A Deep Q-Learning based Smart Scheduling of EVs for Demand Response in Smart Grids

no code implementations • 5 Jan 2024 • Viorica Rozina Chifu, Tudor Cioara, Cristina Bianca Pop, Horia Rusu, Ionut Anghel

Economic and policy factors are driving the continuous increase in the adoption and usage of electrical vehicles (EVs).

Q-Learning Scheduling

Deep Learning for Forecasting the Energy Consumption in Public Buildings

no code implementations • 25 Jul 2022 • Viorica Rozina Chifu, Cristina Bianca Pop, Emil St. Chifu, Horatiu Barleanu

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.

Forecasting the Short-Term Energy Consumption Using Random Forests and Gradient Boosting

no code implementations • 25 Jul 2022 • Cristina Bianca Pop, Viorica Rozina Chifu, Corina Cordea, Emil Stefan Chifu, Octav Barsan

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.