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David Chèze

Researcher at CEA LITEN

Publications -  19
Citations -  231

David Chèze is an academic researcher from CEA LITEN. The author has contributed to research in topics: Renewable energy & Heat pump. The author has an hindex of 6, co-authored 17 publications receiving 190 citations.

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Analysis of system improvements in solar thermal and air source heat pump combisystems

TL;DR: In this article, a reference solar thermal and air source heat pump combisystem was defined and modelled based on products available on the market and a singular economic cash flow analysis was carried out and the "additional investment limit" of each system variation was determined for a range of economic boundary conditions.
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Statistical Modeling for Real Domestic Hot Water Consumption Forecasting

TL;DR: The real domestic hot water consumptions from single family houses equipped with solar hot water tank is studied to understand and forecast the daily needs of inhabitants and an adaptive time series model is proposed which does not require strong a priori and computational time.
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Development of a 5 kW Cooling Capacity Ammonia-water Absorption Chiller for Solar Cooling Applications

TL;DR: In this paper, a thermally driven ammonia-water absorption chiller of 5kW cooling capacity for solar cooling applications was developed in an industrial perspective with a goal of overall compactness and using commercially available components.
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Dynamic whole system testing of combined renewable heating systems – The current state of the art

TL;DR: The objective is to evaluate the energetic performance of combined renewable heating systems that supply space heat and domestic hot water for single family houses and to investigate the dynamic behaviour, component behaviour, and components of these systems.
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Numerical and experimental results of a novel and generic methodology for energy performance evaluation of thermal systems using renewable energies

TL;DR: In this paper, the authors presented a generic methodology to evaluate the energy performance of thermal systems using artificial neural networks (ANNs) for building applications based on experimental data, which was applied to evaluate three different solar Combisystems (SCSs) combined with a gas boiler or a heat pump (HP) as an auxiliary system.