In this short paper, the Electre Tri-Machine Learning Method, generally used to solve ordinal classification problems, is proposed for solving the Record Linkage problem. Preliminary experimental results show that, using the Electre Tri method, high accuracy can be achieved and more than 99% of the matches and nonmatch…
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48 results for “ELECTRE Tri”
ELECTRE Tree infers ELECTRE Tri-B parameters using a machine learning approach.
problem Infer ELECTRE Tri-B parameters from decision-maker inputs.
method Random Forest inspired algorithm: generate models, optimize parameters, merge or vote.
result ELECTRE Tree generates non-linear decision boundaries for voting, linear for merged model.
Small Medium-sized Enterprises (SMEs) face many obstacles when they try to access credit market. These obstacles are increased if the SMEs are innovative. In this case, financial data are insufficient or even not reliable. Thus, when building a judgemental rating model, mainly based on qualitative criteria (soft inform…
Connected graph for twice-punctured torus curves.
problem Structure of tri-pants graph on twice-punctured torus.
method Examined relationship with Farey complex to prove connectivity and infinite diameter.
result Tri-pants graph is connected and has infinite diameter.
We present a fast variational Bayesian algorithm for performing non-negative matrix factorisation and tri-factorisation. We show that our approach achieves faster convergence per iteration and timestep (wall-clock) than Gibbs sampling and non-probabilistic approaches, and do not require additional samples to estimate t…
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.
Integrated analysis of the urban water-electricity demand nexus in the Midwestern United Statescs.LG
Considering the interdependencies between water and electricity use is critical for ensuring conservation measures are successful in lowering the net water and electricity use in a city. This water-electricity demand nexus will become even more important as cities continue to grow, causing water and electricity utiliti…
Constructs tri-Hamiltonian structure and Frobenius manifold for asymmetric gAL hierarchy
problem Tri-Hamiltonian structure and Frobenius manifold for asymmetric gAL hierarchy
method Local tri-Hamiltonian structure construction and Frobenius manifold construction
result Dispersionless limits of flows belong to Principal Hierarchy
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…
Electricity consumption has increased exponentially during the past few decades. This increase is heavily burdening the electricity distributors. Therefore, predicting the future demand for electricity consumption will provide an upper hand to the electricity distributor. Predicting electricity consumption requires man…
Local tri-Hamiltonian structure for Ablowitz-Ladik hierarchy established.
problem Establishing a tri-Hamiltonian structure for the Ablowitz-Ladik hierarchy.
method Constructing a local tri-Hamiltonian structure and computing central invariants.
result Central invariants of one bi-Hamiltonian structure are 1/24, and dispersionless limit matches Frobenius manifold.
Upper bounds for surface-links in the Yoshikawa table are estimated.
problem Estimating Kirby-Thompson invariants of surface-links.
method Using tri-plane diagrams and L-, L*-invariants.
result Upper bounds for surface-links in the Yoshikawa table are obtained.
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.
Panel semiparametric quantile regression neural network for electricity consumption forecastingstat.ML
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…
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.
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…
Electricity accounts for 25% of global greenhouse gas emissions. Reducing emissions related to electricity consumption requires accurate measurements readily available to consumers, regulators and investors. In this case study, we propose a new real-time consumption-based accounting approach based on flow tracing. This…
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.
Bridge trisections and knotted surfaces connected via tri-plane diagrams.
problem Computing and understanding knotted surfaces using bridge trisections.
method Using tri-plane diagrams to compute normal Euler number, fundamental group, and analyze bridge trisections of ribbon surfaces.
result Produced an infinite family of knotted spheres with non-isotopic bridge trisections of minimal complexity.
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…
Measuring long-range dependence in electricity pricescond-mat.stat-mech
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…
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.
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…
Deep Learning-Based Electricity Price Forecast for Virtual Bidding in Wholesale Electricity Marketcs.LG
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.
The liberalization of electricity markets and the development of renewable energy sources has led to new challenges for decision makers. These challenges are accompanied by an increasing uncertainty about future electricity price movements. The increasing amount of papers, which aim to model and predict electricity pri…
New methods find minimal crossing numbers for surfaces in .
problem Determining minimal crossing numbers for surfaces in .
method Developed new tri-plane diagrams to analyze surfaces.
result Proved minimal crossing numbers for nonorientable unknotted surfaces in .
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.
Exploring Strategies for Classification of External Stimuli Using Statistical Features of the Plant Electrical Responsephysics.bio-ph
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…
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.
Model for hedging price and quantity risks in electricity markets.
problem Hedging risks for energy retailers in a regulated electricity market.
method Closed-form solution for optimal portfolio using financial instruments based on price and weather indexes.
result Closed-form solution for mean-var model in discrete setting without distributional assumptions.
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.
Novel probabilistic models forecast residential heating and electricity demand at hourly resolution.
problem Accurate hourly forecasting of residential heating and electricity demand.
method Probabilistic deep learning models trained on gas-heated region data.
result Significant improvement in forecast accuracy compared to NREL's ResStock model.
Adaptive Methods for Short-Term Electricity Load Forecasting During COVID-19 Lockdown in Francestat.AP
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.
Research optimizes a small RES utility's portfolio by dynamically trading in German electricity markets.
problem Managing risks in RES producers and electricity traders in changing electricity markets.
method Uses SVAR model to estimate market relationships and data-driven trading strategies to optimize revenue and reduce risk.
result Data-driven trading strategies increase utility revenue and reduce trading risk.
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.
2-twist trefoil has 6 crossings, proving non-trivial knotted surface.
problem Computing the crossing number of non-trivial knotted surfaces.
method Bridge trisection and tri-plane diagrams to minimize crossings.
result 2-twist trefoil has crossing number 6.
Unifying the theory of storage and the risk premium by an unobservable intrinsic electricity priceq-fin.MF
Introduces an unobservable intrinsic electricity price to link storage theory with risk premium.
problem Connecting storage theory with risk premium in electricity markets.
method Introduces an unobservable intrinsic electricity price and derives prices for various contracts.
result Finds an overall negative risk premium in empirical analysis.
Forecasting Electricity Spot Prices using Lasso: On Capturing the Autoregressive Intraday Structureq-fin.ST
In this paper we present a regression based model for day-ahead electricity spot prices. We estimate the considered linear regression model by the lasso estimation method. The lasso approach allows for many possible parameters in the model, but also shrinks and sparsifies the parameters automatically to avoid overfitti…
Study shows increased VRE penetration reduces electricity prices and volatility.
problem Impact of increased variable renewable energy on electricity prices and volatility.
method Hourly, real-time data from six ISOs, quantile and skew t-distribution regressions.
result Increased VRE penetration is associated with decreased system electricity price and volatility in most ISOs.
Deep learning method clusters multi-view data matrices.
problem Clustering heterogeneous relational data matrices.
method Deep collective matrix tri-factorization (DCMTF).
result Discover latent clusters across input matrices and their associations.
Deep Recurrent Electricity Theft Detection in AMI Networks with Random Tuning of Hyper-parameterscs.LG
Modern smart grids rely on advanced metering infrastructure (AMI) networks for monitoring and billing purposes. However, such an approach suffers from electricity theft cyberattacks. Different from the existing research that utilizes shallow, static, and customer-specific-based electricity theft detectors, this paper p…
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.
The recent research report of U.S. Department of Energy prompts us to re-examine the pricing theories applied in electricity market design. The theory of spot pricing is the basis of electricity market design in many countries, but it has two major drawbacks: one is that it is still based on the traditional hourly sche…