Improves distributed SGD convergence speed with reduced computation load.
problem Mitigating stragglers in distributed SGD to speed up convergence.
method Modeling communication and computation times, adapting number of workers and computation load dynamically.
result Significantly reduces computation load while improving convergence speed.
A new hybrid-ordered SGD method reduces communication and complexity for non-convex optimization.
problem Balancing communication, computational complexity, and convergence rate in distributed non-convex optimization.
method Hybrid-ordered distributed SGD with pre-shared scalers and periodic vector communication.
result Order-wise faster convergence compared to existing methods.
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.
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 reviews low voltage load forecasting methods and applications.
problem Reliable forecasting for low voltage networks is needed for decarbonization.
method Comprehensive survey of current approaches, challenges, and trends.
result Established an open list of low voltage datasets for further research.
Deep learning models improve electric load forecasting accuracy.
problem Accurate short-term electric load forecasting remains challenging.
method Comprehensive evaluation of various deep learning architectures on real-world datasets.
result Deep learning models outperform traditional methods in electric load forecasting.
Asynchronous decentralized SGD with quantized and local updates converges in gossip model.
problem Scalable distributed machine learning challenges in decentralized optimization.
method Asynchronous decentralized SGD with quantization and local steps in gossip model.
result Decentralized optimization with quantization and local steps converges in asynchronous gossip model.
A scalable distributed S-LSR1 algorithm reduces communication costs.
problem Efficiently scaling S-LSR1 for large-scale distributed optimization.
method Proposes DS-LSR1, a communication-efficient variant of S-LSR1.
result DS-LSR1 scales well in problem dimension and data points.
BICompFL tackles bi-directional compression challenges in stochastic FL, reducing communication costs by an order of magnitude.
problem Communication bottleneck in federated learning, especially with stochastic updates.
method Introduces BICompFL, a bi-directional compression approach for stochastic federated learning.
result Significantly reduces communication costs (by an order of magnitude) while maintaining accuracy.
Paper speeds up DGD by allowing multiple transmissions from non-straggling servers.
problem Limited parallel execution due to straggling servers in DGD.
method Allowing multiple transmissions from each CS at each iteration.
result Significant reduction in average completion time per iteration.
In this paper, we show synchronization for a group of output passive agents that communicate with each other according to an underlying communication graph to achieve a common goal. We propose a distributed event-triggered control framework that will guarantee synchronization and considerably decrease the required comm…
DSVGD improves federated learning with fewer communication rounds.
problem Federated learning scalability and trustworthiness.
method Distributed Stein Variational Gradient Descent (DSVGD) for non-parametric Bayesian inference.
result DSVGD achieves comparable accuracy and scalability to other methods, with well-calibrated predictions.
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 develops coding techniques to reduce the running time of distributed learning tasks. It characterizes the fundamental tradeoff to compute gradients (and more generally vector summations) in terms of three parameters: computation load, straggler tolerance and communication cost. It further gives an explicit c…
Paper introduces uncertainty injection for deep learning robust optimization.
problem Uncertainty in input data affects deep learning model performance in optimization problems.
method Uncertainty injection scheme for training deep learning models to produce robust solutions.
result Proposed scheme improves robustness of solutions in wireless communications applications.
COKE reduces communication in decentralized kernel learning.
problem Decentralized kernel learning challenges due to data-dependent decision variables.
method Random feature approximation and ADMM for iterative optimization; communication-censored algorithm.
result COKE achieves faster convergence and reduced communication load.
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.
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.
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…
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…
This paper proposes a new D2D data sharing approach to improve distributed machine learning training speed.
problem Straggler dilemma in distributed edge learning.
method Proposes a D2D data sharing approach to balance computation loads and optimize radio resource allocation.
result Significantly reduces training delay and enhances training accuracy in non-i.i.d. data environments.
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.
We propose a decentralized Maximum Likelihood solution for estimating the stochastic renewable power generation and demand in single bus Direct Current (DC) MicroGrids (MGs), with high penetration of droop controlled power electronic converters. The solution relies on the fact that the primary control parameters are se…
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.
Joint sensing and communication network improves target localization and reduces communication load.
problem Efficiently localize multiple targets with reduced communication overhead.
method Multi-base station cooperative sensing with AI-aided clustering and tracking.
result Optimal sub-pattern assignment (OSPA) error less than 60 cm with reduced communication capacity.
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.
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.
Paper proposes federated learning for SNNs to enable low-power, online training.
problem Limited data at each device for on-device SNN training.
method Federated Learning (FL) for cooperative SNN training, leveraging local and global feedback.
result FL-SNN achieves significant advantages over separate training and offers a flexible trade-off between accuracy and communication load.
A new privacy-preserving ML framework using random sampling.
problem Achieving differential privacy in client-distributed ML.
method Draw and Discard: random model sampling for scalability and privacy.
result Demonstrated improved model quality and privacy guarantees.
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.
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.
Hedge improves decision-making in resource selection problems.
problem Resource selection and distribution problems in networks and transportation.
method Hedge as an adaptive scheme with upper bounded total loss.
result Worst performance of Hedge studied under bounded loss.
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…
Connectivity estimation is challenging in the context of high-dimensional data. A useful preprocessing step is to group variables into clusters, however, it is not always clear how to do so from the perspective of connectivity estimation. Another practical challenge is that we may have data from multiple related classe…
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.
CROCS clusters consumer behaviour from smart meters, capturing variability and robustness.
problem Insufficient consumer segmentation in existing clustering methods.
method Two-stage clustering framework: first stage clusters daily load profiles, second stage uses WSMD for set-to-set comparison.
result CROCS captures intra-consumer variability and robustness to anomalies and missing data.
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.
Methods estimate physical load and fatigue from wearable data.
problem Estimating physical load and fatigue from wearable data.
method Statistical and machine learning methods based on kurtosis-skewness diagram.
result Different levels of physical activities and fitness can be distinguished from the kurtosis-skewness diagram.
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.
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.
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.