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Paolo Sdringola

Researcher at University of Perugia

Publications -  30
Citations -  886

Paolo Sdringola is an academic researcher from University of Perugia. The author has contributed to research in topics: Renewable energy & Life-cycle assessment. The author has an hindex of 14, co-authored 26 publications receiving 740 citations.

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Solar-powered cooling systems: Technical and economic analysis on industrial refrigeration and air-conditioning applications

TL;DR: In this article, the authors analyzed the technical and economic feasibility of solar absorption cooling systems, designed for two different application fields: industrial refrigeration and air conditioning, by considering the refrigeration requirements of a company, which works in meat manufacturing, and the heating and cooling demands of a hotel located in a tourist town in Italy.
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Life Cycle Assessment of a ground-mounted 1778 kWp photovoltaic plant and comparison with traditional energy production systems

TL;DR: In this article, a ground-mounted 1778.48kWp photovoltaic plant, realized by TerniEnergia S.p.A. and located in Marsciano (Perugia, Italy), is examined using the methodology of Life Cycle Assessment (LCA), which allows to consider all stages of the life cycle, from the extraction of raw materials to the plant's disposal (from a cradle to grave perspective).
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MCFC-based CO2 capture system for small scale CHP plants

TL;DR: In this paper, the authors developed a feasible system design for medium scale cogeneration plants which are not considered economically and technically interesting for existing technologies for carbon capture, but are increasing in numbers with respect to large size power plants.
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Life Cycle Assessment of a passive house in a seismic temperate zone

TL;DR: In this article, the authors presented the results of a detailed LCA study of a low-energy consumption building (thermal energy for heating equal to 11kWh/m2 year) located in Perugia, Italy, according to European ISO 14040 and 14044.
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Analyzing wind turbine directional behavior: SCADA data mining techniques for efficiency and power assessment

TL;DR: This work deals with SCADA data analysis methods for assessing the importance of how wind turbines align in patterns to the wind direction, and deals with the most common collective phenomenon causing clusters of turbines behaving as a whole, rather than as a collection of individuality: wake effects.