Wind Farm Feasibility Analysis with Virtual Farms

2022-8
Narin, Eren
The negative impacts of global warming are felt increasingly every day, and how humanity manages energy is one of the most potent causes of global warming. In the era of increasing utilization and importance of renewable energy resources, faster and more accurate decision-making becomes crucial against the chaotic nature of weather. At the top of the decision list, finding feasible and profitable locations to install renewable energy farms presents another challenge. However, typical renewable energy farm feasibility analysis approaches include time-consuming and costly processes. Due to the nature of wind, wind farm feasibility analysis is the most challenging problem among other renewable energy resources, and the lack of data makes this process harder. To overcome this and facilitate the wind farm feasibility analysis process, we have proposed a Generative Adversarial Network (GAN) based novel approach. We have used a limited amount of real historical data and augmented this data for new locations by using geospatial and time-series information. After that, long-term productions with a neural network-based prediction model are predicted by using this synthetic data. With this approach, we not only provide a faster solution for feasibility analysis but also propose a novel geospatial time-series data generation model. Results show that synthetic data that preserves geospatial and time-series characteristics of the original data can be used for long-term feasibility analysis for a selected location and a specified time interval.

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Citation Formats
E. Narin, “Wind Farm Feasibility Analysis with Virtual Farms,” M.S. - Master of Science, Middle East Technical University, 2022.