Abstract
Accurately forecasting solar power using the data from multiple sources is an important but challenging problem. Our goal is to combine two different physics model forecasting outputs with real measurements from an automated monitoring network so as to better predict solar power in a timely manner. To this end, we consider a new approach of analyzing large-scale multilevel models for computational efficiency. This approach features a division of the large-scale data set into smaller ones with manageable sizes, based on their physical locations, and fit a local model in each area. The local model estimates are then combined sequentially from the specified multilevel models using our novel bottom-up approach for parameter estimation. The prediction, on the other hand, is implemented in a top-down matter. The proposed method is applied to the solar energy prediction problem for the US Department of Energy’s SunShot Initiative.
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This report was prepared as an account of work sponsored by an agency of the US government. Neither the US government nor any agency thereof, nor any of their employees, makes any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represented that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the US government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the US government or any agency thereof.
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Hwang, Y., Lu, S., Kim, JK. (2018). Bottom-Up Estimation and Top-Down Prediction: Solar Energy Prediction Combining Information from Multiple Sources. In: Choi, D., et al. Proceedings of the Pacific Rim Statistical Conference for Production Engineering. ICSA Book Series in Statistics. Springer, Singapore. https://doi.org/10.1007/978-981-10-8168-2_1
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DOI: https://doi.org/10.1007/978-981-10-8168-2_1
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