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This paper introduces an innovative framework for wind power prediction that focuses on the future of energy forecasting utilizing intelligent deep learning and strategic feature engineering. This research investigates the application of a state-of-the-art deep learning model for wind energy prediction to make extremely short-term forecasts using real-time data on wind generation from New South Wales, Australia. In contrast with typical approaches to wind energy forecasting, this model relies entirely on historical data and strategic feature engineering to make predictions, rather than relying on meteorological parameters. A hybrid feature engineering strategy that integrates features from several feature generation techniques to obtain the optimal input parameters is a significant contribution to this work. The model's performance is assessed using key metrics, yielding optimal results with a Mean Absolute Error (MAE) of 8.76, Mean Squared Error (MSE) of 139.49, Root Mean Squared Error (RMSE) of 11.81, R-squared score of 0.997, and Mean Absolute Percentage Error (MAPE) of 4.85%. Additionally, the proposed framework outperforms six other deep learning and hybrid deep learning models in terms of wind energy prediction accuracy. These findings highlight the importance of advanced data analysis for feature generation in data processing, pointing to its key role in boosting the precision of forecasting applications. [ABSTRACT FROM AUTHOR]

Many countries have started their transition to a net-zero economy. Lithium-ion batteries (LIBs) play an ever-increasing role towards this transition as a rechargeable energy storage medium. Initially, LIBs were developed for consumer electronics and portable devices but have seen dramatic growth in their use in electric vehicles (EVs) and via the gradual uptake in battery energy storage systems (BESSs) over the last decade. As such, critical metals (Li, Co, Ni, and Mn) and chemicals (polymers, electrolytes, Cu, Al, PVDF, LiPF6, LiBF4, and graphite) needed for LIBs are currently in great demand and are susceptible to global supply shortages. Dramatic increases in raw material prices, coupled with predicted exponential growth in global demand (e.g., United States graphite demand from 2022 7000 t to ~145,000 t), means that LIBs will not be sustainable if only sourced from raw materials. LIBs degrade over time. When their performance can no longer meet the requirement of their intended application (e.g., EVs in the 8–12 year range), opportunities exist to extract and recover battery materials for re-use in new batteries or to supply other industrial chemical sectors. This paper compares the challenges, barriers, opportunities, and successes of the United States of America and Australia as they transition to renewable energy storage and develop a battery supply chain to support a circular economy around LIBs. [ABSTRACT FROM AUTHOR]

This paper outlines a methodology to determine the amount of renewable energy that can be accommodated in a power system before adverse impacts such as over-voltage, over-loading and system instability occur. This value is commonly known as hosting capacity. This paper identifies when the transmission network local hosting capacity might be limited because of static and dynamic network limits. Thus, the proposed methodology can effectively be used in assessing new interconnection requests and provides an estimation of how much and where the new renewable generation can be located such that network upgrades are minimized. The proposed approach was developed as one of the components of the AUSTEn project, which was a three-year project to map Australia's tidal energy resource in detail and to assess its economic feasibility and ability to contribute to the country's energy needs. In order to demonstrate the effectiveness of the proposed approach, two wide area networks were developed in DIgSILENT PowerFactory based on actual Australian network data near two promising tidal resource sites. Then, the proposed approach was used to assess the local tidal hosting capacity. In addition, a complementary local hosting capacity analysis is provided to show the importance of future network upgrades on the locational hosting capaity. [ABSTRACT FROM AUTHOR]