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.
arXiv:2608. 11446v1 Announce Type: new Abstract: 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).
By Milan Zdravkovi\'c
arXiv:2606. 19118v1 Announce Type: new Abstract: Electricity markets are inherently complex systems characterised by strong nonlinearities, high-dimensional interactions, and increasing interdependence across regions.
By Antoine Pesenti, Aidan O'Sullivan
arXiv:2608. 09255v1 Announce Type: new Abstract: Residential energy estimates are often needed before detailed envelope characteristics, equipment efficiencies, infiltration, sensor, or billing data are available.
By Aditya Ramnarayan, Fatih Evren, Patti Gunderson, Samuel Rosenberg
arXiv:2510. 16898v2 Announce Type: replace-cross Abstract: Accurate prediction of electricity prices is crucial for stakeholders in the energy market, particularly for grid operators, energy producers, and consumers.
By Salih Salihoglu, Ibrahim Ahmed, Afshin Asadi
arXiv:2607. 19054v1 Announce Type: new Abstract: In this work, we incorporate first principle physics into the construction of data-driven methods by considering a model that accounts for the different sources of energy losses during vehicle operations.
By Hannes Nilsson, Rafael Basso, Bal\'azs Kulcs\'ar, Morteza Haghir Chehreghani