Marco Savino Piscitelli
- (2023): Leveraging graph convolutional networks for semi-supervised fault diagnosis of HVAC systems in data-scarce contexts. In: Building Simulation, v. 16, n. 8 (Juni 2023).
- (2022): Sharing is caring: An extensive analysis of parameter-based transfer learning for the prediction of building thermal dynamics. In: Energy and Buildings, v. 276 (Dezember 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. In: Energy and Buildings, v. 270 (September 2022).
- (2017): Data analytics for occupancy pattern learning to reduce the energy consumption of HVAC systems in office buildings. In: Sustainable Cities and Society, v. 35 (November 2017).
- (2020): A data analytics-based tool for the detection and diagnosis of anomalous daily energy patterns in buildings. In: Building Simulation, v. 14, n. 1 (November 2020).
- (2017): Data mining for energy analysis of a large data set of flats. In: Proceedings of the Institution of Civil Engineers - Engineering Sustainability, v. 170, n. 1 (Februar 2017).
- (2020): Deep reinforcement learning to optimise indoor temperature control and heating energy consumption in buildings. In: Energy and Buildings, v. 224 (Oktober 2020).
- (2020): Enhancing operational performance of AHUs through an advanced fault detection and diagnosis process based on temporal association and decision rules. In: Energy and Buildings, v. 226 (November 2020).
Structurae kooperiert mit
