arXiv AI By Nikolaos A. Efkarpidis, Despoina Kothona, Georgios C. Christoforidis

Comparative review of hybrid forecasting models for short-term prediction of building thermal load

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arXiv AI
Oct 12

Machine Learning and Time-Series Foundation Models for Day-Ahead Electricity Forecasting in a University Building

The study benchmarks various forecasting models—including statistical, machine‑learning, recurrent neural networks, and pretrained TimesFM—for day‑ahead electricity demand in a university building. TimesFM 3.0 with weather covariates achieves the lowest overall mean absolute error, outperforming a weekly seasonal‑naive baseline by about 43%, while XGBoost remains competitive and requires only local fitting. The LSTM model takes longer to train without improving accuracy, and TimesFM’s performance is robust even without covariates, though covariates slightly improve holiday forecasts.

By Amey Vasulkar, Samuel Kernan Freire, Arie-Willem de Leeuw, Tadeo-Baldiri Salcedo-Rahola
arXiv Machine Learning
Aug 11

Full-Feature versus Limited-Input Machine Learning for Residential Energy Estimation: A Comparative Analysis of RECS and ResStock Under Realistic Input Constraints

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
Hugging Face Trending Papers
Aug 11

XGBoost "is all you need": the case of forecasting transmitted heat energy in District Heating Systems

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.