| Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2015: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2014: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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| Outline of Final Research Achievements |
Micrometeorological data, such as temperature, humidity, and wind speed, has a complicated correlation among different features, and its characteristics change variously with time. In this paper, we propose a new methodology for predicting micrometeorological data, sliding window-based support vector regression (SW-SVR) that involves a novel combination of SVR and ensemble learning. To represent complicated micrometeorological data easily, SW-SVR builds several SVRs specialized for each representative data group in various natural environments, such as different seasons and climates, and changes weights to aggregate the SVRs dynamically depending on the characteristics of test data. We implemented nitrogen absorption amount prediction control system as the prototype system and evaluated the prediction performance. The results demonstrated that SW-SVR reduced remarkably the prediction error of nitrogen absorbed amount compared with conventional machine learning and online learning.
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