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arXiv research

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.

168,657 papers · 148 categories

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11223344 · Oct 201919922001200920172026
48 results for heating demand

Heat demand prediction is a prominent research topic in the area of intelligent energy networks. It has been well recognized that periodicity is one of the important characteristics of heat demand. Seasonal-trend decomposition based on LOESS (STL) algorithm can analyze the periodicity of a heat demand series, and decom…

2018-07-28abs ↗pdf ↗

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.

Using artificial neural network for the prediction of heat demand has attracted more and more attention. Weather conditions, such as ambient temperature, wind speed and direct solar irradiance, have been identified as key input parameters. In order to further improve the model accuracy, it is of great importance to und…

2018-08-02abs ↗pdf ↗

Model forecasts hourly electricity demand influenced by weather, socio-economic, and political factors.

problem Accurate hourly electricity demand forecasting in the face of multifaceted uncertainties.
method Interpretable probabilistic mid-term forecasting model using Generalized Additive Models (GAMs).
result Highlights vulnerability of countries to extreme weather scenarios under electric heating adoption.

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.

Adaptive probabilistic load forecasting improves performance in power systems.

problem Complexity of electricity load forecasting due to changing drivers and local generation.
method Adaptive probabilistic approach using Kalman filter and online gradient descent.
result Adaptive probabilistic forecasts improve performance in both point and probabilistic forecasting.

Study on uniquely determining thermal properties from boundary temperature and heat flux measurements.

problem Determine thermal conductivity and volumetric heat capacity from boundary measurements.
method Uniqueness proof for isotropic and anisotropic media under thermal diffusivity assumption.
result Uniqueness of thermal properties in all dimensions and up to a gauge in two dimensions.

We consider Laplacians acting on sections of homogeneous vector bundles over symmetric spaces. By using an integral representation of the heat semi-group we find a formal solution for the heat kernel diagonal that gives a generating function for the whole sequence of heat invariants. We show explicitly that the obtaine…

2007-01-17abs ↗pdf ↗

The heat kernel for the Cauchy-Riemann subLaplacian on S(2n+1) is derived in a manner which is completely analogous to the classical derivation of elliptic heat kernels. This suggests that the classical hamiltonian construction of elliptic heat kernels, with appropriate modifications, does yield heat kernels for subell…

2013-03-03abs ↗pdf ↗

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.

We consider Laplacians acting on sections of homogeneous vector bundles over symmetric spaces. By using an integral representation of the heat semi-group we find a formal solution for the heat kernel diagonal that gives a generating function for the whole sequence of heat invariants. We argue that the obtained formal s…

2007-08-01abs ↗pdf ↗

In this paper, we first give a direct proof for two recurrence relations of the heat kernels for hyperbolic spaces in \cite{DM}. Then, by similar computation, we give two similar recurrence relations of the heat kernels for spheres. Finally, as an application, we compute the diagonal of heat kernels for odd dimensional…

2018-07-16abs ↗pdf ↗

This paper describes results characterizing the range of the time-t heat operator on various manifolds, including Euclidean spaces, spheres, and hyperbolic spaces. The guiding principle behind these results is this: The functions in the range of the heat operator should be, roughly, those functions having an analytic c…

2004-09-17abs ↗pdf ↗

Study on biharmonic heat equation on manifolds with curvature constraints.

problem Analyzing entire solutions of biharmonic heat equation on manifolds.
method Exponential decay estimates for biharmonic heat kernel under Ricci curvature and noncollapsing conditions. Proving uniqueness criteria for Cauchy problem.
result Conservation law for biharmonic heat kernel and uniform L-infinity estimate for entire solutions.

The paper studies heat kernels on weighted Riemannian manifolds with curvature bounds.

problem Estimating heat kernels on weighted Riemannian manifolds with lower Ricci curvature bounds.
method Establishing parabolic Harnack inequalities, proving Gaussian bounds for heat kernels, and constructing Li-Yau-type gradient estimates.
result Gaussian upper and lower bounds for the heat kernel, Liouville theorem, uniqueness property, and eigenvalue bounds.

Study heat content in sub-Riemannian manifolds, obtaining asymptotic expansion.

problem Heat content in sub-Riemannian manifolds with non-characteristic domains.
method Fourth-order asymptotic expansion, combining rough boundary temperature and stochastic completeness.
result Obtained a fourth-order asymptotic expansion for relative heat content.

The paper develops heat kernel comparison theorems and applies them to spectral geometry.

problem Developing mathematical tools for spectral geometry.
method Established weighted heat kernel comparison theorems for manifolds with bounded radial curvatures.
result Two eigenvalue comparison theorems for the first Dirichlet eigenvalue of the Witten-Laplacian.

We develop a new method for the calculation of the heat trace asymptotics of the Laplacian on symmetric spaces that is based on a representation of the heat semigroup in form of an average over the Lie group of isometries and obtain a generating function for the whole sequence of all heat invariants.

2006-05-30abs ↗pdf ↗

We study new invariants of elliptic partial differential operators acting on sections of a vector bundle over a closed Riemannian manifold that we call the relativistic heat trace and the quantum heat traces. We obtain some reduction formulas expressing these new invariants in terms of some integral transforms of the u…

2016-11-11abs ↗pdf ↗

From the uniformization theorem, we know that every Riemann surface has a simply-connected covering space. Moreover, there are only three simply-connected Riemann surfaces: the sphere, the Euclidean plane, and the hyperbolic plane. In this paper, we collect the known heat kernels, or Green's functions, for these three …

2010-07-30abs ↗pdf ↗

In a 1991 paper by Buttig and Eichhorn, the existence and uniqueness of a differential forms heat kernel on open manifolds of bounded geometry was proven. In that paper, it was shown that the heat kernel obeyed certain properties, one of which was a relationship between the derivative of heat kernel of different degree…

2009-10-23abs ↗pdf ↗

This paper proposes an unsupervised learning method to solve heat equations on chips.

problem Critical need for solving heat transfer equations on chips for 5G and AI.
method Hybrid framework of Auto Encoder and Image Gradient for unsupervised learning.
result Framework can solve heat transfer problems with a single training process and predict unseen cases.

New heat semigroup characterizes Sobolev and BV spaces in Carnot groups.

problem Lack of explicit representations and symmetry in heat kernels in sub-Riemannian geometry.
method Establishes a new heat semigroup characterisation using integral decoupling property.
result Characterizes Sobolev and BV spaces in Carnot groups.