The paper presents an empirical benchmark of nine modern deep‑learning models for time‑series forecasting of smart‑meter energy consumption, evaluated on two publicly available datasets. It examines how historical input length, prediction horizon, and model architecture affect accuracy, finding that longer historical context improves performance up to a saturation point and that accuracy declines with longer horizons. The study also compares computational complexity, showing that lightweight architectures achieve similar performance to heavier models, and notes that model choice has limited impact across most demographic and household subgroups.
By Behnaz Kavoosighafi, Maria Eidenskog, Wiktoria Glad, Katerina Vrotsou
The paper presents a comparative study of six deep learning models—state-space, MLP, RNN, and Transformer-based architectures—for cross-border electricity price forecasting using publicly available data. It focuses on generalization across markets and evaluates performance under low-data target-market conditions (zero-shot, one-shot, few-shot) with a standardized dataset for the Germany‑Luxembourg bidding zone in 2024. Results show that N‑HiTS and NBEATSx perform competitively in limited‑data scenarios, while transformer models achieve comparable accuracy but require more adaptation and tuning, and that careful feature selection and hyperparameter tuning improve performance.
By Hadeer Elashhab, Sai Srijan Papineni, Marvin Dorn, Veit Hagenmeyer, Benjamin Sch\"afer
While publicly available electricity market data presents a valuable resource for forecasting research, the field lacks established benchmark datasets for standardized comparison. As a result, many st...
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
arXiv:2609.06656v1 Announce Type: cross
Abstract: Modern power systems are growing increasingly complex as they integrate diverse generation sources to meet rising demand, making accurate load foreca...
By Varsha Pendyala, Yiwei Fu, Weizhong Yan, Nurali Virani
arXiv:2512. 22702v2 Announce Type: replace Abstract: Deep learning models have grown popular in time series applications.
By Valentina Moretti, Ivan Marisca, Cesare Alippi, Andrea Cini
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
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:2603. 15506v2 Announce Type: replace-cross Abstract: We argue that the current practice of evaluating AI/ML time-series forecasting models, predominantly on benchmarks characterized by strong, persistent periodicities and seasonalities, obscures real progress by overlooking the performance of efficient classical methods.
By Raeid Saqur, Christoph Bergmeir, Blanka Horvath, Daniel Schmidt, Frank Rudzicz, Terry Lyons
The paper benchmarks a range of AI methods—conventional ML, ensemble learning, deep neural networks, recurrent architectures, Transformers, graph models, and hybrid ensemble deep learning—on three renewable energy datasets, including large‑scale wave energy converter (WEC) data and wind farm SCADA measurements. Tree ensembles, particularly Extra Trees, outperform traditional ML and neural predictors on structured WEC layout data, achieving a 63.7% MAE reduction over an MLP baseline. Spatial‑temporal graph networks (STGCN) and an RF‑BiLSTM hybrid further improve forecasting accuracy, with the hybrid model reaching an MAE of 150.5 kW, a 75% reduction over a standalone LSTM and 10% better than STGCN. The study concludes that no single architecture dominates; randomized ensembles excel for structured surrogate modeling, graph networks for explicit spatial interactions, and hybrid recurrent ensembles for combined nonlinear tabular and temporal dynamics.
The study evaluates crop‑yield forecasting methods for the 2012 Midwestern US drought, comparing non‑deep learning machine learning models with a deep learning model (VITA) using 16 meteorological predictors. It highlights challenges such as distributional dissimilarity between training and test data, spatial and temporal sparsity, and demonstrates that sample weighting and feature selection improve non‑deep learning models but not VITA. The work contrasts deep versus non‑deep learning approaches and shows how modifications can mitigate issues arising from extreme drought conditions.
By Shrey Gupta, Yi Ming, George Mohler
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