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.
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.
Novel neural network predicts electricity prices with higher moments.
problem Probabilistic forecasting of volatile electricity prices.
method Distributional neural network with a probability layer.
result Significantly outperforms benchmarks in forecasting.
Spring-electrical models predict network links based on node proximity.
problem Predicting links in networks.
method Spring-electrical models applied to network layouts.
result The Euclidean distance in network layouts correlates with link probabilities.
Neural nets solve electric field in non-convex microfluidic devices.
problem Solving differential equations in non-convex geometries.
method Neural network approximation of electric potential and field.
result Deep neural networks outperform shallow networks in accuracy.
Deep RNN detects electricity theft in smart grids.
problem Electricity theft in smart grids.
method Generalized deep recurrent neural network (RNN) with gated recurrent unit (GRU) and random hyper-parameter tuning.
result Superior performance compared to existing detectors.
Regression decision trees outperform other methods in predicting electricity prices.
problem Short-term forecasting of electricity prices to manage risk and strategy.
method Comparison of regression decision trees and recurrent neural networks (RNNs) with ARIMA.
result Regression decision trees achieve high performance compared to other methods.
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.
Competition has been introduced in the electricity markets with the goal of reducing prices and improving efficiency. The basic idea which stays behind this choice is that, in competitive markets, a greater quantity of the good is exchanged at a lower and a lower price, leading to higher market efficiency. Electricity …
Neural networks outperform single-hour models in day-ahead electricity price forecasting.
problem Improving accuracy in day-ahead electricity price forecasting.
method Compared two neural network structures: one-hour models and daily auction models.
result Daily auction models outperform one-hour models in forecasting accuracy.
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.
Neural networks predict EV charging station usage from network layout.
problem Designing optimal EV charging station networks.
method Used neural networks to predict usage from station layout.
result Quickly estimates average usage statistics from proposed station placements.
The paper uses DNN for electricity price forecasting and XAI for understanding the factors.
problem Complex interactions and dependencies in electricity markets make it hard to understand price dynamics.
method Used DNN for forecasting and XAI (SHAP, Gradient, heatmaps) for understanding factors.
result Introduced novel concepts SSHAP values and SSHAP lines for enhanced representation of high-dimensional tabular models.
Transfer learning improves electricity price forecasting accuracy.
problem Accurate day-ahead electricity price prediction using available data.
method Pre-train a neural network on source markets and fine-tune for target market.
result Transfer learning significantly improves forecasting performance.
New approach to electric group for knots and links.
problem No previous publication of electric invariant for knots and links.
method Simple and general approach to electric group for oriented knots and links, using proper colouring of knot diagrams.
result Each homomorphism from the electric group to an arbitrary finite group can be described by a proper colouring of the diagram.
Deep network improves electrical tomography across multiple frequencies.
problem Nonlinear multi-frequency electrical impedance tomography (mfEIT) for tissue conductivity estimation.
method Integrates graph neural networks (GNNs) into the iterative Proximal Regularized Gauss Newton (PRGN) framework to reconstruct tissue concentrations accurately.
result Accurate reconstruction of overlapping tissue fraction concentrations across multiple frequencies.
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.
Machine learning improves electricity price forecasting.
problem Predicting electricity prices in various horizons.
method Application of machine learning techniques to EPF models.
result Machine learning models outperform traditional methods.
Deep models predict intraday electricity prices accurately.
problem Accurately forecasting intraday electricity prices.
method Two deep time series probabilistic models using ESNs with stochastic disturbances and copulas.
result Deep distributional models provide accurate short-term probabilistic price forecasts.
Develops a deep RL algorithm for ESS control in electricity markets.
problem Controlling ESSs for arbitrage in real-time electricity markets under price uncertainty.
method Formulated as a Markov decision process, developed a deep RL algorithm using a recurrent neural network.
result Effectiveness of the algorithm verified using real-time PJM electricity prices.
In this paper, the concepts and the direct theorems of stability in the sense of Liapunov, within the framework of Birkhoffian dynamical systems on manifolds, are considered. The Liapunov-type functions are constructed for linear and nonlinear LC and RLC electrical networks, to prove stability under certain conditions.
Totally nonnegative Grassmannian and related spaces are shown to be like closed balls.
problem Understanding the topological structure of certain spaces in combinatorics.
method Proving homeomorphic to closed balls using advanced combinatorial and geometric techniques.
result Three significant spaces in combinatorics are proven to be topologically equivalent to closed balls.
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.
Model detects electricity theft with high accuracy.
problem Detecting electricity theft on imbalanced datasets.
method Multi-head self-attention mechanism with dilated convolutions and binary mask.
result Achieved AUC of 0.926, improving previous work by 17%.
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.
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 uses ML for electricity price forecasting using order book data.
problem Forecasting German electricity spot market prices.
method Developed feature extraction for order book data, used cross-validation, compared neural networks and random forests to statistical models.
result Machine learning models outperform traditional approaches.
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.
This paper uses deep reinforcement learning to automate electric transmission voltage control.
problem Automating voltage control in electric transmission systems.
method Deep reinforcement learning (DRL) applied to voltage control, with a novel DQN modification.
result DRL can automate voltage control at scale, but more research is needed.
This research improves LSTM for monthly electricity demand forecasting using pattern-based methods.
problem Forecasting mid-term monthly electricity demand with high accuracy.
method Developed a hybrid LSTM model using x-patterns and exponential smoothing.
result The hybrid model outperformed standard LSTM and classical models.
Study improves electricity price forecasting accuracy using a hybrid model.
problem Accurate short-term electricity price forecasting is challenging due to social and natural factors.
method Hybrid model combining GARMA, G-GARCH, Wavelet, LLWNN, and optimization algorithms.
result The hybrid model outperforms other models in Nord Pool Electricity markets.
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.
Resonant machine learning uses electrical network dynamics to optimize learning efficiently.
problem Traditional energy-based learning models are dissipative and inefficient.
method Proposes a new learning framework with two energy components (active and reactive) to ensure active-power dissipation during learning.
result Support vectors in resonant SVMs correspond to self-sustained oscillations in an LC network.
Dual model predicts electricity spot prices using neural networks and wavelets.
problem Forecasting hourly electricity spot prices.
method Dual generalized long memory modelling with k-factor GARMA and G-GARCH models, using LLWNN and PSO for variance prediction.
result The hybrid k-factor GARMA-LLWNN model outperforms other methods in forecasting accuracy.
Prototype for early fault warnings in large electric grids.
problem Early detection and classification of faults in complex electric grids.
method Multi-stage approach with anomaly detection, feature mapping, classification, and clustering.
result Random forest method offers the most accurate fault classification.
Generative model improves intraday electricity price forecasting.
problem Intraday electricity price forecasting for improved trading strategies.
method Generative neural network model for probabilistic path forecasts.
result Generative model leads to higher profit gains than benchmark methods.
Optimizes electric aircraft deployment for Canadian aviation to reduce emissions.
problem Limited fleet capacity and operational structure hinder electric aircraft transition.
method Multi-period mixed-integer linear programming (MILP) framework.
result Electric aircraft can reduce emissions by over 70% within five years.
Deep learning detects inaccurate smart meters for resource savings.
problem Detecting and replacing inaccurate smart meters to save resources.
method Developed a deep-learning method using LSTM and CNN to predict electricity usage trajectories.
result High accuracy in detecting inaccurate meters for practical usage.
Proposes a neural network for efficient imbalance electricity price forecasting.
problem Accurate and efficient imbalance electricity price forecasting in industrial energy trading systems.
method Market-rule-informed neural network framework.
result The proposed model achieves competitive forecasting performance with fewer parameters and shorter training time.
Two methods improve electricity price forecasting in Europe.
problem Improving accuracy in day-ahead electricity price forecasting.
method Deep neural network and feature selection algorithm considering market integration.
result Predictive accuracy improved from 15.7% to 12.5% sMAPE.
Develops a combinatorial semi-bandit method for electric vehicle charging station selection.
problem Long-distance navigation for BEVs with unknown charging station availability and performance.
method Combinatorial semi-bandit framework, pre-processing road network, Bayesian modeling, Thompson Sampling, BayesUCB, Epsilon-greedy.
result Demonstrates improved navigation performance on long-distance BEV charging station selection.
New method improves probabilistic electricity price predictions.
problem Improving point forecasts to probabilistic distributions for better decision-making.
method Isotonic Distributional Regression combined with other postprocessing methods.
result Isotonic Distributional Regression outperforms other methods in combining probabilistic distributions.
Short-term probabilistic forecasting of German electricity imbalance prices.
problem Uncertainty in renewable energy capacity and electricity prices.
method Combining lasso with bootstrap, gamlss, and probabilistic neural networks for forecasting imbalance prices.
result Sophisticated methods improve empirical coverage of imbalance prices but do not substantially outperform the intraday continuous price index.
THieF improves day-ahead electricity price prediction accuracy by reconciling hourly and block forecasts.
problem Improving accuracy in predicting day-ahead electricity prices.
method Temporal hierarchy forecasting (THieF) reconciling hourly and block forecasts.
result THieF significantly improves accuracy (up to 13%) at all levels of prediction.
Study forecasts monthly electricity demand using pattern similarity-based methods.
problem Forecasting monthly electricity demand accurately.
method Pattern similarity-based forecasting methods (PSFMs) including k-NN, fuzzy, kernel regression, and GRNN.
result Ensemble models outperform individual PSFMs in forecasting accuracy.
Bayesian model for energy consumption helps electric vehicles navigate efficiently.
problem Limited battery capacity in electric vehicles makes energy efficient navigation challenging.
method Developed an online learning framework using Bayesian models and exploration strategies like Thompson Sampling.
result Established rigorous regret bounds for Thompson Sampling in both single-agent and multi-agent settings.
Paper proposes a neural network for estimating brain conductivity without segmentation.
problem Accurate head model generation for personalized TMS with realistic conductivity.
method Convolutional neural network estimating conductivity from MRI data.
result Smooth electric field results similar to conventional methods without segmentation.
Paper introduces reinforcement learning for managing power grids.
problem Balancing power flows and maintaining grid stability in real-time.
method Reinforcement Learning applied to power network operations.
result Demonstrates feasibility of machine learning in power grid management.