所属单位：自动化学院
发表刊物：IEEE INTERNET OF THINGS JOURNAL
关键字：Internet of Things (IoT) k-means plus least absolute shrinkage and selection operator ( LASSO) long short term memory (LSTM) short-term solar power forecasting
摘要：As a special form of the Internet of Things, smart grid is an Internet of both power and information, in which energy management is critical for making the best use of the power from renewable energy resources, such as solar and wind, while efficient energy management is hinged upon precise forecasting of power generation from renewable energy resources. In this paper, we propose a novel least absolute shrinkage and selection operator (LASSO) and long short term memory (LSTM) integrated forecasting model for precise short-term prediction of solar intensity based on meteorological data. It is a fusion of a basic time series model, data clustering, a statistical model, and machine learning. The proposed scheme first clusters data using k-means++. For each cluster, a distinctive forecasting model is then constructed by applying LSTM, which learns the nonlinear relationships and LASSO, which captures the linear relationship within the data. Simulation results with open-source datasets demonstrate the effectiveness and accuracy of the proposed model in short-term forecasting of solar intensity.
ISSN号：2327-4662
是否译文：否
发表时间：2019-04-01
合写作者：Shen, Yinxing,Mao, Shiwen,陈新,陈鑫,Zou, Hualei
通讯作者：王愈
发表时间：2019-04-01