Marco Savino Piscitelli 
- (2024): A portable application framework for energy management and information systems (EMIS) solutions using Brick semantic schema. Dans: Energy and Buildings, v. 323 (novembre 2024).
- (2023): Leveraging graph convolutional networks for semi-supervised fault diagnosis of HVAC systems in data-scarce contexts. Dans: Building Simulation, v. 16, n. 8 (juin 2023).
- (2022): Sharing is caring: An extensive analysis of parameter-based transfer learning for the prediction of building thermal dynamics. Dans: Energy and Buildings, v. 276 (décembre 2022).
- (2022): Towards a self-tuned data analytics-based process for an automatic context-aware detection and diagnosis of anomalies in building energy consumption timeseries. Dans: Energy and Buildings, v. 270 (septembre 2022).
- (2017): Data analytics for occupancy pattern learning to reduce the energy consumption of HVAC systems in office buildings. Dans: Sustainable Cities and Society, v. 35 (novembre 2017).
- (2020): A data analytics-based tool for the detection and diagnosis of anomalous daily energy patterns in buildings. Dans: Building Simulation, v. 14, n. 1 (novembre 2020).
- (2017): Data mining for energy analysis of a large data set of flats. Dans: Proceedings of the Institution of Civil Engineers - Engineering Sustainability, v. 170, n. 1 (février 2017).
- (2020): Deep reinforcement learning to optimise indoor temperature control and heating energy consumption in buildings. Dans: Energy and Buildings, v. 224 (octobre 2020).
- (2020): Enhancing operational performance of AHUs through an advanced fault detection and diagnosis process based on temporal association and decision rules. Dans: Energy and Buildings, v. 226 (novembre 2020).
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