Alfonso Capozzoli 
- : CityLearn v2: energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities. In: Journal of Building Performance Simulation.
- (2024): Effects of occupant thermostat preferences and override behavior on residential demand response in CityLearn. In: Energy and Buildings, v. 324 (Dezember 2024).
- (2024): A portable application framework for energy management and information systems (EMIS) solutions using Brick semantic schema. In: Energy and Buildings, v. 323 (November 2024).
- (2024): An innovative heterogeneous transfer learning framework to enhance the scalability of deep reinforcement learning controllers in buildings with integrated energy systems. In: Building Simulation, v. 17, n. 5 (Januar 2024).
- (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).
- (2023): Ten questions concerning reinforcement learning for building energy management. In: Building and Environment, v. 241 (August 2023).
- (2022): Experimental assessment of ground-truth faults in a typical single-duct dual-fan air-handling unit under Mediterranean climatic conditions: Impact scenarios of sensors’ offset and fans’ failure. In: Energy and Buildings, v. 275 (November 2022).
- (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).
- (2022): Comparison of online and offline deep reinforcement learning with model predictive control for thermal energy management. In: Automation in Construction, v. 135 (März 2022).
- (2021): Enhancing energy efficiency and comfort in buildings through model predictive control for dynamic façades with electrochromic glazing. In: Journal of Building Engineering, v. 43 (November 2021).
- (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).
- (2017): Thermal bridges in vacuum insulation panels at building scale. 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).
- (2015): Estimation models of heating energy consumption in schools for local authorities planning. In: Energy and Buildings, v. 105 (Oktober 2015).
- (2016): Experimental and numerical investigation of thermal bridging effects of jointed Vacuum Insulation Panels. In: Energy and Buildings, v. 111 (Januar 2016).
- (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).
- (2019): Development and evaluation of a comfort-oriented control strategy for thermal management of mixed-mode ventilated buildings. In: Energy and Buildings, v. 202 (November 2019).
- (2019): Analysis of the temperature dependence of the thermal conductivity in Vacuum Insulation Panels. In: Energy and Buildings, v. 183 (Januar 2019).
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