A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New method assesses energy storage value beyond cost reduction.
A new energy-efficient pruning method for federated learning.
Bitcoin mining costs remain stable despite increased activity.
A model is presented in this work for simulating endogenously the evolution of the marginal costs of production of energy carriers from non-renewable resources, their consumption, depletion pathways and timescales. Such marginal costs can be used to simulate the long term average price formation of energy commodities. …
New pricing framework allocates costs of operating reserves and transmission.
New algorithm reduces costs in wind energy systems by minimizing decision changes.
New system assigns vehicles to routes for cost and energy efficiency.
Improves energy efficiency of neuromorphic hardware by optimizing memory organization and encoding schemes.
Residential homes constitute roughly one-fourth of the total energy usage worldwide. Providing appliance-level energy breakdown has been shown to induce positive behavioral changes that can reduce energy consumption by 15%. Existing approaches for energy breakdown either require hardware installation in every target ho…
We introduce a new online learning framework where, at each trial, the learner is required to select a subset of actions from a given known action set. Each action is associated with an energy value, a reward and a cost. The sum of the energies of the actions selected cannot exceed a given energy budget. The goal is to…
Poisson variational autoencoders introduce a metabolic cost term that penalizes high baseline activity.
E2GC optimizes energy efficiency in DNNs by balancing computational and data movement costs.
We show that different rates should be used for borrowing and discount rates, and that the risk-free rate should be used for discounting when assessing and comparing the cost of energy accross diffferent producers and technologies, on the example of photovoltaics. Recent quantitative models using the same rate for borr…
Study links cognitive effort to thermodynamic principles, optimizing decision-making.
An increase in energy production from renewable energy sources is viewed as a crucial achievement in most industrialized countries. The higher variability of power production via renewables leads to a rise in ancillary service costs over the power system, in particular costs within the electricity balancing markets, ma…
SmartDeal reduces energy and storage costs for deep neural networks.
Paper proposes MAMRL for efficient energy dispatch in self-powered edge computing systems.
Domain Adaptation in 6G wireless networks: When is it green?
Using Intel's Loihi neuromorphic research chip and ABR's Nengo Deep Learning toolkit, we analyze the inference speed, dynamic power consumption, and energy cost per inference of a two-layer neural network keyword spotter trained to recognize a single phrase. We perform comparative analyses of this keyword spotter runni…
Optimizes renewable energy mix to meet carbon-free targets at lowest cost.
Study examines barriers to grid-connected battery systems in Spain, finding high cycle cost remains main obstacle.
This paper forecasts renewable energy prospects in South America through cross-border interconnection.
Most cryptocurrencies rely on Proof-of-Work (PoW) "mining" for resistance to Sybil and double-spending attacks, as well as a mechanism for currency issuance. Hashcash PoW has successfully secured the Bitcoin network since its inception, however, as the network has expanded to take on additional value storage and transa…
Study optimizes SREC generation and trading in solar energy markets.
Convolutional neural networks (CNNs) have been increasingly deployed to edge devices. Hence, many efforts have been made towards efficient CNN inference in resource-constrained platforms. This paper attempts to explore an orthogonal direction: how to conduct more energy-efficient training of CNNs, so as to enable on-de…
New proof of energy functional monotonicity via geodesics in measure space.
The grid integration of intermittent Renewable Energy Sources (RES) causes costs for grid operators due to forecast uncertainty and the resulting production schedule mismatches. These so-called profile service costs are marginal cost components and can be understood as an insurance fee against RES production schedule u…
Paper benchmarks and customizes energy forecasting methods.
Model predicts climate change's impact on real estate prices.
Traditional centralized energy systems have the disadvantages of difficult management and insufficient incentives. Blockchain is an emerging technology, which can be utilized in energy systems to enhance their management and control. Integrating token economy and blockchain technology, token economic systems in energy …
This study prioritizes temporal resolution over spatial in energy systems models due to higher influence.
This paper optimizes a power-to-heat system using reinforcement learning for cost minimization under uncertain conditions.
Improves generative models for cost-sensitive decisions.
Ownership cost calculation plays an important role in optimal operation of distributed energy resources (DERs) and microgrids (MGs) in the future power system, known as smart grid. In this paper, a general framework for ownership cost calculation is proposed using uncertainty and risk analyses. Four ownership cost calc…
A new stochastic method handles ensemble creation with cost constraints.
Distributed, controllable energy storage devices offer several benefits to electric power system operation. Three such benefits include reducing peak load, providing standby power, and enhancing power quality. These benefits, however, are only realized during peak load or during an outage, events that are infrequent. T…
The free energy functional has recently been proposed as a variational principle for bounded rational decision-making, since it instantiates a natural trade-off between utility gains and information processing costs that can be axiomatically derived. Here we apply the free energy principle to general decision trees tha…
New attacks exploit neural network energy and latency, increasing costs by 10-200x.
SmartExchange trades memory for computation in neural networks.
The problem of continuous inverse optimal control (over finite time horizon) is to learn the unknown cost function over the sequence of continuous control variables from expert demonstrations. In this article, we study this fundamental problem in the framework of energy-based model, where the observed expert trajectori…
This study optimizes energy storage scheduling under price uncertainty, balancing risk and reward.
Designing energy-efficient networks is of critical importance for enabling state-of-the-art deep learning in mobile and edge settings where the computation and energy budgets are highly limited. Recently, Liu et al. (2019) framed the search of efficient neural architectures into a continuous splitting process: it itera…
Energy savings for DNN inference on resource-constrained devices.
A brisk building boom of hydropower mega-dams is underway from China to Brazil. Whether benefits of new dams will outweigh costs remains unresolved despite contentious debates. We investigate this question with the "outside view" or "reference class forecasting" based on literature on decision-making under uncertainty …
Paper optimizes neural network layers to reduce energy usage without sacrificing accuracy.
Quantum computing offers energy savings over classical computing.
A new algorithm for estimating continuous entropic barycenters under arbitrary costs.
We develop a general Minmax procedure in Euclidian spaces for constructing Willmore surfaces of non zero indices. We implement this procedure to the Willmore Minmax Sphere Eversion in the 3 dimensional euclidian space. We compute the cost of the Sphere eversion in terms of Willmore energies of Willmore Spheres in ${\R}…