Abstract
A key goal of smart grid initiatives is significantly increasing the fraction of grid energy contributed by renewable sources and especially from solar power. One challenge with integrating solar power into the grid is that its power generation is stochastic and depends on various environmental factors. Thus, predicting future energy generation is important to moderate the overall energy requirements. In recent years, the use of machine learning approaches to solar power forecasting is becoming very popular. In this paper, a clustering based data segmentation approach is used to find natural subgrouping in the data. These subgroups are then used to construct forecasting models using various machine learning algorithms. The effectiveness of the approach is demonstrated by comparing the accuracy of clustering based forecasting to the standard forecasting models. The experimental results demonstrate that the proposed clustering based models produce more accurate models.
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Ahmad, W., Sahil, S., Mughal, A. (2018). Predicting Solar Intensity Using Cluster Analysis. In: Nguyen, N., Pimenidis, E., Khan, Z., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2018. Lecture Notes in Computer Science(), vol 11055. Springer, Cham. https://doi.org/10.1007/978-3-319-98443-8_50
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DOI: https://doi.org/10.1007/978-3-319-98443-8_50
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