• Energy Research

This dataset contains very long minutely time series representing the wind power production of 339 wind farms in Australia. It was downloaded from the Australian Energy Market Operator (AEMO) online platform. {"references": ["Australian Energy Market Operator, 2020. https://aemo.com.au/", "Australian Energy Market Operator, 2020. Market Data NemWeb, http://www.nemweb.com.au/"]}

This dataset contains very long minutely time series representing the wind power production of 339 wind farms in Australia. It was downloaded from the Australian Energy Market Operator (AEMO) online platform. The original dataset contains missing values and they have been replaced by zeros. {"references": ["Australian Energy Market Operator, 2020. https://aemo.com.au/", "Australian Energy Market Operator, 2020. Market Data NemWeb, http://www.nemweb.com.au/"]}

This dataset contains very long minutely time series representing the wind power production of 339 wind farms in Australia. It was downloaded from the Australian Energy Market Operator (AEMO) online platform. The original dataset contains missing values and they have been replaced by zeros. {"references": ["Australian Energy Market Operator, 2020. https://aemo.com.au/", "Australian Energy Market Operator, 2020. Market Data NemWeb, http://www.nemweb.com.au/"]}

Predict+Optimize frameworks integrate forecasting and optimization to address real-world challenges such as renewable energy scheduling, where variability and uncertainty are critical factors. This paper benchmarks solutions from the IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling, focusing on forecasting renewable production and demand and optimizing energy cost. The competition attracted 49 participants in total. The top-ranked method employed stochastic optimization using LightGBM ensembles, and achieved at least a 2% reduction in energy costs compared to deterministic approaches, demonstrating that the most accurate point forecast does not necessarily guarantee the best performance in downstream optimization. The published data and problem setting establish a benchmark for further research into integrated forecasting-optimization methods for energy systems, highlighting the importance of considering forecast uncertainty in optimization models to achieve cost-effective and reliable energy management. The novelty of this work lies in its comprehensive evaluation of Predict+Optimize methodologies applied to a real-world renewable energy scheduling problem, providing insights into the scalability, generalizability, and effectiveness of the proposed solutions. Potential applications extend beyond energy systems to any domain requiring integrated forecasting and optimization, such as supply chain management, transportation planning, and financial portfolio optimization.

This dataset contains very long minutely time series representing the wind power production of 339 wind farms in Australia. It was downloaded from the Australian Energy Market Operator (AEMO) online platform. {"references": ["Australian Energy Market Operator, 2020. https://aemo.com.au/", "Australian Energy Market Operator, 2020. Market Data NemWeb, http://www.nemweb.com.au/"]}