Adaptive Solar Power Forecasting based on Machine Learning Methods

Due to the existence of predicting errors in the power systems, such as solar power, wind power and load demand, the economic performance of power systems can be weakened accordingly. In this paper, we propose an adaptive solar power forecasting (ASPF) method for precise solar power forecasting, whi...

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Bibliographic Details
Published in:Applied sciences 2018-11, Vol.8 (11), p.2224
Main Authors: Wang, Yu, Zou, Hualei, Chen, Xin, Zhang, Fanghua, Chen, Jie
Format: Article
Language:English
Subjects:
Accuracy
adaptive solar power forecasting
Adaptive systems
Algorithms
Alternative energy
Artificial intelligence
Back propagation
BPNN
Clustering
Computer simulation
Data mining
Energy management
Forecasting
k-means
LARS
Learning algorithms
Machine learning
Neural networks
Researchers
Solar energy
Solar power
Statistical methods
Variables
Wind power
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Summary:Due to the existence of predicting errors in the power systems, such as solar power, wind power and load demand, the economic performance of power systems can be weakened accordingly. In this paper, we propose an adaptive solar power forecasting (ASPF) method for precise solar power forecasting, which captures the characteristics of forecasting errors and revises the predictions accordingly by combining data clustering, variable selection, and neural network. The proposed ASPF is thus quite general, and does not require any specific original forecasting method. We first propose the framework of ASPF, featuring the data identification and data updating. We then present the applied improved k-means clustering, the least angular regression algorithm, and BPNN, followed by the realization of ASPF, which is shown to improve as more data collected. Simulation results show the effectiveness of the proposed ASPF based on the trace-driven data.
ISSN:2076-3417
2076-3417
DOI:10.3390/app8112224