Zobrazeno 1 - 10
of 465
pro vyhledávání: '"Pop, Cristina"'
Autor:
POP, Cristina Anca
Publikováno v:
Revista Română de Terapia Tulburărilor de Limbaj şi Comunicare, Vol IV, Iss 2, Pp 25-35 (2018)
Externí odkaz:
https://doaj.org/article/3471aa7dca064351a6f8e49d5dbb56e1
Economic and policy factors are driving the continuous increase in the adoption and usage of electrical vehicles (EVs). However, despite being a cleaner alternative to combustion engine vehicles, EVs have negative impacts on the lifespan of microgrid
Externí odkaz:
http://arxiv.org/abs/2401.02653
The rise of renewables coincides with the shift towards Electrical Vehicles (EVs) posing technical and operational challenges for the energy balance of the local grid. Nowadays, the energy grid cannot deal with a spike in EVs usage leading to a need
Externí odkaz:
http://arxiv.org/abs/2311.14563
Privacy is important when dealing with sensitive personal information in machine learning models, which require large data sets for training. In the energy field, access to household prosumer energy data is crucial for energy predictions to support e
Externí odkaz:
http://arxiv.org/abs/2309.10337
Lately, the energy communities have gained a lot of attention as they have the potential to significantly contribute to the resilience and flexibility of the energy system, facilitating widespread integration of intermittent renewable energy sources.
Externí odkaz:
http://arxiv.org/abs/2309.01418
Autor:
Anil, Rohan, Gadanho, Sandra, Huang, Da, Jacob, Nijith, Li, Zhuoshu, Lin, Dong, Phillips, Todd, Pop, Cristina, Regan, Kevin, Shamir, Gil I., Shivanna, Rakesh, Yan, Qiqi
For industrial-scale advertising systems, prediction of ad click-through rate (CTR) is a central problem. Ad clicks constitute a significant class of user engagements and are often used as the primary signal for the usefulness of ads to users. Additi
Externí odkaz:
http://arxiv.org/abs/2209.05310
Publikováno v:
2021 20th RoEduNet Conference: Networking in Education and Research (RoEduNet), 2021, pp. 1-6
In this paper we propose a Long Short-Term Memory Network based method to forecast the energy consumption in public buildings, based on past measurements. Our approach consists of three main steps: data processing step, training and validation step,
Externí odkaz:
http://arxiv.org/abs/2207.11953
Autor:
Pop, Cristina Bianca, Chifu, Viorica Rozina, Cordea, Corina, Chifu, Emil Stefan, Barsan, Octav
Publikováno v:
2021 20th RoEduNet Conference: Networking in Education and Research (RoEduNet), 2021, pp. 1-6
This paper analyzes comparatively the performance of Random Forests and Gradient Boosting algorithms in the field of forecasting the energy consumption based on historical data. The two algorithms are applied in order to forecast the energy consumpti
Externí odkaz:
http://arxiv.org/abs/2207.11952