New LSTM model predicts disaggregated electricity loads accurately.
problem Forecasting disaggregated electricity loads from smart meters.
method Single complex LSTM model capturing individual consumption patterns.
result Model accurately predicts future loads of new consumers.
The paper tackles energy disaggregation by improving dictionary learning with deep neural models.
problem Decomposing electricity signals of a whole home into its operating devices.
method Proposes a novel optimization program that learns both the dictionary and sparse coefficients using a deep neural model (LSTM-AE) to capture temporal energy signals.
result Significant improvement in disaggregation accuracy and F-score metrics compared to state-of-the-art methods.
Convolutional model disaggregates electricity consumption data.
problem Disaggregating aggregate electricity consumption data into individual appliance usage.
method Gated linear unit convolutional layers and residual blocks refine neural network output. Partially overlapped sequences are averaged for final output.
result The proposed model outperforms existing models in disaggregating various appliance usage.
Paper proposes a neural network for disaggregating appliance-level energy consumption.
problem Estimating appliance-level electricity consumption from a single meter.
method Adapts a neural network to classify operational state changes of appliances.
result Competitive performance compared to existing methods in simulated experiments.
New method for disaggregate electricity demand forecasting at household level.
problem Challenges in forecasting electricity demand at individual household level.
method Additive stacking method for probabilistic disaggregate electricity demand forecasting.
result Improved accuracy in disaggregate electricity demand forecasting.
This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dynamic filtering, the STPN framework is used to capture not only the individual system characteristics but also the pair-wise causal dependen…
Deep network improves NILM by distinguishing on/off states of appliances.
problem Break down household aggregate electricity consumption into individual appliance usages.
method Subtask gated network combining regression and classification subtasks.
result Surpasses state-of-the-art performance for most benchmark cases.
Though distribution system operators have been adding more sensors to their networks, they still often lack an accurate real-time picture of the behavior of distributed energy resources such as demand responsive electric loads and residential solar generation. Such information could improve system reliability, economic…
Improved non-intrusive load monitoring with a novel neural network.
problem Accurately disaggregating household electricity consumption without dedicated meters.
method Developed a scale- and context-aware network with multi-scale features and contextual information.
result Significantly improved accuracy compared to state-of-the-art methods.
The paper evaluates various forecasting methods for inflation, finding ML models superior.
problem Forecasting inflation using disaggregated data and machine learning.
method Examines traditional and machine learning models, including random forest, for disaggregated and aggregated inflation forecasts.
result Aggregating disaggregated forecasts performs similarly to survey-based expectations and aggregate models.
Efficient neural network improves disaggregation of home energy usage.
problem Estimating power consumption of individual appliances from total home power.
method Fully convolutional neural network architecture with improved computational efficiency.
result Achieves state-of-the-art disaggregation performance with reduced training and prediction times.
New framework for interpreting disaggregated fairness evaluations using causal models.
problem Misinterpretation of disaggregated fairness evaluations due to data representativeness and selection bias.
method Causal graphical models to characterize fairness properties and metric stability under different data generating processes.
result Disaggregated evaluations are unreliable without explicit assumptions regarding bias mechanisms.
SureMap estimates model performance across subpopulations efficiently.
problem Estimating model performance across subpopulations with scarce data.
method Simultaneous Gaussian mean estimation with external data.
result High accuracy in both multi-task and single-task disaggregated evaluations.
Enhances water disaggregation for parallel appliances using shape features and Bayesian Discriminative Sparse Coding.
problem Accurately discriminate and disaggregate water consumption patterns from parallel appliances.
method Bayesian Discriminative Sparse Coding (BDSC-LP) with Laplace Prior, shape features, Gibbs sampling.
result Extensive experiments validate the effectiveness of the proposed model.
Paper proposes a novel optimization method for disaggregating smart meter data.
problem Energy disaggregation, inferring appliance-specific energy consumption from aggregate meter data.
method Two-stage optimization approach: first phase uses mixed integer programming, second phase binary quadratic optimization with penalty terms and appliance constraints.
result Proposed method successfully reconstructs appliance signatures, overcoming previous optimization-based methods' limitations.
In many statistical problems, a more coarse-grained model may be suitable for population-level behaviour, whereas a more detailed model is appropriate for accurate modelling of individual behaviour. This raises the question of how to integrate both types of models. Methods such as posterior regularization follow the id…
We propose a clustering-based iterative algorithm to solve certain optimization problems in machine learning, where we start the algorithm by aggregating the original data, solving the problem on aggregated data, and then in subsequent steps gradually disaggregate the aggregated data. We apply the algorithm to common m…
Energy is a limited resource which has to be managed wisely, taking into account both supply-demand matching and capacity constraints in the distribution grid. One aspect of the smart energy management at the building level is given by the problem of real-time detection of flexible demand available. In this paper we pr…
Deep neural network improves NILM with attention mechanism.
problem Energy disaggregation of individual appliance power demands from aggregate meter readings.
method Regression and classification subnetworks with attention mechanism.
result Proposed model outperforms state-of-the-art on REDD and UK-DALE datasets.
tempdisagg transforms low-frequency data into high-frequency estimates.
problem Transforming low-frequency data into high-frequency estimates.
method Uses econometric techniques including Chow-Lin, Denton, Litterman, Fernandez, and uniform interpolation.
result Transforms low-frequency aggregates into consistent, high-frequency estimates.
Paper proposes an unsupervised NILM framework using GLDA for diverse utility data.
problem Extract appliance components from aggregate energy signals without labeled data.
method Bayesian hierarchical mixture models, Gaussian Latent Dirichlet Allocation (GLDA), online processing.
result Algorithm finds useful consumption patterns from mixed utility data.
New algorithm extracts device profiles for short-term power predictions in commercial buildings.
problem Short-term power prediction in commercial buildings with high accuracy.
method Unsupervised extraction of device profiles from aggregate power measurements, disaggregation using particle swarm optimization, and state changes forecast by artificial neural networks.
result Developed approach outperforms existing methods with high accuracy.
PREMA recovers detailed data from aggregated views.
problem Reconstructing detailed data from aggregated views.
method Low-rank tensor factorization.
result Recovery guarantees under certain conditions.
We propose a new framework for single-channel source separation that lies between the fully supervised and unsupervised setting. Instead of supervision, we provide input features for each source signal and use convex methods to estimate the correlations between these features and the unobserved signal decomposition. We…
Review and compare sorting model selection methods for preference disaggregation.
problem Selecting a representative sorting model from multiple compatible models.
method Reviewed and proposed new procedures for selecting sorting models, including robust assignment rule.
result Identified most efficient procedures in terms of classification accuracy and robustness.
Develops algorithm to reduce real-world inequality.
problem Reduces inequality in real-world disparities.
method Impact remediation framework using social science insights and constrained optimization.
result Optimal intervention policies discovered to improve equity.
Simpler model outperforms state-of-the-art for disaggregating census data.
problem Disaggregating detailed census data into finer-grained, high-resolution mappings.
method Aggregate learning approach using ancillary data for an interpretable model.
result Simple model outperforms state-of-the-art on disaggregation metrics.
A new deep learning method for energy disaggregation.
problem Energy disaggregation or non-intrusive load monitoring (NILM) to identify individual appliance power usage.
method Sequence to Point Learning based on Bidirectional Dilated Residual Network (BRDN).
result Our method outperforms state-of-the-art approaches in all appliances on REDD and UK-DALE datasets.
A new method improves AI fairness assessment by estimating performance across intersectional subgroups.
problem Limited evaluation of AI systems across intersectional subgroups due to small sample sizes.
method Structured regression approach to disaggregated evaluation.
result Our method yields more accurate performance estimates, especially for small subgroups.
Novel approach uses Gaussian processes to estimate conflict trends.
problem Estimating temporal and spatial patterns of violent conflict.
method Highly disaggregated conflict event data with Gaussian processes.
result Powerful conflict forecasts and insights into conflict dynamics.
Study electric field and potential of torus knots, focusing on z-axis.
problem Analyze electric field and potential of torus knots.
method Parametrize torus knots, use symmetry, numerical methods, contour integration.
result Electric field is zero only at the origin, extreme points analyzed.
Model predicts climate-sensitive water and electricity use in Midwestern cities.
problem Ensuring conservation measures in growing cities under climate change.
method Statistical learning theory-based modeling framework for predicting climate-sensitive water-electricity demand nexus.
result Water use is slightly more sensitive to climate than electricity use.
Deep neural networks predict electricity consumption accurately.
problem Predicting future electricity consumption for better management.
method Used Recurrent Neural Networks (RNN) and Long Short Term Memory (LSTM) networks to predict electricity consumption based on past data.
result Both RNN and LSTM achieved an average Root Mean Square error of 0.1.
This thesis tackles NILM challenges with a new dataset and efficient edge deployment techniques.
problem Limited datasets and high computational power for NILM deployment.
method Developed an interoperable data collection framework and introduced model compression techniques.
result Efficient edge deployment of NILM models for global scalability and sustainability.
In this paper, we formulate a method for minimising the expectation value of the procurement cost of electricity in two popular spot markets: {\it day-ahead} and {\it intra-day}, under the assumption that expectation value of unit prices and the distributions of prediction errors for the electricity demand traded in tw…
New method tracks electricity flows to measure carbon emissions in real-time.
problem Accurate measurement of carbon emissions from electricity consumption.
method Real-time consumption-based accounting using flow tracing.
result Substantial differences found between production and consumption intensities.
Study shows Bitcoin mining with surplus electricity can boost KEPCO's financial stability.
problem Improving energy resource efficiency and reducing KEPCO's debt.
method Utilized surplus electricity for Bitcoin mining using Antminer S21 XP Hyd, analyzed with Random Forest Regressor and Long Short-Term Memory models.
result Bitcoin mining with surplus electricity generates economic revenue, minimizes energy loss, and resolves payment issues for KEPCO.
Paper provides a method to price electricity storage contracts using COS technique.
problem Valuation of electricity storage contracts considering physical and operational constraints.
method Uses Fourier-based COS method to price contracts based on stochastic polynomial process.
result The COS method accurately and efficiently prices electricity storage contracts.
PSQRNN model forecasts electricity consumption in China by integrating neural networks and quantile regression.
problem Electricity forecasting in China due to regional economic, social, and natural conditions.
method PSQRNN combines neural networks and semiparametric quantile regression to model electricity consumption.
result PSQRNN model outperforms traditional methods in forecasting electricity consumption in China.
Algorithm optimizes electricity procurement costs by 1.65%.
problem Minimizing energy cost while covering forecast consumption.
method Deep learning forecasting and deviation indicator.
result Reduction of 1.65% in costs compared to uniform policy.
The S&P500 daily values and log-returns fail to conform to Benford's laws, revealing underlying trends.
problem Testing financial data for conformity to Benford's laws.
method Analyzed S&P500 daily closing values and log-returns over 16,265 days, disaggregating at five levels.
result S&P500 daily values show a huge lack of conformity to Benford's laws, with missing first and first two digits.
In this paper, we analyze Nash equilibria between electricity producers selling their production on an electricity market and buying CO2 emission allowances on an auction carbon market. The producers' strategies integrate the coupling of the two markets via the cost functions of the electricity production. We set out a…
In this note, we present an existence result of a Nash equilibrium between electricity producers selling their production on an electricity market and buying CO2 emission allowances on an auction carbon market. The producers' strategies integrate the coupling of the two markets via the cost functions of the electricity…
Proposes a pricing agent using reinforcement learning to balance renewable energy demand.
problem Intermittent renewable energy sources challenge carbon-free electricity generation.
method Reinforcement learning approach to balance customer demand with renewable energy generation.
result Demonstrates improved electricity pricing strategy for renewable energy integration.
Paper models and forecasts intra-day electricity price spreads.
problem Forecasting intra-day price spreads for electricity traders and operators.
method Dynamic density functions based on skewed-t distributions, conditional on exogenous drivers.
result Best fitting and forecasting specifications selected using Pinball Loss function.
Novel approach integrates Multivariate Square-root Lasso into Synthetic Control for high-dimensional data.
problem Challenges in practical implementation and computational efficiency of Synthetic Control method for high-dimensional disaggregated data.
method Integrates Multivariate Square-root Lasso into Synthetic Control framework.
result Demonstrates superior computational efficiency without compromising estimation accuracy.
Electricity is bought and sold in wholesale markets at prices that fluctuate significantly. Short-term forecasting of electricity prices is an important endeavor because it helps electric utilities control risk and because it influences competitive strategy for generators. As the "smart grid" grows, short-term price fo…
Study predicts electricity prices using LSTM models with feature selection, considering market coupling.
problem Accurate day-ahead electricity price forecasting in coupled markets.
method Hybrid LSTM-based deep learning models with feature selection algorithms.
result Proposed models achieve considerably accurate results in Nordic market.