Journals

  1. Peppas, K., Tsolakis, A.C., Krinidis, S. and Tzovaras, D., 2020. Real-Time Physical Activity Recognition on Smart Mobile Devices Using Convolutional Neural Networks. Applied Sciences, 10(23), p.8482.

Conferences

  1. Peppas, K., Chouliara, A., Tsolakis, A., Krinidis, S. and Tzovaras, D., 2019, August. Redefining Micro-Moments for Improving Energy Behaviour: The SIT4Energy Approach. In 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (pp. 1811-1816). IEEE.
  2. Chouliara, A., Peppas, K., Tsolakis, A.C., Vafeiadis, T., Krinidis, S. and Tzovaras, D., 2019, September. Occupancy Inference Through Energy Consumption Data: A Smart Home Experiment. In International Conference on Computer Vision Systems (pp. 670-679). Springer, Cham.
  3. Grimaldo, A.I. and Novak, J., 2019, September. User-Centered Visual Analytics Approach for Interactive and Explainable Energy Demand Analysis in Prosumer Scenarios. In International Conference on Computer Vision Systems (pp. 700-710). Springer, Cham.
  4. Kalamaras, I., Tsolakis, A. C., Chouliara, A., Krinidis, S., & Tzovaras, D. (2020, September). Optimal Recommendation Strategy Identification towards Energy Efficiency in Tertiary Buildings. In 2020 5th International Conference on Smart and Sustainable Technologies (SpliTech) (pp. 1-6). IEEE.
  5. Grimaldo, A. I., & Novak, J. (2020). Combining Machine Learning with Visual Analytics for Explainable Forecasting of Energy Demand in Prosumer Scenarios. Procedia Computer Science, 175, 525-532.

Posters

      1. Grimaldo, A.I. and Novak, J., Adaptive incentives and context aware triggering for personalized recommendations for energy consumption bahaviour.
      2. Grimaldo, A.I. and Novak, J., Combining Machine Learning with Visual Analytics for Explainable Forecasting of Energy Demand in Prosumer Scenarios