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A Salient Object Detection Algorithm Based on Region Merging and Clustering Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Environmental Parameters Analysis and Power Prediction for Photovoltaic Power Generation Based on Ensembles of Decision Trees

Authors : Shuai Zhang, Hongwei Dai, Aizhou Yang, Zhongzhi Shi

Published in: Intelligent Information Processing X

Publisher: Springer International Publishing

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Abstract

Due to the influence of solar irradiation, temperature and other environmental factors, the output power of photovoltaic power generation has great randomness and randomness discontinuity. In this paper, a method for analyzing environment data related photovoltaic power generation based on ensembles of decision trees algorithm is studied. Firstly, the characteristics of environmental factors of photovoltaic power generation are analyzed by K-means clustering. And then the corresponding cluster label is assigned. Furthermore, the Radom Forests is combined to build a model. Finally, the method is validated by given data above from a real project. The results show that the proposed method can provide reference for the forecasting of photovoltaic power.

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previous chapter Multi-label Classification of Short Text Based on Similarity Graph and Restart Random Walk Model
next chapter The Conjugate Entangled Manifold of Space–Time Induced by the Law of Unity of Contradiction
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Metadata
Title
Environmental Parameters Analysis and Power Prediction for Photovoltaic Power Generation Based on Ensembles of Decision Trees
Authors
Shuai Zhang
Hongwei Dai
Aizhou Yang
Zhongzhi Shi
Copyright Year
2020
Book
Intelligent Information Processing X

Print ISBN: 978-3-030-46930-6

Electronic ISBN: 978-3-030-46931-3

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-46931-3_8

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