arXiv:2607. 16168v1 Announce Type: new Abstract: Residential short-term load forecasting (STLF) is challenging because household demand is heterogeneous, temporally variable, and shaped by diverse behavioural routines.
District heating energy hubs require reliable heat load forecasts for efficient operational scheduling. Conventional forecasting workflows train system-specific models on historical data, which can become burdensome when networks change through new consumers, retrofits, or changing operating regimes.
arXiv:2608. 20024v1 Announce Type: new Abstract: District heating energy hubs require reliable heat load forecasts for efficient operational scheduling.
LoaDiff is a diffusion-based generative model that produces year-long, sub-hourly smart‑meter electricity consumption time series. It can be conditioned on static household attributes like appliance ownership and dynamic factors such as calendar dates and outdoor temperature. Evaluations on three residential datasets show that LoaDiff generates realistic, diverse load profiles, limits memorization, retains useful information for downstream tasks, and responds coherently to conditioning changes.
arXiv:2607. 12730v1 Announce Type: cross Abstract: Smart-building load forecasters are often trained offline on dense, multivariate, high-frequency data, but deployment may provide only hourly, feature-limited inputs.
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...
arXiv:2606. 03358v1 Announce Type: new Abstract: Smart meter data can reveal sensitive socio-demographic characteristics of households, raising privacy concerns.
Aurora‑X is a billion‑parameter time‑series foundation model designed for extreme forecasting tasks. It employs a progressive curriculum that starts with channel‑independent pretraining, then adds cross‑variable dependencies, variable context and horizon lengths, and optional future covariates during mid‑training. A variable‑resolution post‑training stage allows adjustable temporal spans per token at inference, while a pattern‑guided mixture‑of‑experts expands capacity through sparse activation and expert specialization. An implicit quantile network head predicts arbitrary quantiles, enhancing probabilistic forecasting flexibility. Experiments on GIFT‑Eval, TIME, FEV‑Bench, TFB, and DAG‑Bench show state‑of‑the‑art performance against both pretrained TSFMs and task‑specific supervised models.
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.
The paper presents an empirical benchmark of nine deep learning models for smart meter energy forecasting, evaluating them on two public 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 while accuracy declines with longer horizons. The study also compares computational cost, showing lightweight models achieve similar accuracy to heavier ones, and notes that model choice matters less across most population segments.
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.
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.
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.