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.
Model predicts future electricity production and flows in Europe.
problem Anticipate changes in electricity production and flows due to energy transition.
method Constructed aggregated pan-European model coupled with dispatch algorithm.
result Large power fluctuations can be absorbed via increased electricity exchanges.
How does dynamic price information flow among Northern European electricity spot prices and prices of major electricity generation fuel sources? We use time series models combined with new advances in causal inference to answer these questions. Applying our methods to weekly Nordic and German electricity prices, and oi…
Study uses machine learning to optimize power generation in electrical grids.
problem Optimizing power generation in electrical grids while respecting physical and engineering constraints.
method Two formulations of ACOPF as machine learning problems: direct prediction and constraint prediction.
result Validated machine learning approaches on two benchmark grids.
It is shown that 3D part of a spherically symmetric solution in conformal Weyl gravity interacting with Maxwell electrodynamics is a Yamabe flow as well. The Yamabe flow describes the transition from a horn of an initial wormhole to a 3D Euclidean space both filled with a radial electric field. It is supposed that such…
Stable knots and links can exist in electromagnetic fields.
problem Stability of knots and links in electromagnetic fields.
method Proving the existence of electromagnetic fields preserving link topology.
result Every link can be realized as stable field lines in electromagnetic fields.
A new data-driven model forecasts electricity prices efficiently.
problem Forecasting electricity prices using traditional methods.
method Integrates data-driven and fundamental models, learns from historical data.
result Significantly improves forecasting accuracy compared to existing models.
The problem of probabilistic forecasting and online simulation of real-time electricity market with stochastic generation and demand is considered. By exploiting the parametric structure of the direct current optimal power flow, a new technique based on online dictionary learning (ODL) is proposed. The ODL approach inc…
Paper analyzes Min-Sum scheme for solving Laplacian systems and flow problems.
problem Solving systems of linear equations and computing electric flows in graphs.
method Develops a framework to analyze Min-Sum message passing for voltage and flow problems.
result Characterizes error and convergence of Min-Sum algorithm on general and regular graphs.
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.
The paper forecasts joint electricity demand across 14 British regions using additive models.
problem Forecasting regional electricity demand with cross-regional dependencies.
method Modified Cholesky parametrisation for multivariate Gaussian model, gradient boosting for model selection.
result The proposed model outperforms non-Gaussian copula-based models in forecasting.
New matrix completion method for arbitrary sampling patterns using network flows.
problem Matrix completion under arbitrary sampling patterns.
method Network flow approach to matrix completion.
result Minimax optimal estimation for individual entries.
Paper introduces normalizing flows for accurate probabilistic energy forecasting.
problem Uncertainty in renewable energy forecasting for power systems.
method Normalizing flows for direct learning of multivariate stochastic distributions.
result Normalizing flows outperform other deep learning models in probabilistic forecasting.
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.
Method minimizes electricity procurement cost based on demand prediction errors.
problem Minimizing electricity procurement cost in spot markets.
method Formulate method to minimize procurement cost over two parameters.
result Minimizes total electricity cost with known unit prices and prediction errors.
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.
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.
New method maps global value chains at product level from trade data.
problem Lack of detailed product-level value chain information in existing datasets.
method Machine learning and trade theory applied to international trade data.
result Approximate product-level value chain information inferred from trade patterns.
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.
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.
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.
Paper classifies plant electrical signals to identify external stimuli.
problem Classifying external stimuli using plant electrical response.
method Computed 11 statistical features from plant electrical signals and used discriminant analysis.
result Raw electrical signals contain enough information for stimulus classification.
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.
New approach predicts electricity prices for months to years with probabilistic forecasts.
problem Uncertainty in long-term electricity price forecasting.
method Extends X-Model using supply and demand curve for hourly electricity prices.
result Probabilistic forecasts detect long-term price spikes.
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.
The paper proposes a new method for probabilistic load forecasting using Bernstein-Polynomial Normalizing Flows.
problem High variability in short-term load forecasting at the low-voltage level due to fluctuating demand and increasing electrification.
method Flexible conditional density forecasting based on Bernstein polynomial normalizing flows with neural network control.
result Density predictions outperform traditional methods for 24h-ahead load forecasting.
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.
We analyze long-term memory properties of hourly prices of electricity in the Czech Republic between 2009 and 2012. As the dynamics of the electricity prices is dominated by cycles -- mainly intraday and daily -- we opt for the detrended fluctuation analysis, which is well suited for such specific series. We find that …
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.
Algorithm optimizes hedging in electricity markets by minimizing variance risk.
problem Hedging contracts in electricity markets due to liquidity and market characteristics.
method Developed an algorithm for mean variance hedging considering transaction costs and market depth.
result Algorithm effectively reduces residual risk in electricity market positions.
This paper improves electricity price forecasting and analyzes economic benefits.
problem Improving accuracy of quarter-hourly electricity price forecasts.
method Proposes a multivariate elastic net regression model for German spot markets.
result Simple trading strategies with accurate forecasts can lead to substantial economic impact.
Improves cardiac simulator fit to real patient ECG data.
problem Intractable inference over non-differentiable cardiac simulators.
method Variational inference combined with Bayesian optimization.
result Significant improvement in simulator fit to real patient ECG data.
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.
The price of electricity is far more volatile than that of other commodities normally noted for extreme volatility. The possibility of extreme price movements increases the risk of trading in electricity markets. However, underlying the process of price returns is a strong mean-reverting mechanism. We study this featur…
The paper proves a new theorem linking mass and electric charge for certain types of manifolds.
problem Proving a new positive mass theorem for manifolds with charge.
method Using conformal relations and scalar curvature, the authors derive a new theorem.
result The sum of mass is not less than the modulus of total electric charge under certain conditions.
Extends RSP model with net flow and capacity constraints for better network analysis.
problem Improving shortest path models with net flows and capacity constraints.
method Developed net flow RSP model and introduced capacity constraints. Proposed algorithms for computing expected routing costs and solving constrained problems using Lagrangian duality.
result Net flow RSP dissimilarity measure is competitive with state-of-the-art dissimilarities.
The paper simplifies electricity market curves with less parameters.
problem Modeling electricity prices and demands efficiently.
method Mesh-free interpolation using radial basis functions.
result The method reduces parameters needed to represent curves.
In our paper we analyze the relationship between the day-ahead electricity price of the Energy Exchange Austria (EXAA) and other day-ahead electricity prices in Europe. We focus on markets, which settle their prices after the EXAA, which enables traders to include the EXAA price into their calculations. For each market…
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.
ElecSim models long-term electricity planning with agent-based Monte-Carlo simulations.
problem Transitioning to zero-carbon energy systems requires careful policy decisions.
method Agent-based Monte-Carlo model for long-term electricity investment decisions.
result Monte-Carlo simulation improves model performance by 52.5%.
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.
Study proposes ATS algorithm to solve market clearing problem in Turkish day-ahead market.
problem Market clearing problem in Turkish day-ahead electricity market.
method Proposes an adaptive tabu search (ATS) algorithm to solve the problem, discretizing continuous search space and using adaptive radius.
result ATS algorithm performs better than heuristic decomposition method in most synthetic data sets.
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.