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.
Proposes a machine learning framework for more efficient economic dispatch.
Sparse oblique decision tree improves security rules for renewable power systems.
Modeling intraday dispatch for wind-battery assets to meet grid targets.
The energy transition is well underway in most European countries. It has a growing impact on electric power systems as it dramatically modifies the way electricity is produced. To ensure a safe and smooth transition towards a pan-European electricity production dominated by renewable sources, it is of paramount import…
GP CC-OPF solves uncertain power grid optimization with Gaussian Process.
We design a dispatch system to improve the peak service quality of video on demand (VOD). Our system predicts the hot videos during the peak hours of the next day based on the historical requests, and dispatches to the content delivery networks (CDNs) at the previous off-peak time. In order to scale to billions of vide…
Paper benchmarks and customizes energy forecasting methods.
Modern vehicle fleets, e.g., for ridesharing platforms and taxi companies, can reduce passengers' waiting times by proactively dispatching vehicles to locations where pickup requests are anticipated in the future. Yet it is unclear how to best do this: optimal dispatching requires optimizing over several sources of unc…
Year by year control of normal and emergency conditions of up-to-date power systems becomes an increasingly complicated problem. With the increasing complexity the existing control system of power system conditions which includes operative actions of the dispatcher and work of special automatic devices proves to be ins…
Efficient dispatching rule in manufacturing industry is key to ensure product on-time delivery and minimum past-due and inventory cost. Manufacturing, especially in the developed world, is moving towards on-demand manufacturing meaning a high mix, low volume product mix. This requires efficient dispatching that can wor…
Order dispatching and driver repositioning (also known as fleet management) in the face of spatially and temporally varying supply and demand are central to a ride-sharing platform marketplace. Hand-crafting heuristic solutions that account for the dynamics in these resource allocation problems is difficult, and may be…
Paper uses DRL for smart MG energy dispatch, improving stability and performance.
DiffOPF solves multi-valued OPF problems by sampling from system history.
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…
Paper proposes MAMRL for efficient energy dispatch in self-powered edge computing systems.
The potential of recovering the topology of a grid using solely publicly available market data is explored here. In contemporary whole-sale electricity markets, real-time prices are typically determined by solving the network-constrained economic dispatch problem. Under a linear DC model, locational marginal prices (LM…
New approach uses deep reinforcement learning for vehicle dispatching, reducing waiting times.
Develops Bayesian approach for end-to-end learning in stochastic optimization.
Deep RL learns effective job shop scheduling rules from raw features.
In distributed machine learning, data is dispatched to multiple machines for processing. Motivated by the fact that similar data points often belong to the same or similar classes, and more generally, classification rules of high accuracy tend to be "locally simple but globally complex" (Vapnik & Bottou 1993), we propo…
The paradox of the energy transition is that the low marginal costs of new renewable energy sources (RES) drag electricity prices down and discourage investments in flexible productions that are needed to compensate for the lack of dispatchability of the new RES. The energy transition thus discourages the investments t…
New findings challenge the importance of forecast accuracy in battery storage optimization, highlighting the role of rank correlation instead.
Grid security and open markets are two major smart grid goals. Transparency of market data facilitates a competitive and efficient energy environment, yet it may also reveal critical physical system information. Recovering the grid topology based solely on publicly available market data is explored here. Real-time ener…
DNN policies improve stochastic AC OPF for power grid optimization.
A learning-based algorithm optimizes admission control in a queuing system.
Improved forecast accuracy for energy systems through decision-focused fine-tuning.
We address the problem of assisting human dispatchers in operating power grids in today's changing context using machine learning, with theaim of increasing security and reducing costs. Power networks are highly regulated systems, which at all times must meet varying demands of electricity with a complex production sys…
This paper uses a diffusion model to forecast electrical loads with uncertainty.
Proposes a new method for predicting uncertain net electricity demand.
A new method solves complex hydroelectricity planning problems.
In this paper, we consider same-day delivery with vehicles and drones. Customers make delivery requests over the course of the day, and the dispatcher dynamically dispatches vehicles and drones to deliver the goods to customers before their delivery deadline. Vehicles can deliver multiple packages in one route but trav…
CoLA automates efficient numerical linear algebra for complex matrix structures.
A new method improves ridesharing efficiency using QMIX.
Model predicts BESS interactions and price impacts in energy markets.
Recent research on Software-Defined Networking (SDN) strongly promotes the adoption of distributed controller architectures. To achieve high network performance, designing a scheduling function (SF) to properly dispatch requests from each switch to suitable controllers becomes critical. However, existing literature ten…
Paper uses econometrics time series model with T-student Distribution for short-term load forecasting.
M4L-JMF tackles multi-typed objects learning, improving on M3L.
We evaluate the applicability of the generic Vickrey-Clarke-Groves (VCG) mechanism as an antimonopoly measure against a profit-maximizing producer with market power operating a portfolio of generating units at the centralized two-settlement energy market. The producer may indicate in its bid not only the altered cost f…
Optimal allocation of human effort to correct AI assessments in decision-making.
Efficient FPGA virtualization for deep learning reduces user isolation and overhead.
This paper describes Convex, a convex optimization modeling framework in Julia. Convex translates problems from a user-friendly functional language into an abstract syntax tree describing the problem. This concise representation of the global structure of the problem allows Convex to infer whether the problem complies …
Airlines optimize fuel loading with better flight time predictions.
Current economic theories miss most of economic dynamics.
We consider the problem of reconstructing the dynamic state matrix of transmission power grids from time-stamped PMU measurements in the regime of ambient fluctuations. Using a maximum likelihood based approach, we construct a family of convex estimators that adapt to the structure of the problem depending on the avail…
Modeling business cycles via collective risk fluctuations in economic agents' risk space.
'Ergodicity economics' is criticized as pseudoscience.
This paper presents a simple model to measure the relative economic growth of economic systems. The model considers S-Shaped patterns of economic growth that, represented with a linear model, measure how an economic system grows in comparison with another one. In particular, this model introduces an approach which indi…
Conventional economic analysis of stringent climate change mitigation policy generally concludes various levels of economic slowdown as a result of substantial spending on low carbon technology. Equilibrium economics however could not explain or predict the current economic crisis, which is of financial nature. Meanwhi…