干旱区生态环境知识资源中心(Arid): Multi-step ahead forecasting of global solar radiation for arid zones using deep learning

Arid

DOI	10.1016/j.procs.2020.03.329
	Multi-step ahead forecasting of global solar radiation for arid zones using deep learning
	Chandola, Deeksha; Gupta, Harsh; Tikkiwal, Vinay Anand; Bohra, Manoj Kumar
通讯作者	Chandola, D (corresponding author), Jaypee Inst Informat Technol, Dept Elect & Commun Engn, Noida 201304, India.
会议名称	International Conference on Computational Intelligence and Data Science (ICCIDS)
会议日期	SEP 06-07, 2019
会议地点	NorthCap Univ, Gurugram, INDIA
英文摘要	Solar irradiance is fluctuating and interutittent in nature. In order to optimally farness solar energy, this variability needs to be accounted for. Forecasting solar radiation proves to be helpful in optimal design, and operation of solar energy based systems. This paper presents a solar irradiance forecasting scheme for multi-horizon forecasting of solar radiation considering 3/6/24 hours ahead scenarios. The proposed model uses long short tett!t memory network, considering the dependence between hours of the same day along with other variables such as: direct horizontal irradiance, direct notinal irradiance, relative humidity, dew point, temperature, wind speed, and wind direction. Solar radiations for four different locations of the Thar desert region have been forecasted. The model is optimized in tent's of number of neurons and is evaluated using standard statistical indicators: RMSE and MAPE. RMSE for four different locations varied in the range of 0.099 to 0.181, along with MAPE values, which range from 6.79% to 10.47%. Low values of RMSE and MAPE indicate the efficacy of the proposed model. (C) 2020 The Authors. Published by Elsevier B.V.
英文关键词	Solar irradiance Artificial neural network LSTM Forecasting RMSE
来源出版物	INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND DATA SCIENCE
ISSN	1877-0509
出版年	2020
卷号	167
页码	626-635
出版者	ELSEVIER SCIENCE BV
类型	Proceedings Paper
语种	英语
开放获取类型	gold
收录类别	CPCI-S
WOS记录号	WOS:000582710700066
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS研究方向	Computer Science
资源类型	会议论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/337085
作者单位	[Chandola, Deeksha; Tikkiwal, Vinay Anand] Jaypee Inst Informat Technol, Dept Elect & Commun Engn, Noida 201304, India; [Gupta, Harsh] Jaypee Inst Informat Technol, Noida 201304, India; [Bohra, Manoj Kumar] Manipal Univ, Sch Comp & Informat Technol, Jaipur 30300, Rajasthan, India
推荐引用方式 GB/T 7714	Chandola, Deeksha,Gupta, Harsh,Tikkiwal, Vinay Anand,et al. Multi-step ahead forecasting of global solar radiation for arid zones using deep learning[C]:ELSEVIER SCIENCE BV,2020:626-635.