LASSO and LSTM Integrated Temporal Model for Short-Term Solar Intensity Forecasting

Affiliation of Author(s):自动化学院

Journal:IEEE INTERNET OF THINGS JOURNAL

Key Words:Internet of Things (IoT) k-means plus least absolute shrinkage and selection operator ( LASSO) long short term memory (LSTM) short-term solar power forecasting

Abstract: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 No.:2327-4662

Translation or Not:no

Date of Publication:2019-04-01

Co-author:Shen, Yinxing,Mao, Shiwen,CHEN XIN,Chen Xin,Zou, Hualei

Correspondence Author:Yu Wang

Date of Publication:2019-04-01