and distribute profits.
result Retailer gains significant profit from customer load data.
A method for selecting data for short-term load forecasting using Bayesian probabilistic models.
problem Lack of effective data selection techniques in power load forecasting.
method A fully automatic methodology based on a full Bayesian probabilistic model.
result The method improves the performance of load forecast models using real data.
Proposes a DRL-based MLB for UDNs to balance large-scale traffic.
problem Large-scale load balancing in ultra-dense networks (UDNs).
method Two-layer architecture with DRL for intra-cluster load balancing.
result Empirical results show superior load balancing performance.