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.
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…
Traditional load analysis is facing challenges with the new electricity usage patterns due to demand response as well as increasing deployment of distributed generations, including photovoltaics (PV), electric vehicles (EV), and energy storage systems (ESS). At the transmission system, despite of irregular load behavio…
Paper optimizes demand aggregation for low-level electricity markets.
problem Accurate short-term load forecasting at low aggregation levels for market participants.
method Probabilistic portfolio optimization of residential households' demand using ARMA-GARCH models or KDE forecasts.
result Seasonal Residual approach outperforms others in accuracy and efficiency.
Frugal method predicts multiple local electricity loads efficiently.
problem Day-ahead forecasting of over 1000 substations in France.
method Adaptive generalized additive models with state-space representations, combined with transfer learning.
result Reduction of computational needs and emissions with competitive accuracy.
We examine a network of learners which address the same classification task but must learn from different data sets. The learners cannot share data but instead share their models. Models are shared only one time so as to preserve the network load. We introduce DELCO (standing for Decentralized Ensemble Learning with CO…
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.
Optimizes insurance pricing to minimize ruin probability under various claim dependencies.
problem Determining optimal insurance premiums in the presence of dependencies between claim occurrences.
method Analyzes both independent and dependent claim processes, considering single and multiple risks.
result Optimal insurance premiums depend on initial reserve and claim dependencies.
Study compares forecasting methods for distribution grid loads.
problem Improving load prediction accuracy in distribution grids.
method Evaluation of probabilistic forecasting techniques on distribution grid load prediction.
result Hierarchical techniques enhance bottom-level forecast accuracy.
MELO predicts electricity loads by adapting to shifts without external indicators.
problem Adapting to non-stationary prediction challenges in online settings.
method MELO combines multiple forgetting factors and aggregation rules to adaptively predict.
result MELO reduces RMSE by 34.7% compared to base predictors and external covariates.
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.
Adaptive probabilistic load forecasting improves performance in power systems.
problem Complexity of electricity load forecasting due to changing drivers and local generation.
method Adaptive probabilistic approach using Kalman filter and online gradient descent.
result Adaptive probabilistic forecasts improve performance in both point and probabilistic forecasting.
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.…
Proposes ML-RBM for non-intrusive load monitoring without appliance-level data.
problem Non-intrusive load monitoring without appliance-level data.
method Multi-label Restricted Boltzmann Machine (ML-RBM)
result Experimental evaluation of proposed and state-of-the-art techniques.
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…
A convolutional sequence to sequence non-intrusive load monitoring model is proposed in this paper. Gated linear unit convolutional layers are used to extract information from the sequences of aggregate electricity consumption. Residual blocks are also introduced to refine the output of the neural network. The partiall…
Method learns CTMC models from steady-state data, predicting unseen states.
problem Learning CTMC models from aggregate steady-state statistics without sequence examples.
method ∞-SGD, a stochastic gradient descent method that avoids infinite sums.
result Successfully learns CTMC models and predicts unseen states.
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 assesses the market value of sharing privacy-protected smart meter data.
problem Value of sharing privacy-protected smart meter data between consumers and load serving entities.
method Discounted differential privacy model, ANN-based load forecasting, optimal procurement problem.
result Significant value in sharing smart meter data while retaining individual consumer privacy.
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.
Truckload brokerages, a $100 billion/year industry in the U.S., plays the critical role of matching shippers with carriers, often to move loads several days into the future. Brokerages not only have to find companies that will agree to move a load, the brokerage often has to find a price that both the shipper and carri…
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.
New method forecasts time series with changing variances.
problem Real-world processes with changing variances cannot be captured by classical models.
method State-space model with Markov switching variances, using online learning and expert aggregation.
result Proposed method outperforms traditional expert aggregation and is robust to misspecification.
Adaptive models improve electricity demand forecasting during lockdown.
problem Poor load forecasting due to sudden consumption changes during lockdown.
method Adaptive generalized additive models with Kalman filters and expert aggregation.
result Significant reduction in prediction errors compared to traditional models.
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.
A distributed framework for reducing high-dimensional matrix-variate time series data.
problem Reducing dimensionality of high-dimensional, heterogeneous matrix-variate time series data.
method Data partitioning, distributed two-dimensional tensor PCA, aggregation, final PCA, factor matrix computation.
result Preserves latent matrix structure, improves computational efficiency and information utilization.
A winning method for day-ahead electricity demand forecasting during and after the COVID-19 pandemic.
problem Day-ahead electricity demand forecasting during and after the COVID-19 pandemic.
method Online forecast combination of multiple point prediction models with a holiday adjustment procedure and smoothed Bernstein Online Aggregation (BOA).
result Excellent forecasting performance, particularly due to the holiday adjustment procedure and fully adaptive smoothed BOA approach.
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.
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.
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.
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…
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.
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.
Non-intrusive load monitoring (NILM), also known as energy disaggregation, is a blind source separation problem where a household's aggregate electricity consumption is broken down into electricity usages of individual appliances. In this way, the cost and trouble of installing many measurement devices over numerous ho…
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.
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.
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.
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.