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Adaptive Solar Power Forecasting based on Machine Learning Methods

Department of Electrical Engineering, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 210016, China
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(11), 2224;
Received: 19 September 2018 / Revised: 20 October 2018 / Accepted: 7 November 2018 / Published: 12 November 2018
(This article belongs to the Special Issue Applications of Artificial Neural Networks for Energy Systems)
PDF [716 KB, uploaded 12 November 2018]


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. View Full-Text
Keywords: adaptive solar power forecasting; machine learning; k-means; BPNN; LARS adaptive solar power forecasting; machine learning; k-means; BPNN; LARS

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Wang, Y.; Zou, H.; Chen, X.; Zhang, F.; Chen, J. Adaptive Solar Power Forecasting based on Machine Learning Methods. Appl. Sci. 2018, 8, 2224.

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