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Die folgende Bibliografie enthält alle in dieser Datenbank indizierten Veröffentlichungen, die mit diesem Namen als Autor, Herausgeber oder anderweitig Beitragenden verbunden sind.

  1. Pombeiro, Henrique / Santos, Joao / Carreira, Paulo / Silva, Carlos (2019): Displaying data is not enough: Incorporating User Behavior Transformation in domestic reporting systems. In: Sustainable Cities and Society, v. 48 (Juli 2019).

    https://doi.org/10.1016/j.scs.2019.101451

  2. Pedro, Joana / Silva, Carlos / Pinheiro, Manuel Duarte (2018): Scaling up LEED-ND sustainability assessment from the neighborhood towards the city scale with the support of GIS modeling: Lisbon case study. In: Sustainable Cities and Society, v. 41 (August 2018).

    https://doi.org/10.1016/j.scs.2017.09.015

  3. Pombeiro, Henrique / Machado, Maria João / Silva, Carlos (2017): Dynamic programming and genetic algorithms to control an HVAC system: Maximizing thermal comfort and minimizing cost with PV production and storage. In: Sustainable Cities and Society, v. 34 (Oktober 2017).

    https://doi.org/10.1016/j.scs.2017.05.021

  4. Azevedo, Luís / Gomes, Ricardo / Silva, Carlos (2019): Influence of model calibration and optimization techniques on the evaluation of thermal comfort and retrofit measures of a Lisbon household using building energy simulation. In: Advances in Building Energy Research, v. 15, n. 5 (April 2019).

    https://doi.org/10.1080/17512549.2019.1654916

  5. Gomes, Ricardo / Ferreira, Ana / Azevedo, Luís / Costa Neto, Rui / Aelenei, Laura / Silva, Carlos (2018): Retrofit measures evaluation considering thermal comfort using building energy simulation: two Lisbon households. In: Advances in Building Energy Research, v. 15, n. 3 (September 2018).

    https://doi.org/10.1080/17512549.2018.1520646

  6. Pombeiro, Henrique / Santos, Rodolfo / Carreira, Paulo / Silva, Carlos / Sousa, João M. C. (2017): Comparative assessment of low-complexity models to predict electricity consumption in an institutional building: Linear regression vs. fuzzy modeling vs. neural networks. In: Energy and Buildings, v. 146 (Juli 2017).

    https://doi.org/10.1016/j.enbuild.2017.04.032

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