Prévision à long terme de la réponse d'un stockage de chaleur sensible dans le sol

Desmons, J.Y.; Younès, Rached Ben

doi:10.1016/s0017-9310(96)00334-1

Cited by 11 publications

(9 citation statements)

References 2 publications

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“…We evoke the particular case where the temperature imposed on (St) is equal to the reference temperature T 0 supposed as uniform. Consequently, F( r ' ,t ) is null [6] . 8We study the case where the contact between the soil and the exchanger is perfect.…”

Section: Mathematical Formulation Of the Problemmentioning

confidence: 99%

“…Going to the original, the temperature of the exchanger surface (wall and base) is relied to the heat flux that escapes at any point from this surface [5,10,17] : (4) where: (5) The general solution of the Eq. (2) is the Green function: (6) We consider on (St) the case where the surface temperature is imposed and equal to T a ( r ,t):…”

Section: Mathematical Formulation Of the Problemmentioning

confidence: 99%

See 1 more Smart Citation

Simulation of Convective Heat-Transfer Coefficient in a Buried Exchanger

Mnasri¹,

Younès²,

Raddaoui³

et al. 2008

American J. of Applied Sciences

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This study presents analytical models allowing to study a forced convection laminar flow in non-established dynamic and thermic regimes. We treated a flow in a bitubular exchanger in permanent thermal contact with a semi-infinite medium, such as the ground. The wall temperature as well as the wall heat flux evolve in the course of time until a quasi-steady mode. The theoretical method is original because it uses Green's functions method to determine the analytical solutions of the heat propagation equation on the wall during the heating phase. These analytical solutions allow to identify the temperature distribution versus time. The complexity of the system geometry as well as the infinity of the medium surrounding the exchanger make the traditional methods of numerical resolution unable to solve the problem. We used, to solve it, the finite volume method coupled with the finite element method at the boundary. We studied the effect of Reynolds number, the fluid entry temperature and the transfer duration on the axial evolution of the heat transfer coefficient. We illustrated also the profile of the temperature field in the fluid medium

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Section: Mathematical Formulation Of the Problemmentioning

confidence: 99%

Section: Mathematical Formulation Of the Problemmentioning

confidence: 99%

Simulation of Convective Heat-Transfer Coefficient in a Buried Exchanger

Mnasri¹,

Younès²,

Raddaoui³

et al. 2008

American J. of Applied Sciences

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“…Traditionally, hourly forecasts with a lead time between one hour and seven days are required for the scheduling and control of power systems. Medium term load forecasting (MTLF) [7] covers the horizon from one month up to a few years ahead.…”

Section: Introductionmentioning

confidence: 99%

“…Long term load forecasting (LTLF) [7] considers load peaks and consumed energy, on a yearly basis, for several years ahead. For example, long-term forecasting is relevant for the planning of new electricity utilities, and inaccurate predictions have important financial costs.…”

Section: Introductionmentioning

confidence: 99%

Medium term load forecasting using ANFIS predictor

Dragomir

Gouriveau

et al. 2010

18th Mediterranean Conference on Control and Automation, MED'10

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Nowadays, there are huge ranges of energy market participants. Commercial success of this area actor depends on the ability to submit competitive predictions relative to energy balance trends Thus, it seems convenient to "anticipate" this parameter evolution in time in order to act consequently and resort to protective actions. In this context, this paper proposes a tool for energy balance prediction based on ANFIS (Adaptive Neuro Fuzzy Inference System). This neuro-fuzzy predictor is modified in order to obtain an accurate forecasting for medium term. The solutions are illustrated on a real application and take into account the known "future": the programmed actions.

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“…In particular, the different time horizons required by the electricity markets (e.g., day-ahead, hour-ahead) can be respectively associated to short term (STLF) (Hippert et al, 2001), medium term (MTLF) or with long term forecasting (LTLF) (Desons et al, 1997). For short term forecasting the information is sampled on an hourly (or half hourly) basis, or even a daily basis (for load peak prediction) so is defined as varying from a few minutes up to a few weeks ahead.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Renewable Energy Balance on Medium Term

Dragomir

Gouriveau

et al. 2010

IFAC Proceedings Volumes

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The general purpose of the paper is to explore the way of performing renewable energy balance predictions prognostics so that energy market actors can act consequently. Different aspects of forecasting are discussed to point out pragmatic challenges of this approach. An illustration, with real monitored data, based on a neuro-fuzzy predictor is given. The architecture of the artificial intelligence technique used for forecasting is modified in order to obtain accurate estimations for medium term.

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Prévision à long terme de la réponse d'un stockage de chaleur sensible dans le sol

Cited by 11 publications

References 2 publications

Simulation of Convective Heat-Transfer Coefficient in a Buried Exchanger

Simulation of Convective Heat-Transfer Coefficient in a Buried Exchanger

Medium term load forecasting using ANFIS predictor

Forecasting of Renewable Energy Balance on Medium Term

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