Hourly Solar Radiation Forecasting Through Model Averaged Neural Networks and Alternating Model Trees

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• 关键词：Solar radiation ; Time series forecasting ; Artificial neural networks ; Decision trees
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9799
• 期：1
• 页码：737-750
• 全文大小：913 KB
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• 作者单位：Cameron R. Hamilton (18)
  Frederick Maier (18)
  Walter D. Potter (18)
  
  18. Institute for Artificial Intelligence, 30602, Athens, Georgia
  
• 丛书名：Trends in Applied Knowledge-Based Systems and Data Science
• ISBN：978-3-319-42007-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9799

文摘

The objective of the current study was to develop a solar radiation forecasting model capable of determining the specific times during a given day that solar panels could be relied upon to produce energy in sufficient quantities to meet the demand of the energy provider, Southern Company. Model averaged neural networks (MANN) and alternating model trees (AMT) were constructed to forecast solar radiation an hour into the future, given 2003–2012 solar radiation data from the Griffin, GA weather station for training and 2013 data for testing. Generalized linear models (GLM), random forests, and multilayer perceptron (MLP) were developed, in order to assess the relative performance improvement attained by the MANN and AMT models. In addition, a literature review of the most prominent hourly solar radiation models was performed and normalized root mean square error was calculated for each, for comparison with the MANN and AMT models. The results demonstrate that MANN and AMT models outperform or parallel the highest performing forecasting models within the literature. MANN and AMT are thus promising time series forecasting models that may be further improved by combining these models into an ensemble.