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: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:2606. 17692v1 Announce Type: new Abstract: Accurate short-term electricity load forecasting is critical for the reliable and economic operation of modern power systems, under non-stationarity arising from weather variability, calendar effects, and evolving consumption patterns.
By Vansh Bansal
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: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:2609.06194v1 Announce Type: new
Abstract: Offshore wind turbines are widely used to generate renewable energy, but their maintenance can result in decreased efficiency due to forced shutdowns....
By Khivishta Boodhoo, Isaac Triguero, Josh Plumbly, Bruce Nicolson, Nicholas Watson
arXiv:2604. 26634v2 Announce Type: replace Abstract: Norway's electricity market is heavily dominated by hydropower, but the 2021-2022 energy crisis and stronger integration with Continental Europe have fundamentally altered price formation, reducing the reliability of forecasting models calibrated on historical data.
By My Thi Diem Phan, Trung Tuyen Truong, Hoai Phuong Ha, Dat Thanh Nguyen
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:2512.09360v2 Announce Type: replace
Abstract: The persistent volatility of construction material prices poses significant risks to cost estimation, budgeting, and project delivery, underscoring...
By Boge Lyu, Qianye Yin, Iris Denise Tommelein, Hanyang Liu, Karnamohit Ranka, Karthik Yeluripati, Junzhe Shi
arXiv:2608. 06001v1 Announce Type: new Abstract: Commercial grazing systems yield irregular livestock observations, which challenge cattle growth forecasting.
By Muhammad Riaz Hasib Hossain, Rafiqul Islam, Shawn R. McGrath, Md Zahidul Islam, David W. Lamb
arXiv:2511. 09789v3 Announce Type: replace Abstract: Short-term energy forecasting plays an important role in real-time operational decision-making, such as electricity market bidding and power system dispatch, where both numerical accuracy and correct directional signals are essential.
By Fulong Yao, Wanqing Zhao, Chao Zheng, Xiaofei Han