We experimentally achieve a 19% capacity gain per Watt of electrical supply power in a 12-span link by eliminating gain flattening filters and optimizing launch powers using machine learning by deep neural networks in a massively parallel fiber context.
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48 results for “power supply”
Dynamic pricing aims to match power supply and demand in an energy transition.
problem Mismatch between renewable energy supply and consumer demand.
method Formalizes decision-making problem, designs forecasting models, and statistical demand response models.
result Dynamic pricing can synchronise power supply and demand effectively.
The paper shows supply chain features improve cyber risk prediction.
problem Predicting cyber risk from supply chain attributes.
method Machine learning, external supply chain features, AUC improvement.
result Supply chain network features improve AUC by 2.3%.
This paper optimizes a power-to-heat system using reinforcement learning for cost minimization under uncertain conditions.
problem Optimizing a power-to-heat system with fluctuating renewable energy sources.
method Stochastic optimal control, reinforcement learning (Q-learning).
result Reinforcement learning provides an efficient solution to the optimization problem.
New method quantifies systemic risk of firms in supply networks.
problem Quantifying economic systemic risk of firms from supply networks.
method Unique value-added tax dataset; novel approach for computing ESR.
result A tiny fraction of companies have high systemic risk impacting 23% of national production.
The study finds that supply chain information from LLM embeddings improves stock returns predictions.
problem Predicting stock returns using textual information from annual reports.
method Combining LLM embeddings of annual reports with supply chain knowledge graph propagation.
result Network-augmented embeddings significantly predict stock returns with a Sharpe ratio of 0.86 and alpha of 7.27%.
Study on supply chain networks using wire transfers in Brazil.
problem Understanding economic integration and specialization in Brazilian cities.
method Constructed a directed and weighted network of wire transfers between cities, analyzed centrality measures, and used econometric analysis.
result Disassortative mixing pattern in trade network, stronger after recession, and impact of court efficiency on economic transactions.
Study builds dataset and benchmarks ML models for accurate solar and wind power forecasting in France.
problem Accurate prediction of non-dispatchable renewable energy sources for grid stability and price prediction.
method Comprehensive methodology using machine learning models trained with spatially explicit weather data and production site capacity.
result Neural networks outperform traditional models in forecasting solar and wind power production in France.
Prediction-Powered Inferencestat.ML
Valid inference from data and predictions.
problem Valid statistical inference with machine learning predictions.
method Framework for valid inference using machine learning predictions.
result Valid confidence intervals without assumptions on predictions.
The growing integration of distributed energy resources (DERs) in urban distribution grids raises various reliability issues due to DER's uncertain and complex behaviors. With a large-scale DER penetration, traditional outage detection methods, which rely on customers making phone calls and smart meters' "last gasp" si…
We describe explicit presentations of all stable and the first nonstable homotopy groups of the unitary groups. In particular, for each n >= 2 we supply n homotopic maps that each represent the (n-1)!-th power of a suitable generator of pi_2n(U(n)) = Z_{n!}. The product of these n commuting maps is the constant map to …
How does supply uncertainty affect the structure of supply chain networks? To answer this question we consider a setting where retailers and suppliers must establish a costly relationship with each other prior to engaging in trade. Suppliers, with uncertain yield, announce wholesale prices, while retailers must decide …
Predictability of Power Grid Frequencyphysics.soc-ph
A predictor improves power grid frequency forecasts up to one hour.
problem Improving frequency forecast for better power system stability.
method Developed a weighted-nearest-neighbor (WNN) predictor.
result Forecasts for up to one hour are more precise than averaged daily profiles.
Implementing a set of microeconomic criteria, we develop price dynamics equations using a function of demand/supply with key symmetry properties. The function of demand/supply can be linear or nonlinear. The type of function determines the nature of the tail of the distribution based on the randomness in the supply and…
Reliability Options are capacity remuneration mechanisms aimed at enhancing security of supply in electricity systems. They can be framed as call options on electricity sold by power producers to System Operators. This paper provides a comprehensive mathematical treatment of Reliability Options. Their value is first de…
GNNs improve supply chain analytics with real-world benchmarks.
problem Limited research on applying GNNs to supply chain management.
method Conceptual discussions, detailed formulations, examples, mathematical definitions, and task guidelines.
result GNN-based models outperform other methods by 10-40% in various supply chain tasks.
Supply chains are the backbone of the global economy. Disruptions to them can be costly. Centrally managed supply chains invest in ensuring their resilience. Decentralized supply chains, however, must rely upon the self-interest of their individual components to maintain the resilience of the entire chain. We examine t…
New framework forecasts both supply and demand in rental markets.
problem Booking models ignore supply, leading to regime-specific ceilings.
method Three-part coupling framework (behavioral, informational, intervention).
result Booking models learn a regime-specific ceiling and become fragile.
Optimal annuitization strategy depends on age, labor income, and mortality risk.
problem Maximizing utility from consumption and labor income under age-dependent mortality.
method Dynamic programming approach to derive closed-form solutions.
result Post-retirement labor income acts as a substitute for annuitization.
This paper evaluates the impact of the power extent on price in the electricity market. The competitiveness extent of the electricity market during specific times in a day is considered to achieve this. Then, the effect of competitiveness extent on the forecasting precision of the daily power price is assessed. A price…
Deep-Gap predicts crowdsourcing supply-demand gaps using deep learning.
problem Balancing supply and demand in mobile crowdsourcing.
method Residual learning-based deep neural networks trained on time series data and external factors.
result Deep-Gap achieves lowest forecasting errors compared to state-of-the-art methods.
Paper uses tensor completion to estimate HVAC fan power baselines.
problem Estimating HVAC fan power without demand response.
method Tensor completion for multi-dimensional data analysis.
result Tensor completion outperforms existing baselining methods.
GraPhyR uses GNNs to optimize power grid reconfiguration in real-time.
problem Optimizing power grid reconfiguration for reliability and efficiency.
method Physics-informed Graph Neural Network (GNN) framework.
result GraPhyR learns to optimize DyR tasks efficiently.
Unified framework for complex financial networks using lattice theory.
problem Complex financial networks with multiple currencies and dependencies.
method Recast classical financial clearing model into lattice liability networks.
result Lattice-valued clearing sections form a complete lattice, enabling tractable analysis.
This paper applies reactor theory to supply chain management.
problem Maintaining optimal item delivery and collection ratios in supply chains.
method Translating neutron transport and diffusion theory to supply chain management, introducing analogy factors and interactors.
result A deterministic model for supply chain optimization.
This paper develops a stochastic learning-optimization model for resilient automotive supply chains.
problem Supply chain disruptions and volatile demand pose challenges to the UK automotive industry.
method Integrates Bayesian inference with inventory optimization for a two-echelon system subject to stochastic demand and disruptions.
result The integrated approach achieves significant cost reductions and improved resilience during disruptions.
Elastic Cashq-fin.GN
Elastic Cash adjusts money supply to stabilize interest rates.
problem Stabilizing interest rates in a decentralized system.
method Modifies supply to keep interest rate fixed by public market.
result Improves elasticity of US Dollar and new cryptocurrencies.
Approximation of supply curvesq-fin.PR
The paper approximates supply curves using a one-step basis method.
problem Computing supply curves accurately and efficiently.
method Derives L2 approximation expression and proposes node selection procedure.
result Illustrates the approach with European electricity market bid curves.
Compound Finance optimizes risk metrics for V3 protocol using Chainrisk simulations.
problem Optimizing systemic risks in Compound V3 protocol.
method Millions of Chainrisk simulations to evaluate VaR and LaR, providing parameter adjustments.
result Optimization framework enhances protocol stability.
Study reveals supply chain correlations in firm growth rates.
problem Understanding correlations in firm growth rates and their supply chain relationships.
method Investigated correlation structure of firm growth rates and used Gaussian Markov Models to reconstruct supply chain networks.
result Supply chain-linked firms exhibit stronger correlation in growth rates than non-linked firms.
Deep neural networks optimize inventory decisions in complex supply chains.
problem Optimizing inventory decisions in stochastic multi-echelon supply chains.
method Pairwise modeling and DNN agents for order-up-to levels.
result The method performs better than alternate methods in general supply chain networks.
Study examines how COVID-19 intensified demand variability in U.S. supply chains.
problem The amplification of demand variability (Bullwhip Effect) in supply chains during the pandemic.
method Extensive industry-level data analysis using traditional and advanced empirical techniques.
result COVID-19 significantly amplified the Bullwhip Effect across different U.S. industries.
Analyzes new economic paradigm for non-independent consumer choices.
problem Non-independent consumer choices due to firm supply and consumer information.
method Develops a new mathematical framework for economic systems.
result New paradigm for economic system description is necessary.
Recently, along with the emergence of food scandals, food supply chains have to face with ever-increasing pressure from compliance with food quality and safety regulations and standards. This paper aims to explore critical factors of compliance risk in food supply chain with an illustrated case in Vietnamese seafood in…
Approach for assessing supply chain cyber risks using expert judgment and forecasting.
problem Supply chain managers face challenges in assessing cyber risks affecting business factors.
method Structured expert judgment and forecasting models to assess various attack techniques and impacts.
result Facilitates implementation of risk management activities and decision-making processes.
Supply chains lend themselves to blockchain technology, but certain challenges remain, especially around invoice financing. For example, the further a supplier is removed from the final consumer product, the more difficult it is to get their invoices financed. Moreover, for competitive reasons, retailers and manufactur…
Log-Ergodic Dynamics in Stochastic Monetary Velocity: Theoretical Insights and Economic Implicationsq-fin.GN
Log-ergodic model improves velocity of money prediction.
problem Improving velocity of money prediction for economic control.
method Log-ergodic processes to simulate monetary velocity.
result Log-ergodic model offers superior predictive power.
A theory for long-memory in supply and demandcond-mat.other
Recent empirical studies have demonstrated long-memory in the signs of orders to buy or sell in financial markets [2, 19]. We show how this can be caused by delays in market clearing. Under the common practice of order splitting, large orders are broken up into pieces and executed incrementally. If the size of such lar…
The average economic agent is often used to model the dynamics of simple markets, based on the assumption that the dynamics of many agents can be averaged over in time and space. A popular idea that is based on this seemingly intuitive notion is to dampen electric power fluctuations from fluctuating sources (as e.g. wi…
AI framework predicts invoice dilution in supply chain finance.
problem Invoice dilution risk in supply chain finance.
method AI, machine learning, dynamic credit limits, real-time projections.
result Supplemental AI model improves prediction accuracy.
Unified theory explains market impact using a simplified supply-demand parameter.
problem Understanding the market impact of metaorders and excess volatility.
method Coarse-grained approach with a single parameter ρ to model supply-demand equilibrium and market impact.
result Establishes a connection between excess volatility and order-driven markets through the square-root law.
A Self-organising Model of Market with Single Commoditycond-mat.stat-mech
We have studied here the self-organising features of the dynamics of a model market, where the agents `trade' for a single commodity with their money. The model market consists of fixed numbers of economic agents, money supply and commodity. We demonstrate that the model, apart from showing a self-organising behaviour,…
Study quantifies financial contagion risks in supply chains.
problem Supply chain shocks contribute to financial losses.
method Multi-layer network framework, micro-dataset of Hungarian firms.
result Supply chain shocks amplify financial losses by 4-3x.
Optimal supply against fluctuating demandphysics.soc-ph
Sornette et al. claimed that the optimal supply does not agree with the average demand, by analyzing a bakery model where a daily demand fluctuates with a uniform distribution. In this note, we extend the model to general probability distributions, and obtain the formula of the optimal supply for Gaussian distribution,…
TransCORALNet: A Two-Stream Transformer CORAL Networks for Supply Chain Credit Assessment Cold Startcs.LG
TransCORALNet uses transformer and CORAL for supply chain credit assessment with cold start.
problem Supply chain credit assessment for new borrowers with limited data.
method Two-stream transformer CORAL networks with domain adaptation and LIME.
result TransCORALNet outperforms state-of-the-art models in accuracy.
In this paper we explain the wild fluctuations of financial prices from the intrinsic amplifying feedback of speculative supply and demand. Formally, we show that an asset return follows a multiplicative random growth with exogenous input, which is well-known to be a generic power-law generating process, and which coul…
The relationship between price volatilty and a market extremum is examined using a fundamental economics model of supply and demand. By examining randomness through a microeconomic setting, we obtain the implications of randomness in the supply and demand, rather than assuming that price has randomness on an empirical …
India's 2020-21 GDP growth forecast is projected at 1.9% due to COVID-19.
problem Impact of COVID-19 on India's 2020-21 GDP growth forecast.
method Quarterly GVA estimates using capacity utilisation assumptions and 2019-20 data.
result India's GDP growth in 2020-21 is expected to be 1.9%.