Earticle

초록

영어

As a kind of renewable energy, solar power becomes more and more widely used as the power supply for large-scale datacenters to save the brown energy consumption and to reduce the overall cost. The prediction accuracy of solar energy generation becomes a fundamental issue in the research of how to efficiently manage the renewable energy resources. This paper explores the possible ways to predict the solar radiation intensity based on the assumption that it impacts the solar power generation proportionally. Through the analysis and research of photovoltaic power generation system, we explore the influence factors for solar radiation intensity, establish a relationship of solar radiation intensity and ambient temperature, time, humidity, wind speed in the forecasting model, and finally established the multivariate linear regression model and artificial neural network model. According to the two models, the environmental monitoring data measured at the Qinghai University are employed as the basis of the prediction of solar radiation intensity, and compared with the actual measurement data monitoring system. Experimental results show that, by using the BP neural network prediction model, the achieved accuracy is higher than other empirical model. The prediction method and good results provide a necessary foundation for future related research based on solar radiation values forecasts.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Preparatory Analysis
  3.1 Data Sources
  3.2 The Relationship between Solar Power Generation and Solar Radiation Intensity
  3.3 Impacting Factors of Solar Radiation Intensity
 4. Prediction Models based on Environmental Monitoring
  4.1 Multivariate Linear Regression Model
  4.2 Artificial Neural Network Modeling
 5. Performance Evaluation Experiments
  5.1 Prediction Results of Multivariate Linear Regression Model
  5.2 Artificial Neural Network Model
 6. Case Study
  6.1 Comparison of the Two Models
  6.2 Error Analysis for Two Models in Different Weather
 7. Conclusion
 Acknowledgement
 References

키워드

저자

• Guojing Zhang [ Department of Computer Technology and Applications, Qinghai University, Xining,Qinghai, 810016, China ]
• Xiaoying Wang [ 1Department of Computer Technology and Applications, Qinghai University, Xining,Qinghai, 810016, China ] Corresponding Author
• Zhihui Du [ Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University,Beijing, 100084, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605