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.
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.
New pricing model identifies and values different generator attributes.
problem Traditional hourly scheduling ignores time continuity and inter-temporal constraints.
method Continuous time commodity model with spot pricing and load duration models.
result Load duration pricing reduces total electricity purchasing cost and distributes profits more equitably.
Active learning reduces smart meter data needs for better electric load predictions.
problem Inaccurate and costly electric load predictions due to insufficient data.
method Active learning to collect more informative data subsets.
result Electric load predictions can be made with about half the data using active learning.
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.
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.
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…
Study examines multifractality in European power loads over 5 years.
problem Understanding multifractality in European power load time series.
method Applied Multifractal Detrended Fluctuation Analysis (MF-DFA) with improved methodology.
result European power loads exhibit multifractality in both distribution and autocorrelation functions.
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.
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 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.
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.
Hybrid model combines LSTM and ETS for mid-term electric load forecasting.
problem Mid-term electric load forecasting accuracy.
method Combines LSTM, ETS, and ensemble learning; uses dilated LSTM for long-term relationships.
result High performance and competitiveness compared to classical and machine learning models.
The increasing importance of renewable energy, especially solar and wind power, has led to new forces in the formation of electricity prices. Hence, this paper introduces an econometric model for the hourly time series of electricity prices of the European Power Exchange (EPEX) which incorporates specific features like…
A model shows faster energy transition rewards flexible production more quickly.
problem Energy transition discourages investments in flexible production.
method Modeling future electricity prices from residual load, investigating revenues for various flexibility levels.
result Faster energy transition rewards flexible production more quickly.
Study shows increased renewables lead to more extreme price spikes in balancing markets.
problem Higher variability of renewable energy leads to increased ancillary service costs, especially extreme price spikes.
method Agent-based modeling to simulate fluctuations in load and renewable energy production, forecasting resulting energy prices and costs.
result The probability of extreme price events increases with an increased share of renewable energy sources.
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.
EGPR method forecasts power grid load and generation with high accuracy.
problem Accurate week-long forecasting of load demand and power generation for efficient power grid operation.
method Gaussian process regression with ensembles of preceding weeks' data.
result EGPR method outperforms traditional methods in forecasting weekly load and generation.
A new model uses sparse Gaussian processes to hedge electricity market risks.
problem Risk minimization in electricity markets due to non-storability and volatility.
method Coregionalized sparse Gaussian processes to model price and load correlations.
result The model outperforms traditional average-load strategies in hedging.
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…
Improved electrical load forecasting model using Fourier-enhanced RNN.
problem Electrical load time series downscaling with high accuracy and low error.
method Combines recurrent neural network with Fourier seasonal embeddings and self-attention.
result Significantly reduces RMSE across different time horizons compared to existing methods.
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.
The study improves load forecasting for electricity consumers using advanced machine learning models.
problem Improving short-term load forecasting for effective scheduling and decision-making.
method Proposes and evaluates statistical nonlinear models, including LSTM and GRU, for 15-min frequency electricity load forecasting.
result Advanced models outperform other models in out-of-sample forecasting accuracy, as shown by the Diebold-Mariano test.
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.
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.
Paper proposes combining GAM and DNN for accurate peak demand estimation from lower-resolution data.
problem Predicting high-resolution peak demand from limited lower-resolution data.
method Combines generalized additive models (GAM) and deep neural networks (DNN) for half-hourly load forecasting.
result Proposed method reduces out-of-sample RMSE by 57.4% compared to benchmark.
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.
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.
New principle reduces load imbalance in LLM serving systems, saving up to 52% energy.
problem Wasted computational power due to load imbalance in LLM serving systems.
method Developed a universal load-balancing principle for barrier-synchronized systems with non-migratable state.
result Proves worst-case theoretical guarantees for imbalance reduction and energy savings.
Recently there has been significant research on power generation, distribution and transmission efficiency especially in the case of renewable resources. The main objective is reduction of energy losses and this requires improvements on data acquisition and analysis. In this paper we address these concerns by using con…
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.
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.
Designing probing injections for smart inverters to infer non-metered loads.
problem Inferring non-metered loads from electric grid probing.
method Designing probing injections that adhere to inverter and network constraints, using a library of candidate vectors and SDP relaxation for noisy data.
result Improved load estimates through optimal probing design.
We present a methodology for probabilistic load forecasting that is based on lasso (least absolute shrinkage and selection operator) estimation. The model considered can be regarded as a bivariate time-varying threshold autoregressive(AR) process for the hourly electric load and temperature. The joint modeling approach…
Hierarchical clustering models capture electricity load patterns from smart meters.
problem Improving efficiency and sustainability of electricity systems.
method Quantile autocovariances and autocorrelations for summarizing time series.
result Clusters identify relevant consumption behaviors and capture geo-demographic segmentation.
Smart inverters probe grids to infer non-metered loads.
problem Lack of real-time metering in distribution grids.
method Probing inverters to record voltage responses and infer loads.
result Grid probing can infer non-metered loads under certain conditions.
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.
An on-going debate in the energy economics and power market community has raised the question if energy-only power markets are increasingly failing due to growing feed-in shares from subsidized renewable energy sources (RES). The short answer to this is: No, they are not failing. Energy-based power markets are, however…
Transformer model forecasts electricity price spread for virtual bidding.
problem Volatility in renewable energy causes price forecasting challenges.
method Transformer-based deep learning model using various time-series features.
result Trading strategy at peak hour yields nearly consistent profit.
Optimizes heating setpoints with uncertain loads using various feedback types.
problem Optimizing heating setpoints with uncertain, flexible loads.
method Online convex optimization (OCO) with different types of feedback.
result Sublinear regret bounds achieved in all feedback types.
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.
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.
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…
System detects power grid health using AI and machine learning.
problem Detecting and preventing power grid malfunctions.
method Artificial intelligence, machine learning, recurrent neural networks, SVM, LSTM.
result High accuracy in detecting grid health, scalable for complex architectures.
Paper presents a shape-based approach for better household load curve clustering and prediction.
problem Difficulty in classifying and predicting consumer energy consumption due to many clusters.
method Shape-based approach using Dynamic Time Warping (DTW) to align energy consumption patterns.
result Reduces the number of representative groups by 50% and improves prediction accuracy.
Improved electricity price forecasting model combining linear and non-linear structures.
problem Day-ahead electricity price forecasting in energy systems.
method Recurrent neural networks with embedded linear structures.
result Approximately 11% higher accuracy than state-of-the-art models.
FMOPF generates diverse near-optimal power flow solutions.
problem Generating diverse near-optimal power flow solutions for risk quantification.
method Decouples compression from generation through latent flow matching and explicitly models load-state coupling.
result FMOPF provides the most effective Newton-Raphson warm starts and lowest tail risk.
Machine learning helps dispatchers manage power grids more efficiently.
problem Managing power grids with varying demands and complex production systems.
method Developed novel machine learning techniques to mimic human decisions and devise remedial actions.
result The approach successfully prevents power flow limits violations in real-time.