arXiv:2604. 22328v2 Announce Type: replace-cross Abstract: Driven by the transition towards a climate-neutral energy system, accurate energy time series forecasting is critical for planning and operations.
By Marco Obermeier, Marco Pruckner, Florian Haselbeck, Andreas Zeiselmair
arXiv:2606. 20918v2 Announce Type: replace-cross Abstract: Accurate short-term electricity demand forecasting is critical for reliable power system operation, energy market planning, and infrastructure optimization.
By Reza Ghanavati, Behrooz Mosallaei
arXiv:2606. 02852v1 Announce Type: new Abstract: Accurate short-term forecasting of residential energy load and indoor temperature is essential for home energy management systems, grid-level demand response, and community energy efficiency efforts.
By Jainam Dhruva, Yousaf Raza, A. B. Siddique, Simone Silvestri
arXiv:2607. 10720v1 Announce Type: new Abstract: The accelerating shift toward low-carbon power systems, together with the widespread adoption of behind-the-meter technologies such as rooftop solar and electric vehicles, is placing new operational and analytical demands on electricity grids.
By Mohannad Takrouri, Nicolas M. Cuadrado A., Martin Tak\'a\v{c}
arXiv:2509. 05364v2 Announce Type: replace-cross Abstract: Residential buildings contribute significantly to energy use, health outcomes, and carbon emissions.
By Abdollah Baghaei Daemei
This paper presents a comparative study of two distinct approaches, XGBoost and Long-Short Term Memory (LSTM), for forecasting transmitted heat energy in District Heating Systems (DHS). The objective is to explore scenarios in which conventional ML algorithms demonstrate better performance over deep learning networks in time series forecasting and the associated benefits in terms of computational cost and environmental impact.