Plants sense their environment by producing electrical signals which in essence represent changes in underlying physiological processes. These electrical signals, when monitored, show both stochastic and deterministic dynamics. In this paper, we compute 11 statistical features from the raw non-stationary plant electric…
Plants monitor their surrounding environment and control their physiological functions by producing an electrical response. We recorded electrical signals from different plants by exposing them to Sodium Chloride (NaCl), Ozone (O3) and Sulfuric Acid (H2SO4) under laboratory conditions. After applying pre-processing tec…
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.
Study compares two factor models for electricity spot prices across different periods.
problem Analyzing performance of factor models for electricity spot prices in various time periods.
method Developed a Markov Chain Monte Carlo method for model calibration and used simulations and posterior predictive checks for evaluation.
result 4-factor model outperforms 3-factor model in non-crisis times, but not in crises.
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.
A new method forecasts hourly electricity prices considering product dynamics and limit order book signals.
problem High volatility and imbalance in power systems due to renewable energy and flexible demand.
method Incorporates short-term features from hourly and quarter-hourly products, including limit order book and neighboring product signals.
result Features from the limit order book are most influential, and neighboring product signals improve forecast accuracy.
This paper is an attempt to separate cardiac and respiratory signals from an electrical bio-impedance (EBI) dataset. For this two well-known algorithms, namely Principal Component Analysis (PCA) and Independent Component Analysis (ICA), were used to accomplish the task. The ability of the PCA and the ICA methods first …
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.
ABACUS detects changes in multivariate data without labels.
problem Challenging to detect changes in correlated multivariate data.
method Bayesian source separation with multi-level sparsity.
result Competitive or superior performance in simulations and real-world applications.
Predicting the health of components in complex dynamic systems such as an automobile poses numerous challenges. The primary aim of such predictive systems is to use the high-dimensional data acquired from different sensors and predict the state-of-health of a particular component, e.g., brake pad. The classical approac…
Model predicts and optimizes trading of electricity price spreads across multiple zones.
problem Forecasting and optimizing day-ahead versus real-time price spreads in U.S. electricity markets.
method Unified statistical model for positive and negative spikes, structural price impact model based on bid stacks.
result Optimal trading strategy improves risk-return profile and highlights market heterogeneity.
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.
PIML uses physics equations in machine learning for better forecasting.
problem Forecasting time series data with physical constraints.
method Physics-informed neural networks (PINNs) and kernel methods.
result PIML improves forecasting accuracy with physical constraints.
New method uncovers small but significant local activities in time-series data.
problem Reconstructing small but important local activities in time-series data.
method Neural state-space models with latent causal-effect disentanglement.
result Demonstrated proof-of-concept on reconstructing ectopic foci in cardiac electrical propagation.
Power quality (PQ) analysis describes the non-pure electric signals that are usually present in electric power systems. The automatic recognition of PQ disturbances can be seen as a pattern recognition problem, in which different types of waveform distortion are differentiated based on their features. Similar to other …
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.
New method learns interaction-aware orderbook representation for better intraday electricity price forecasting.
problem Challenges in probabilistic intraday electricity price forecasting due to dynamic orderbook microstructure.
method OrderFusion: an end-to-end and parameter-efficient probabilistic forecasting model that learns interaction-aware representation of buy-sell dynamics.
result Consistent improvements over conventional baselines in probabilistic forecasting of CID price indices.
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.
High temporal resolution measurements of human brain activity can be performed by recording the electric potentials on the scalp surface (electroencephalography, EEG), or by recording the magnetic fields near the surface of the head (magnetoencephalography, MEG). The analysis of the data is problematic due to the fact …
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.
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.
Demand response is designed to motivate electricity customers to modify their loads at critical time periods. The accurate estimation of impact of demand response signals to customers' consumption is central to any successful program. In practice, learning these response is nontrivial because operators can only send a …
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.
Emotion classification improved using brain signals from tactile enhanced multimedia.
problem Classifying viewer emotions in tactile enhanced multimedia.
method Frequency domain features from EEG data analyzed using SVM.
result Increased accuracy (76.19%) compared to time domain features (63.41%).
This paper addresses converting speech to EGG signals without hardware, improving accuracy.
problem Estimating EGG signals from speech without hardware.
method Optimization of evidence lower bound with KL-divergence minimization.
result The method generates EGG signals that agree with gold standard and outperforms state-of-the-art.
System identifies power grid location from media recordings.
problem Identifying the origin of power distribution grid from media recordings.
method Cascaded SVM and pole-matching classifiers for grid identification.
result Cascaded system improves accuracy by 15.57%.
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.
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…
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.
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…
Paper proposes a method to locate power grid recordings using ENF sequences.
problem Locating power grid recordings without concurrent power signals.
method Extract ENF sequences from power and audio recordings, develop multi-class SVM model.
result Validation of location authenticity of recordings using ENF sequences.
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 …
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.
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.
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…
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.
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.
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.
Riemannian geometry has been applied to Brain Computer Interface (BCI) for brain signals classification yielding promising results. Studying electroencephalographic (EEG) signals from their associated covariance matrices allows a mitigation of common sources of variability (electronic, electrical, biological) by constr…