The paper proposes a heterogeneous spatial‑temporal graph neural network (HSTGNN) to create virtual smart heat meters for district heating networks, addressing sparse instrumentation and sensor faults. It integrates network functional relationships and learns graph structures and temporal dynamics for flow, temperature, and pressure, enabling joint modeling of cross‑variable and spatial correlations. A new laboratory dataset from the Aalborg Smart Water Infrastructure Laboratory is introduced, and experiments show the HSTGNN outperforms existing baselines.
arXiv:2606. 00506v1 Announce Type: new Abstract: Energy consumption prediction is essential for efficient grid management, demand-side optimization, and sustainable energy planning.
arXiv:2606. 31804v1 Announce Type: new Abstract: Accurate energy demand forecasting is essential for the reliable operation and planning of modern sustainable energy systems.
arXiv:2605. 02524v2 Announce Type: replace Abstract: Accurate reconstruction of greenhouse climate variables from sparse sensor measurements is essential for intelligent environmental monitoring, automated climate control, and precision agriculture.
arXiv:2606. 23833v1 Announce Type: new Abstract: Terrestrial water storage (TWS) integrates snow, soil moisture, surface water, and groundwater and is a key indicator of how climate variability and human activity reshape the global water cycle.
arXiv:2606. 19138v1 Announce Type: new Abstract: Neural Controlled Differential Equations (NCDE) provide a powerful continuous-time framework for forecasting time series, but standard graph-based extensions typically learn spatial structure purely from data, even in settings where a directed graph structure is known a priori.
The paper introduces STOIC, a post‑hoc residual calibration framework that enhances uncertainty quantification for energy time series over graphs. STOIC combines spatial‑temporal graph neural network forecasts with in‑context learning from tabular foundation models to estimate feature‑conditional residual quantiles, thereby capturing both sequential and relational dependencies. It is designed as a conformal‑inspired method that does not require task‑specific retraining of the calibration model.
arXiv:2607. 01621v1 Announce Type: new Abstract: Fine-scale rainfall reconstruction is critical for urban flood modeling, but real rainfall sensing systems observe the field through incompatible spatial supports: gauges measure points, microwave links measure paths, and radar/satellite products measure gridded areas.
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.
This study presents a new mobile‑sensing dataset from Surat, India, capturing PM2.5 concentrations along with meteorological and land‑use variables. The authors model the data as a graph using two node‑definition strategies—uniform segmentation and DBSCAN clustering—and introduce a Spatially Attentive Graph Neural Network (SA‑GNN) that combines cluster‑specific GRUs with a Graph Attention Network to forecast fine‑grained, short‑term PM2.5 levels. SA‑GNN outperforms traditional LSTM, RNN, GRU, and ANN baselines, achieving an R² of 0.95, RMSE of 6.8, and MAE of 4.2 µg/m³ on the dataset.
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.
HiFiNet is a hierarchical fault identification framework for Wireless Sensor Networks that uses edge-based LSTM stacked autoencoders for initial temporal feature extraction and a Graph Attention Network to aggregate neighboring node information for refined classification. The approach captures both local temporal patterns and network-wide spatial dependencies, leading to higher accuracy, F1-score, and precision compared to existing methods. Experiments on synthetic datasets derived from the Intel Lab Dataset and NASA's MERRA-2 reanalysis data demonstrate HiFiNet’s robustness and its ability to balance diagnostic performance with energy efficiency.
arXiv:2606. 02038v1 Announce Type: cross Abstract: Reconstructing spatially continuous daily temperature fields from sparse observations is important for urban climate monitoring and heat-risk analysis, but practical deployments are limited by sensor budgets and spacing constraints.