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The following bibliography contains all publications indexed in this database that are linked with this name as either author, editor or any other kind of contributor.

  1. Coraci, Davide / Brandi, Silvio / Hong, Tianzhen / Capozzoli, Alfonso (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 (January 2024).

    https://doi.org/10.1007/s12273-024-1109-6

  2. Nagy, Zoltan / Henze, Gregor / Dey, Sourav / Arroyo, Javier / Helsen, Lieve / Zhang, Xiangyu / Chen, Bingqing / Amasyali, Kadir / Kurte, Kuldeep / Zamzam, Ahmed / Zandi, Helia / Drgoňa, Ján / Quintana, Matias / McCullogh, Steven / Park, June Young / Li, Han / Hong, Tianzhen / Brandi, Silvio / Pinto, Giuseppe / Capozzoli, Alfonso / Vrabie, Draguna / Berges, Mario / Nweye, Kingsley / Marzullo, Thibault / Bernstein, Andrey (2023): Ten questions concerning reinforcement learning for building energy management. In: Building and Environment, v. 241 (August 2023).

    https://doi.org/10.1016/j.buildenv.2023.110435

  3. Brandi, Silvio / Fiorentini, Massimo / Capozzoli, Alfonso (2022): Comparison of online and offline deep reinforcement learning with model predictive control for thermal energy management. In: Automation in Construction, v. 135 (March 2022).

    https://doi.org/10.1016/j.autcon.2022.104128

  4. Piscitelli, Marco Savino / Brandi, Silvio / Capozzoli, Alfonso / Xiao, Fu (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).

    https://doi.org/10.1007/s12273-020-0650-1

  5. Brandi, Silvio / Piscitelli, Marco Savino / Martellacci, Marco / Capozzoli, Alfonso (2020): Deep reinforcement learning to optimise indoor temperature control and heating energy consumption in buildings. In: Energy and Buildings, v. 224 (October 2020).

    https://doi.org/10.1016/j.enbuild.2020.110225

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