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Giuseppe Peter Vanoli
Researcher at University of Molise
Publications - 163
Citations - 5025
Giuseppe Peter Vanoli is an academic researcher from University of Molise. The author has contributed to research in topics: Efficient energy use & Building envelope. The author has an hindex of 33, co-authored 141 publications receiving 3734 citations. Previous affiliations of Giuseppe Peter Vanoli include University of Sannio.
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Energy refurbishment of existing buildings through the use of phase change materials: Energy savings and indoor comfort in the cooling season
Fabrizio Ascione,Nicola Bianco,Rosa Francesca De Masi,Filippo de Rossi,Giuseppe Peter Vanoli +4 more
TL;DR: In this paper, an office building is analyzed, with reference to the entire cooling season (from May 1st to September 30th), in reliable conditions as regards building use, and thus internal gains, occupancy, activation of cooling systems.
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Green roofs in European climates. Are effective solutions for the energy savings in air-conditioning?
TL;DR: In this article, a large parametric analysis evaluates the technical and economical feasibility of green roofs applied to a modern office building, considering various vegetations and different external coatings, and shows that green roofs show satisfactory performance if monthly rainfalls do not imply significant additional watering.
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Simulation-based model predictive control by the multi-objective optimization of building energy performance and thermal comfort
TL;DR: In this paper, a simulation-based model predictive control (MPC) procedure, consisting of the multi-objective optimization of operating cost for space conditioning and thermal comfort, is proposed.
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Energy retrofit of historical buildings: theoretical and experimental investigations for the modelling of reliable performance scenarios
TL;DR: A multi-criteria approach for the energy refurbishment of historical buildings, proposing methodologies for the performance analysis, coupling several experimental and numerical studies, is suggested to evidence a best-practice specified for the Italian territorial context.
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Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach
TL;DR: In this paper, two families of ANNs are generated: the existing building stock (as is), the renovated stock in presence of energy retrofit measures (ERMs), which are generated in MATLAB ® by using the outcomes of EnergyPlus simulations as targets for networks' training and testing.