arXiv subjects

Jiaxiang Ji

Publications and source records attributed to Jiaxiang Ji.

4 recordsLinked to original sources

arXiv2026

A Deep Zero-Inflated Model of North Atlantic Right Whale Presence To Support Blue Economy Management in the U.S. East Coast

Effective modeling of endangered marine mammal species, such as the North Atlantic Right Whale, is critical for balancing marine conservation with the growing blue economy. Passive acoustic monitoring data collected by autonomous underwater vehicles provide new opportunities for localized marine species detection and oceanographic sensing, but introduce complex statistical challenges such as zero inflation, imperfect detection, and intricate dependence structures. In response, we propose the Deep Zero-Inflated Bernoulli (DeepZIB) model--a deep statistical method which jointly models latent species presence and conditional detection probabilities while learning complex habitat relationships from heterogeneous covariate information. We establish theoretical results on the model's structural properties and conduct simulation experiments to demonstrate its ability to recover underlying parameters and latent presence fields. Application to real-world passive acoustic monitoring data on the North Atlantic Right Whale along the U.S. East Coast demonstrates improved model adequacy and predictive performance in capturing the species' dynamic and spatially varying habitat. A key advantage of DeepZIB is its ability to generate high-resolution, spatially and temporally varying presence maps, providing valuable insights for targeted and risk-aware management of blue economy industries, ranging from offshore and marine energy, to fisheries management and maritime transport.

stat.AP↗

arXiv2026

Day-Ahead Electricity Price Forecasting Using a Multivariate Group Lasso Method

Electricity price signals in modern power systems exhibit complex dependence structures that render forecasting inherently challenging. Our analysis of real-world pricing signals from the California Independent System Operator (CAISO) reveals complex temporal group effects, whereby the influence of explanatory variables on electricity prices persists across consecutive blocks of time due to underlying economic and operational drivers. In response, we propose a multivariate statistical method based on a Group Lasso formulation to forecast the vector of day-ahead electricity prices, by leveraging multi-feature temporal group effects. Our approach is evaluated on two full years of electricity prices from CAISO, demonstrating considerable improvements in point and probabilistic forecast metrics compared to a wide array of statistical and deep learning methods. Theoretical and empirical analyses confirm the effectiveness of the proposed approach in modeling realistic group effects, maintaining both interpretability and low computational complexity. When retrospectively evaluated on test data from a recent international electricity price forecasting challenge, the proposed method ranked in second place, despite having access to significantly less information than competing approaches. Finally, the proposed method is independently validated against two operational electricity price forecasting systems in CAISO, demonstrating competitive predictive performance and practical relevance.

stat.AP↗

arXiv2026

Accessibility and Serviceability Assessment to Inform Offshore Wind Energy Development and Operations off the U.S. East Coast

The economic success of offshore wind energy projects relies on accurate projections of the construction, and operations and maintenance (O&M) costs. These projections must consider the logistical complexities introduced by adverse met-ocean conditions that can prohibit access to the offshore assets for sustained periods of time. In response, the goal of this study is two-fold: (1) to provide high-resolution estimates of the accessibility of key offshore wind energy areas in the United States (U.S.) East Coast--a region with significant offshore wind energy potential; and (2) to introduce a new operational metric, called serviceability, as motivated by the need to assess the accessibility of an offshore asset along a vessel travel path, rather than at a specific site, as commonly carried out in the literature. We hypothesize that serviceability is more relevant to offshore operations than accessibility, since it more realistically reflects the success and safety of a vessel operation along its journey from port to site and back. Our analysis reveals high temporal and spatial variations in accessibility and serviceability, even for proximate offshore locations. We also find that solely relying on numerical met-ocean data can introduce considerable bias in estimating accessibility and serviceability, raising the need for a statistical treatment that combines both numerical and observational data sources, such as the one proposed herein. Collectively, our analysis sheds light on the value of high-resolution met-ocean information and models in supporting offshore operations, including but not limited to future offshore wind energy developments.

stat.AP↗

arXiv2026

Economic and Reliability Value of Improved Offshore Wind Forecasting in Bulk Power Grid Operation: A Case Study of The New York Power Grid

This study investigates the economic and reliability benefits of improved offshore wind forecasting for grid operations along the U.S. East Coast. We introduce and evaluate a state-of-the-art, machine-learning-based offshore wind forecasting model tailored for this region by integrating its improved forecasts into a dynamic reserve procurement framework aligned with New York Independent System Operator (NYISO) practices to evaluate their economic value. To determine system-wide reserve needs, plant-specific reserves are aggregated. However, conventional methods overlook spatial correlation across sites, often leading to over procurement. To address this, we propose a risk-based reserve aggregation technique that leverages spatial diversification. Additionally, we evaluate the reliability improvements enabled by the enhanced offshore wind forecast. To evaluate the operational impact, we propose an operational resource adequacy framework that captures uncertainty from forecast errors and grid conditions. Using this framework, we quantify key reliability metrics under different offshore wind forecast scenarios. Using New York State as a case study, we find that the improved forecast enables more accurate reserve estimation, reducing procurement costs by 5.53% in 2035 scenario compared to a well-validated numerical weather prediction model. Applying the risk-based aggregation further reduces total production costs by 7.21%. From a reliability perspective, the improved forecasts lower the system Loss of Load Probability (LOLP) by approximately 19% in the 2035 scenario, highlighting its potential to enhance system reliability during real-time grid operations.

eess.SY↗