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Wind energy generation proliferated over the past decades, introducing unique challenges and opportunities for failure prediction, operation and maintenance. Decision-makers are continuously looking into new methods to infer failure mechanisms and behaviors of wind turbine components to detect and intervene in the failures before they happen. Evidently, degradation modeling and prognosis become engaging topics for researchers and practitioners to prevent catastrophic failures. Prognostics-driven approaches predict the time of failure for the components (e.g., predicting remaining useful life), which provides significant insights for scheduling of operations and maintenance activities. Integrating these prognostics-driven insights into wind farm operations and maintenance presents a substantial challenge, demanding careful consideration of numerous factors such as accessibility, crew routing, and spare part logistics. This study provides state-of-the-art review for degradation modeling, prognosis, and prognostics-driven maintenance techniques for wind energy systems. The discussed techniques align with the United Nations’ sustainable development goals, in particular Goal 7 (Affordable and Clean Energy), by enhancing effectiveness and sustainability of wind energy operations. This work also showcases open research questions related to degradation modeling, prognosis, and prognostics-driven maintenance. 

Summary:   This repository contains the datasets used in the 2025 PG&E Energy Analytics Challenge on Electricity Load Forecasting, co-organized by the IISE Energy Systems Division and the INFORMS Quality, Statistics, and Reliability Section, and graciously sponsored by the Pacific Gas & Electric Company (PG&E).  The repository contains two files: Train.xlsx: Contains three years of hourly electric loads from San Diego, California (Years 2020-2022), as well as exogenous weather information at five neighboring sites within the San Diego area.    Test.xlsx: Contains one year of hourly electric loads from San Diego, California (Year 2023), as as well as exogenous weather information at five neighboring sites within the San Diego area.    Background:  This competition aimed to predict electricity loads for a specific location within the CAISO system. Accurate load forecasting is critical for managing electricity distribution within California’s diverse and dynamic energy market. Load patterns can vary significantly due to factors such as weather conditions, local supply and demand, and the mix of nearby energy generation sources.   During the competition, the specific location and the actual years were not disclosed to the participants. Participants were then asked to generate a year’s worth of load forecasts using historical load values and exogeneous weather information. Predictor data were not allowed to be taken from future time periods: When predicting the load values at a particular day, the model was allowed to only use predictor variable information from that time period or before. For example, if a team is predicting the load for Day 5 Hour 3 in Year 3, the model can only take in predictor values from Day 5 Hour 3 in Year 3, or before. Furthermore, during the competition, the load information for the test set (highlighted in yellow in the test data file) was reserved from all participants, who were asked to submit their predictions for the full year.    This is the second offering of the PG&E Energy Analytics challenge, complementing the first offering in 2024 which focused on electricity price forecasting [Results of the 2024 challenge: Aziz Ezzat, A., Mansouri, M., Yildirim, M., & Fang, X. (2025). IISE PG&E Energy Analytics Challenge 2024: Forecasting day-ahead electricity prices. IISE Transactions, 1–13. https://doi.org/10.1080/24725854.2024.2447049].