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DOI	10.1016/j.apenergy.2020.115023
	Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing
	Theocharides, Spyros; Makrides, George; Livera, Andreas; Theristis, Marios; Kaimakis, Paris; Georghiou, George E.
通讯作者	Theocharides, S
来源期刊	APPLIED ENERGY
ISSN	0306-2619
EISSN	1872-9118
出版年	2020
卷号	268
英文摘要	A main challenge towards ensuring large-scale and seamless integration of photovoltaic systems is to improve the accuracy of energy yield forecasts, especially in grid areas of high photovoltaic shares. The scope of this paper is to address this issue by presenting a unified methodology for hourly-averaged day-ahead photovoltaic power forecasts with improved accuracy, based on data-driven machine learning techniques and statistical post-processing. More specifically, the proposed forecasting methodology framework comprised of a data quality stage, data-driven power output machine learning model development (artificial neural networks), weather clustering assessment (K-means clustering), post-processing output optimisation (linear regressive correction method) and the final performance accuracy evaluation. The results showed that the application of linear regression coefficients to the forecasted outputs of the developed day-ahead photovoltaic power production neural network improved the performance accuracy by further correcting solar irradiance forecasting biases. The resulting optimised model provided a mean absolute percentage error of 4.7% when applied to historical system datasets. Finally, the model was validated both, at a hot as well as a cold semi-arid climatic location, and the obtained results demonstrated close agreement by yielding forecasting accuracies of mean absolute percentage error of 4.7% and 6.3%, respectively. The validation analysis provides evidence that the proposed model exhibits high performance in both forecasting accuracy and stability.
英文关键词	Artificial neural networks Clustering Forecasting Machine learning Photovoltaic Performance
类型	Article
语种	英语
开放获取类型	Green Submitted
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000537357800026
WOS关键词	NUMERICAL WEATHER PREDICTION ; SOLAR
WOS类目	Energy & Fuels ; Engineering, Chemical
WOS研究方向	Energy & Fuels ; Engineering
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/324595
作者单位	[Theocharides, Spyros; Makrides, George; Livera, Andreas; Georghiou, George E.] Univ Cyprus, FOSS Res Ctr Sustainable Energy, PV Technol Lab, Dept Elect & Comp Engn, CY-1678 Nicosia, Cyprus; [Theristis, Marios] Sandia Natl Labs, POB 5800, Albuquerque, NM 87185 USA; [Kaimakis, Paris] Univ Cent Lancashire Cyprus, Univ Ave 12-14, CY-7080 Pyla, Cyprus
推荐引用方式 GB/T 7714	Theocharides, Spyros,Makrides, George,Livera, Andreas,et al. Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing[J],2020,268.
APA	Theocharides, Spyros,Makrides, George,Livera, Andreas,Theristis, Marios,Kaimakis, Paris,&Georghiou, George E..(2020).Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing.APPLIED ENERGY,268.
MLA	Theocharides, Spyros,et al."Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing".APPLIED ENERGY 268(2020).