Short-term forecasting for Wind Speed based on machine learning using weather observation data<sup>†</sup>
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(2) 824. Hyeong-Se Jeong. 포 ᆼ ᅮ ᆨ 스 ᆯ ᄋ ᄋ ᅨᄎ ᆨᄒ ᅳ ᅡᄂ ᆫ ᄀ ᅳ ᆫᄅ ᅪ ᆫ ᄋ ᅧ ᆫᄀ ᅧ ᅮ ᄃ ᆼᄒ ᅩ ᆼᄋ ᅣ ᆫ ᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪ ᄀ ᇀᄃ ᅡ ᅡ. Hui ᄃ ᆼ (2009)ᄋ ᅳ ᆫ ᄑ ᅳ ᆼᄅ ᅮ ᆨᄋ ᅧ ᅨᄎ ᆨᄋ ᅳ ᅦᄉ ᅥ ᄌ ᅮᄅ ᅩ ᄒ ᆯᄋ ᅪ ᆼᄃ ᅭ ᅬᄂ ᆫ ᅳ Weibull ᄀ ᅵᄇ ᆸᄋ ᅥ ᆯᄉ ᅳ ᅵᄀ ᅨᄋ ᆯᄅ ᅧ ᅩᄇ ᆫᄅ ᅮ ᅲᄒ ᅡᄋ ᅧᄑ ᆼᄅ ᅮ ᆨᄇ ᅧ ᆯᄌ ᅡ ᆫᄅ ᅥ ᆼᅡ ᅣ ᆫ ᄉᄌ ᆼᄋ ᅥ ᅴᄌ ᅮᄋ ᆼᄋ ᅭ ᆫᄌ ᅵ ᅡᄋ ᆫᄑ ᅵ ᆼᄉ ᅮ ᆨᄀ ᅩ ᅪᄀ ᅵᄋ ᆸᄋ ᅡ ᆯᄋ ᅳ ᅨᄎ ᆨᄒ ᅳ ᅡᄂ ᆫᄋ ᅳ ᆫᄀ ᅧ ᅮᄅ ᆯᄒ ᅳ ᅡ ᆻᄋ ᅧ ᄋ ᅳᄆ ᅧ, Palutikof ᄃ ᆼ (2002)ᄋ ᅳ ᆫᄋ ᅳ ᅲᄅ ᆸᄌ ᅥ ᅵᄋ ᆨᄋ ᅧ ᆯᄃ ᅳ ᅢᄉ ᆼᄋ ᅡ ᅳᄅ ᅩ MLR (multiple linear regression), PLSR (partial least squares regression), PCR (principal component regression)ᄋ ᅴᄒ ᅬᄀ ᅱᄇ ᆫᄉ ᅮ ᆨᄀ ᅥ ᅵᄇ ᆸᄋ ᅥ ᆯᄒ ᅳ ᆯᄋ ᅪ ᆼᄒ ᅭ ᅡᄋ ᅧᄑ ᆼᄉ ᅮ ᆨᅳ ᅩ ᆯ ᄋ ᅨᄎ ᄋ ᆨᅡ ᅳ ᄒᄋ ᆻᄃ ᅧ ᅡ. ᄀ ᅳᄅ ᅵᄀ ᅩᄆ ᆫᄉ ᅮ ᆼᄋ ᅳ ᅴᄃ ᆼ (1998)ᄋ ᅳ ᆫᅮ ᅳ ᆼ ᄑᄉ ᆨᄋ ᅩ ᅴᄉ ᅵᄀ ᅨᄋ ᆯᄌ ᅧ ᆨᄐ ᅥ ᆨᄉ ᅳ ᆼᄋ ᅥ ᆯᄀ ᅳ ᅩᄅ ᅧᄒ ᆫᄆ ᅡ ᅩᄒ ᆼᄋ ᅧ ᆯᄀ ᅳ ᅮᄎ ᆨᄒ ᅮ ᅡᄋ ᅧᄑ ᆼᄉ ᅮ ᆨᄆ ᅩ ᅩᄋ ᅴᄅ ᆯ ᅳ ᅮᄒ ᄉ ᆼᅡ ᅢ ᄒᄋ ᆻᄃ ᅧ ᅡ. ᄋ ᅵᅬ ᄋᄋ ᅦᄃ ᅩᄀ ᅵᄉ ᆼᄇ ᅡ ᆫᄋ ᅮ ᅣᄋ ᅦᄉ ᅥᄌ ᅮᄅ ᅩᄉ ᅡᄋ ᆼᄃ ᅭ ᅬᄂ ᆫᄋ ᅳ ᅨᄎ ᆨᄀ ᅳ ᅵᄇ ᆸᄋ ᅥ ᆫ ARIMA (Park ᄃ ᅳ ᆼ, 2020), ᄋ ᅳ ᆫᄀ ᅵ ᆼᄉ ᅩ ᆫᄀ ᅵ ᆼ ᅧ ᆼ (Lee ᄃ ᅡ ᄆ ᆼ, 2013), ᄉ ᅳ ᆫᄒ ᅮ ᆫᄉ ᅪ ᆫᅧ ᅵ ᆼ ᄀᄆ ᆼ (Yona ᄃ ᅡ ᆼ, 2013), LSTM (Shinᄀ ᅳ ᅪ Kim, 2018), ᄋ ᅴᄉ ᅡᄀ ᆯᅥ ᅧ ᆼ ᄌᄂ ᅡᄆ ᅮ (Kim ᄃ ᆼ, ᅳ 2020), ᄅ ᆫᅥ ᅢ ᆷ ᄃᄑ ᅩᄅ ᅦᄉ ᅳᄐ ᅳ (Jeong ᄃ ᆼ, 2018), ᄉ ᅳ ᅥᄑ ᅩᄐ ᅳᄇ ᆨᄐ ᅦ ᅥᄆ ᅥᄉ ᆫ (Lee ᄃ ᅵ ᆼ, 2016)ᄋ ᅳ ᅵᄋ ᆻᄃ ᅵ ᅡ. ᆫ ᄋ ᅩ ᄇ ᆫᄀ ᅧ ᅮᄋ ᅴ ᄆ ᆨᄌ ᅩ ᆨᄋ ᅥ ᆫ ᄂ ᅳ ᆷᅡ ᅡ ᆫ ᄒᄌ ᅵᄋ ᆨ ᄌ ᅧ ᆫᄋ ᅥ ᆨᄋ ᅧ ᅴ ᄌ ᆼᄀ ᅩ ᆫᄀ ᅪ ᅵᄉ ᆼᄀ ᅡ ᆫᄎ ᅪ ᆨᄌ ᅳ ᆼᄇ ᅡ ᅵ (automated synoptic observing system; ASOS) 95ᄀ ᅢᄌ ᅵᄌ ᆷ ᄀ ᅥ ᅵᄉ ᆼ ᄀ ᅡ ᆫᄎ ᅪ ᆨᄌ ᅳ ᅡᄅ ᅭᄅ ᆯ ᄀ ᅳ ᅵᄇ ᆫᄋ ᅡ ᅳᄅ ᅩ ᄀ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᅳ ᅳ ᆯ ᄋ ᄉ ᅮᄒ ᆼᄒ ᅢ ᅡᄋ ᅧ ᄀ ᆨ ᄀ ᅡ ᆫᄎ ᅪ ᆨᄌ ᅳ ᅵᄌ ᆷᄋ ᅥ ᅴ ᄃ ᅡᄋ ᆷ ᄂ ᅳ ᆯ 24ᄉ ᅡ ᅵᄀ ᆫᄋ ᅡ ᅦ ᅢᄒ ᄃ ᆫᄃ ᅡ ᆫᄀ ᅡ ᅵᄑ ᆼᄉ ᅮ ᆨᄋ ᅩ ᅨᄎ ᆨᄆ ᅳ ᅩᄒ ᆼᄋ ᅧ ᆯᄀ ᅳ ᅢᄇ ᆯᄒ ᅡ ᅡᄀ ᅩᄌ ᅡᄒ ᆫᄃ ᅡ ᅡ. ᄋ ᅵᄅ ᆯᄋ ᅳ ᅱᄒ ᅢᄉ ᅥᅬ ᄎᄀ ᆫᄋ ᅳ ᅴᅡ ᆫ ᄃᄀ ᅵᄌ ᅡᄅ ᅭᄆ ᆫᄋ ᅡ ᆯᄉ ᅳ ᆫᅢ ᅥ ᆨ ᄐᄒ ᅡᄋ ᅧᄒ ᆨᄉ ᅡ ᆸᄃ ᅳ ᅦᄋ ᅵᄐ ᅥᄋ ᅴ ᅦᄐ ᄉ ᅳᄅ ᆯᄀ ᅳ ᅮᄉ ᆼᄒ ᅥ ᅡᄂ ᆫᄉ ᅳ ᆯᄅ ᅳ ᅡᄋ ᅵᄃ ᆼᄋ ᅵ ᆫᄃ ᅱ ᅩᄋ ᅮᄀ ᅵᄇ ᆸ (sliding window method)ᄋ ᅥ ᆯᄌ ᅳ ᆨᄋ ᅥ ᆼᄒ ᅭ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄀ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅴᄀ ᅵᄇ ᆸᄋ ᅥ ᆫᄎ ᅳ ᅬ ᆫᄋ ᅳ ᄀ ᆫᅮ ᅧ ᄀᄋ ᅦᄃ ᅢᄑ ᅭᄌ ᆨᄋ ᅥ ᅳᄅ ᅩᄉ ᅡᄋ ᆼᄃ ᅭ ᅬᄂ ᆫ DNN (deep neural network), SVM (support vector machine), RF ᅳ (random forest)ᄅ ᆯᄒ ᅳ ᆯᄋ ᅪ ᆼᄒ ᅭ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄄ ᅩᄒ ᆫᄇ ᅡ ᆫᄋ ᅩ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫᄀ ᅳ ᆨᄀ ᅡ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄇ ᅳ ᆯᄎ ᅧ ᅬᄌ ᆨᄒ ᅥ ᅪᄅ ᆯᄋ ᅳ ᅱᄒ ᅢᄀ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅴᄉ ᆯᄌ ᅥ ᆼᄋ ᅥ ᆫ ᅵ ᅡᄅ ᄌ ᆯᅵ ᅳ ᄀᄌ ᆫᄋ ᅮ ᅳᄅ ᅩᄆ ᆫᄀ ᅵ ᆷᄃ ᅡ ᅩᄐ ᅦᄉ ᅳᄐ ᅳᄅ ᆯᄀ ᅳ ᅥᄎ ᅧᄀ ᆨᄀ ᅡ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄇ ᅳ ᆯᄀ ᅧ ᅡᄌ ᆼᄋ ᅡ ᅮᄉ ᅮᄒ ᆫᅥ ᅡ ᆼ ᄉᄂ ᆼᄋ ᅳ ᅴᄆ ᅩᄒ ᆼᄋ ᅧ ᆯᄉ ᅳ ᆫᅥ ᅥ ᆼ ᄌᄒ ᅡᄋ ᆻᄀ ᅧ ᅩ, ᄋ ᅵᄅ ᆯᄀ ᅳ ᆷᄌ ᅥ ᆼ ᅳ ᆾᄇ ᅵ ᄆ ᆫᅥ ᅮ ᆨ ᄉᄋ ᅦᄉ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᆻᄃ ᅧ ᅡ.. 2. 연구방법 2.1. 학습자료 ᆫ ᄋ ᅩ ᄇ ᆫᄀ ᅧ ᅮᄋ ᅦ ᄉ ᅡᄋ ᆼᄃ ᅭ ᆫ ᄌ ᅬ ᅡᄅ ᅭᄂ ᆫ ᄀ ᅳ ᅵᄉ ᆼᄎ ᅡ ᆼᄋ ᅥ ᅦᄉ ᅥ ᄌ ᅦᄀ ᆼᄒ ᅩ ᅡᄂ ᆫ ASOS 95ᄀ ᅳ ᅢ ᄌ ᅵᄌ ᆷ (Figure 2.1)ᄋ ᅥ ᅴ 2017ᄂ ᆫ 08ᄋ ᅧ ᆯᄇ ᅯ ᅮᄐ ᅥ 2018ᄂ ᆫ 08ᄋ ᅧ ᆯᄁ ᅯ ᅡᄌ ᅵᄋ ᅦ ᄃ ᅢᄒ ᆫ ᄀ ᅡ ᅵᄋ ᆫ, ᄉ ᅩ ᆸᄃ ᅳ ᅩ, ᄑ ᆼᄉ ᅮ ᆨ, ᄑ ᅩ ᆼᄒ ᅮ ᆼ, ᄀ ᅣ ᆼᄉ ᅡ ᅮᄅ ᆼ ᄇ ᅣ ᆫᄉ ᅧ ᅮᄋ ᅴ 1ᄉ ᅵᄀ ᆫ ᄑ ᅡ ᆼᄀ ᅧ ᆫ ᄀ ᅲ ᆫᄎ ᅪ ᆨᄌ ᅳ ᅡᄅ ᅭᄅ ᆯ ᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᆻᄃ ᅧ ᅡ. Figure 2.2ᄂ ᆫᄑ ᅳ ᆼᄉ ᅮ ᆨᄌ ᅩ ᅡᄅ ᅭᄋ ᅴᄐ ᆨᄌ ᅳ ᆼᄋ ᅵ ᆯᄉ ᅳ ᆯᄑ ᅡ ᅧᄇ ᅩᄀ ᅩᄌ ᅡ 95ᄀ ᅢᄌ ᅵᄌ ᆷᄋ ᅥ ᅦᄃ ᅢᄒ ᆫᄑ ᅡ ᆼᄀ ᅧ ᆫᄑ ᅲ ᆼᄉ ᅮ ᆨᄉ ᅩ ᅵᄀ ᅨᄋ ᆯᄀ ᅧ ᅪᄑ ᆼᄉ ᅮ ᆨᄀ ᅩ ᅮᄀ ᆫᄋ ᅡ ᅦᄄ ᅡᄅ ᆫᅮ ᅳ ᆼ ᄑ ᆨᄇ ᅩ ᄉ ᆫᅩ ᅵ ᄃᄅ ᆯᄂ ᅳ ᅡᄐ ᅡᄂ ᅢᄋ ᆻᄃ ᅥ ᅡ. ᄑ ᆼᄉ ᅮ ᆨᄉ ᅩ ᅵᄀ ᅨᄋ ᆯᄋ ᅧ ᆫᄋ ᅳ ᅵ ᆯᄎ ᆯᄋ ᅮ ᅵᄒ ᅮ 10ᄉ ᅵᄅ ᆯᄀ ᅳ ᅵᄌ ᆷᄋ ᅥ ᅳᄅ ᅩᄌ ᅵᄑ ᅭᄂ ᆫᄅ ᅡ ᅲᄋ ᅴᄒ ᅭᅪ ᄀᄅ ᅩᄑ ᆼᄉ ᅮ ᆨᄋ ᅩ ᅵᄉ ᆼᄉ ᅡ ᆼᄒ ᅳ ᅡᄆ ᅧ, ᅵᄅ ᄋ ᅥᄒ ᆫᄀ ᅡ ᆼᄒ ᅧ ᆼᄋ ᅣ ᆫ 15ᄉ ᅳ ᅵᄁ ᅡᄌ ᅵᄋ ᅲᄌ ᅵᄃ ᆫᄃ ᅬ ᅡ. ᄋ ᅵᄒ ᅮᄉ ᅵᄀ ᆫᄃ ᅡ ᅢᄋ ᅦᄉ ᅥᄂ ᆫᄃ ᅳ ᅡᄉ ᅵᄌ ᆷᄎ ᅥ ᅡᄀ ᆷᄉ ᅡ ᅩᄒ ᅡᄃ ᅡᄀ ᅡᄋ ᆯᄆ ᅵ ᆯᄋ ᅩ ᅵᄒ ᅮᄏ ᆫᄇ ᅳ ᆫᄃ ᅧ ᆼᄋ ᅩ ᆹᄋ ᅥ ᅵᄋ ᆯ ᅵ ᆼᄒ ᅥ ᄌ ᅡᄀ ᅦᄋ ᅲᄌ ᅵᅬ ᄃᄂ ᆫᄀ ᅳ ᆼᄒ ᅧ ᆼᄋ ᅣ ᆯᄇ ᅳ ᅩᄋ ᅵᄆ ᅧ, ᄋ ᅵᄂ ᆫᄉ ᅳ ᅵᄀ ᆫᄇ ᅡ ᆯᄎ ᅧ ᅬᄃ ᅢᄑ ᆼᄉ ᅮ ᆨᄀ ᅩ ᅪᄃ ᅩᄋ ᆯᄎ ᅵ ᅵᄒ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄑ ᆼᄉ ᅮ ᆨᄌ ᅩ ᅡᄅ ᅭᄋ ᅴᄀ ᅮᄀ ᆫᄇ ᅡ ᆯᄇ ᅧ ᆫᄃ ᅵ ᅩᄂ ᆫᅮ ᅳ ᆼ ᄑ ᆨᄀ ᅩ ᄉ ᅪᅮ ᄀᄀ ᆫᄇ ᅡ ᆯᄇ ᅧ ᆫᄃ ᅵ ᅩᄀ ᅡᄇ ᆫᄇ ᅡ ᅵᄅ ᅨᄒ ᅡᄂ ᆫᄀ ᅳ ᆼᄒ ᅧ ᆼᄋ ᅣ ᆯᄇ ᅳ ᅩᄋ ᅵᄆ ᅧ, ᄋ ᅵᄂ ᆫᄒ ᅳ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅡᄅ ᅭᄋ ᅦᄂ ᆽᄋ ᅡ ᆫᅮ ᅳ ᆼ ᄑᄉ ᆨᄃ ᅩ ᅢᄀ ᅡᄃ ᅡᄉ ᅮᄇ ᆫᄑ ᅮ ᅩᄒ ᆷᄋ ᅡ ᆯᄄ ᅳ ᆺᄒ ᅳ ᆫᄃ ᅡ ᅡ. ᆨᄉ ᅡ ᄒ ᆸᄌ ᅳ ᅡᄅ ᅭᄅ ᅩᄉ ᅡᄋ ᆼᄃ ᅭ ᆫᄀ ᅬ ᅵᄉ ᆼᄌ ᅡ ᅡᄅ ᅭᄂ ᆫᄀ ᅳ ᅵᄉ ᆼᄇ ᅡ ᆫᄉ ᅧ ᅮᄇ ᆯᄀ ᅧ ᆹᄋ ᅡ ᅴᄏ ᅳᄀ ᅵᄀ ᅡᄆ ᅩᄃ ᅮᄃ ᅡᄅ ᅳᄀ ᅵᄄ ᅢᄆ ᆫᄋ ᅮ ᅦ, ᄀ ᆨᄀ ᅡ ᅵᄉ ᆼᄇ ᅡ ᆫᄉ ᅧ ᅮᄂ ᆫᄌ ᅳ ᅡᄅ ᅭᄇ ᆷᄋ ᅥ ᅱ ᅦᄄ ᄋ ᅡᄅ ᅡᄑ ᅭᄌ ᆫᄒ ᅮ ᅪ (normalized)ᄅ ᆯᄉ ᅳ ᅮᄒ ᆼᄒ ᅢ ᅡᄋ ᅧᄋ ᅣᄒ ᆫᄃ ᅡ ᅡ. ᄄ ᅩᄒ ᆫ, ᄀ ᅡ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᆫᄋ ᅳ ᆸᄎ ᅵ ᆯᄅ ᅮ ᆨᄃ ᅧ ᅦᄋ ᅵᄐ ᅥᄋ ᅴᄏ ᅳᄀ ᅵᄀ ᅡᄌ ᆨᄋ ᅡ ᅡᄋ ᅣᄒ ᆨ ᅡ ᆸᄒ ᅳ ᄉ ᅭᅪ ᄀᄀ ᅡᄂ ᇁᄋ ᅩ ᅡᄌ ᅵᄆ ᅳᄅ ᅩ, ᄆ ᅩᄃ ᆫᄌ ᅳ ᅡᄅ ᅭᄂ ᆫᄀ ᅳ ᆨᄋ ᅡ ᅭᄉ ᅩᄋ ᅦᄄ ᅡᄅ ᅡᄃ ᅡᄋ ᆷᄋ ᅳ ᅴᄉ ᆨᄋ ᅵ ᆯᄒ ᅳ ᆯᄋ ᅪ ᆼᄒ ᅭ ᅡᄋ ᅧᄑ ᅭᄌ ᆫᄒ ᅮ ᅪᄀ ᅪᄌ ᆼᄋ ᅥ ᆯᄀ ᅳ ᅥᄎ ᆫᄒ ᅵ ᅮᄒ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅡ ᅭᄅ ᄅ ᅩᅡ ᄉᄋ ᆼᄃ ᅭ ᅬᄋ ᆻᄃ ᅥ ᅡ. di − dmean , i = 1, · · · , n, (2.1) dsd ᅧᄀ ᄋ ᅵᅥ ᄉ, di ᄂ ᆫᄒ ᅳ ᅢᄃ ᆼᄋ ᅡ ᅭᄉ ᅩᄋ ᅴᄌ ᅡᄅ ᅭ, nᄋ ᆫᄒ ᅳ ᅢᄃ ᆼᄋ ᅡ ᅭᄉ ᅩᄋ ᅴᄌ ᅡᄅ ᅭᄀ ᅢᄉ ᅮᄅ ᆯᄋ ᅳ ᅴᄆ ᅵᄒ ᆫᄃ ᅡ ᅡ. dmean ᄋ ᆫᄌ ᅳ ᅡᄅ ᅭᄋ ᅴᄑ ᆼᄀ ᅧ ᆫ, dsd ᄂ ᅲ ᆫ ᅳ ᆸᄅ ᅵ ᄋ ᆨᄇ ᅧ ᆫᄉ ᅧ ᅮᄋ ᅴᄑ ᅭᄌ ᆫᄑ ᅮ ᆫᄎ ᅧ ᅡᄅ ᆯᄂ ᅳ ᅡᄐ ᅡᄂ ᆫᄃ ᅢ ᅡ. ᆫᄋ ᅩ ᄇ ᆫᄀ ᅧ ᅮᄋ ᅴᄀ ᆷᄌ ᅥ ᆼᄀ ᅳ ᅵᄀ ᆫᄋ ᅡ ᆫᄉ ᅳ ᆯᄅ ᅳ ᅡᄋ ᅵᄃ ᆼᄋ ᅵ ᆫᄃ ᅱ ᅩᄋ ᅮᄋ ᅴᄎ ᅬᄃ ᅢᄀ ᆯᄋ ᅵ ᅵᄀ ᅡ 30ᄋ ᆯᄋ ᅵ ᆫᄀ ᅵ ᆫᄀ ᅪ ᅨᄅ ᅩᄉ ᅡᄋ ᆼᄃ ᅭ ᆫᄌ ᅬ ᅡᄅ ᅭᄋ ᅦᄉ ᅥ 1ᄃ ᆯᄋ ᅡ ᆯᄌ ᅳ ᅦᅬ 아 ᆫ ᄒ 2017ᄂ ᆫ 9ᄋ ᅧ ᆯ∼2018ᄂ ᅯ ᆫ 8ᄋ ᅧ ᆯᄋ ᅯ ᅵᄆ ᅧ, ᄀ ᆷᄌ ᅥ ᆼᄃ ᅳ ᅢᄉ ᆼᄋ ᅡ ᆫᄀ ᅳ ᆨᄋ ᅡ ᆯᄋ ᅵ ᅴᄆ ᅢᄉ ᅵᄀ ᆫᄋ ᅡ ᆯᄀ ᅳ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄇ ᅳ ᆯᄅ ᅧ ᅩᄋ ᅨᄎ ᆨᄒ ᅳ ᆫᄑ ᅡ ᆼᄉ ᅮ ᆨᄋ ᅩ ᅵᄃ ᅡ. ᄀ ᆨᄀ ᅡ ᅵᄀ ᅨᄒ ᆨ ᅡ ᆸᄋ ᅳ ᄉ ᅴᅨ ᄋᄎ ᆨᄉ ᅳ ᆼᄂ ᅥ ᆼᅳ ᅳ ᆯ ᄋᄑ ᆼᄀ ᅧ ᅡᄒ ᅡᄀ ᅵᄋ ᅱᄒ ᅡᄋ ᅧᄉ ᅡᄋ ᆼᄃ ᅭ ᆫᄀ ᅬ ᆷᄌ ᅥ ᆼᄌ ᅳ ᅵᄉ ᅮᄂ ᆫᄑ ᅳ ᆫᄎ ᅧ ᅡ (bias), ᄑ ᆼᄀ ᅧ ᆫᄌ ᅲ ᅦᄀ ᆸᄀ ᅩ ᆫᄋ ᅳ ᅩᄎ ᅡ (root mean square error; RMSE)ᄅ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᆻᄋ ᅧ ᅳᄆ ᅧ, Biasᄋ ᅪ RMSEᄂ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄋ ᅡ ᅵᄌ ᆼᄋ ᅥ ᅴᄃ ᆫᄃ ᅬ ᅡ. Zi =.
(3) Short-term forecasting for wind speed based on machine learning using weather observation data. 825. Figure 2.1 The map for the ASOS 95 observation sites. Figure 2.2 Data of Wind speed: (a) Time series of Wind speed, (b) Frequency by wind speed section. v u N N u1 X X 1 Bias = (Mi − Oi ), RM SE = t (Mi − Oi )2 , N i=1 N i=1 ᅧᄀ ᄋ ᅵᅥ ᄉ, Mi ᄂ ᆫᄀ ᅳ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᅳ ᅳ ᆯ ᄋᄒ ᆯᄋ ᅪ ᆼᄒ ᅭ ᅡᄋ ᅧᄋ ᅨᄎ ᆨᄃ ᅳ ᆫᄀ ᅬ ᆹᄋ ᅡ ᅵᄆ ᅧ, Oi ᄂ ᆫᄉ ᅳ ᆯᄌ ᅵ ᅦᄀ ᆫᄎ ᅪ ᆨᄀ ᅳ ᆹᄋ ᅡ ᅵᄃ ᅡ.. (2.2).
(4) 826. Hyeong-Se Jeong. 2.2. 실험설계 ᆫᄋ ᅩ ᄇ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫᄑ ᅳ ᆼᄉ ᅮ ᆨᄋ ᅩ ᆯᄋ ᅳ ᅨᄎ ᆨᄒ ᅳ ᅡᄀ ᅵᄋ ᅱᄒ ᅡᄋ ᅧᄉ ᆯᄅ ᅳ ᅡᄋ ᅵᄃ ᆼᄋ ᅵ ᆫᄃ ᅱ ᅩᄋ ᅮᄀ ᅵᄇ ᆸᄀ ᅥ ᅪᄀ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᆯᄒ ᅳ ᆯᄋ ᅪ ᆼᄒ ᅭ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄉ ᆯᄅ ᅳ ᅡᄋ ᅵᄃ ᆼᄋ ᅵ ᆫ ᅱ ᄃᄋ ᅩ ᅮᅵ ᄀᄇ ᆸᄋ ᅥ ᆫᄆ ᅳ ᆨᄑ ᅩ ᅭᄉ ᅵᄌ ᆷᄋ ᅥ ᅦᄉ ᅥᄀ ᅡᄌ ᆼᄎ ᅡ ᅬᄀ ᆫᄋ ᅳ ᅴᄌ ᅡᄅ ᅭᄃ ᆯᅳ ᅳ ᆯ ᄋᄒ ᆨᄉ ᅡ ᆸᄒ ᅳ ᅡᄂ ᆫᄀ ᅳ ᅵᄇ ᆸᄋ ᅥ ᅳᄅ ᅩᄉ ᅵᄌ ᆷᄋ ᅥ ᅴᄇ ᆫᄒ ᅧ ᅪᄋ ᅦᄄ ᅡᄅ ᅡᄎ ᅬᄉ ᆫᄋ ᅵ ᅴᄀ ᆼᄒ ᅧ ᆼᄋ ᅣ ᆯ ᅳ ᆨᄉ ᅡ ᄒ ᆸᅡ ᅳ ᄌᄅ ᅭᄋ ᅦᄑ ᅩᄒ ᆷᄒ ᅡ ᆯᄉ ᅡ ᅮᄋ ᆻᄂ ᅵ ᆫᄋ ᅳ ᅵᄌ ᆷᄋ ᅥ ᅵᄋ ᆻᄂ ᅵ ᆫᄀ ᅳ ᅵᄇ ᆸᄋ ᅥ ᅵᄃ ᅡ. Figure 2.3ᄂ ᆫᄉ ᅳ ᆯᄅ ᅳ ᅡᄋ ᅵᄃ ᆼᄋ ᅵ ᆫᄃ ᅱ ᅩᄋ ᅮᄋ ᅴᄆ ᅩᄉ ᆨᄃ ᅵ ᅩᄋ ᅵᄃ ᅡ. ᄋ ᅧᄀ ᅵ ᅥ, PT −w ᄂ ᄉ ᆫᄆ ᅳ ᆨᄑ ᅩ ᅭᄋ ᆯ Tᄋ ᅵ ᅦᄉ ᅥ wᄁ ᅡᄌ ᅵᄋ ᅨᄎ ᆨᄌ ᅳ ᅡ (predictor)ᄃ ᆯᄋ ᅳ ᅴᄋ ᅨᄎ ᆨᄃ ᅳ ᆫᄌ ᅬ ᅡᄅ ᅭᄅ ᆯᄋ ᅳ ᅴᄆ ᅵᄒ ᅡᄆ ᅧ, wᄂ ᆫᄋ ᅳ ᆫᄃ ᅱ ᅩᄋ ᅮᄋ ᅴᄀ ᆯ ᅵ ᅵ (ᄋ ᄋ ᆯᄃ ᅵ ᆫᄋ ᅡ ᅱ)ᄅ ᆯᄋ ᅳ ᅴᄆ ᅵᄒ ᆫᄃ ᅡ ᅡ. T ᄉ ᅵᄌ ᆷᄋ ᅥ ᅴᄑ ᆼᄉ ᅮ ᆨᄆ ᅩ ᅩᄃ ᆯᄋ ᅦ ᆫ T −1 ᄇ ᅳ ᅮᄐ ᅥ T − wᄁ ᅡᄌ ᅵᄋ ᅴᄌ ᅡᄅ ᅭᄅ ᆯᄒ ᅳ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅦᄒ ᆯᄋ ᅪ ᆼᄒ ᅭ ᅡᄋ ᅧᄀ ᅮᄎ ᆨ ᅮ ᅡᄀ ᄒ ᅩ, T ᄉ ᅵᄌ ᆷᄋ ᅥ ᅴᄑ ᆼᄉ ᅮ ᆨᄋ ᅩ ᅨᄎ ᆨᄋ ᅳ ᆫᄀ ᅳ ᇀᄋ ᅡ ᆫᄉ ᅳ ᅵᄌ ᆷᄋ ᅥ ᅴᄀ ᅵᄉ ᆼᄌ ᅡ ᅡᄅ ᅭᄅ ᆯᄋ ᅳ ᆸᄅ ᅵ ᆨᄒ ᅧ ᅡᄋ ᅧᄉ ᅮᄒ ᆼᄃ ᅢ ᆫᄃ ᅬ ᅡ. ᄃ ᅡᄋ ᆷᄂ ᅳ ᆯᄋ ᅡ ᅴᄆ ᅩᄃ ᆯᄋ ᅦ ᆫᄋ ᅳ ᆫᄃ ᅱ ᅩᄋ ᅮᄅ ᆯ ᅳ ᅵᄃ ᄋ ᆼᅡ ᅩ ᄒᄋ ᅧᄋ ᆫᄃ ᅱ ᅩᄋ ᅮᄀ ᆯᄋ ᅵ ᅵᄋ ᅦᄉ ᅥᄀ ᅡᄌ ᆼᄆ ᅡ ᆫᄀ ᅥ ᅪᄀ ᅥᄋ ᅴᄌ ᅡᄅ ᅭᄅ ᆯᄌ ᅳ ᅦᄀ ᅥᄒ ᅡᄀ ᅩᄎ ᅬᄉ ᆫᄋ ᅵ ᅴᄌ ᅡᄅ ᅭᄅ ᆯᄉ ᅳ ᅢᄅ ᅩᄋ ᆫᄒ ᅮ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅡᄅ ᅭᄋ ᅦᄑ ᅩᄒ ᆷᄒ ᅡ ᅡᄋ ᅧ ᆼᅥ ᅢ ᄉ ᆼ ᄉᄃ ᆫᄃ ᅬ ᅡ. ᄉ ᆯᄅ ᅳ ᅡᄋ ᅵᄃ ᆼᄋ ᅵ ᆫᄃ ᅱ ᅩᄋ ᅮᄀ ᅵᄇ ᆸᄋ ᅥ ᆫᄋ ᅳ ᆫᄃ ᅱ ᅩᄋ ᅮᄋ ᅴᄀ ᆯᄋ ᅵ ᅵᄋ ᅦᄄ ᅡᄅ ᅡᄉ ᆼᄃ ᅡ ᆼᄒ ᅡ ᅵᄃ ᅡᄅ ᆫᄉ ᅳ ᆼᄂ ᅥ ᆼᅳ ᅳ ᆯ ᄋᄂ ᅡᄐ ᅡᄂ ᅢᄆ ᅳᄅ ᅩ, ᄋ ᅵᄅ ᆯᄎ ᅳ ᅬᄌ ᆨᄒ ᅥ ᅪ ᅡᄋ ᄒ ᅧᅥ ᆨ 저 ᆯ ᄌᄒ ᆫᄋ ᅡ ᆫᄃ ᅱ ᅩᄋ ᅮᄀ ᆯᄋ ᅵ ᅵᄋ ᅴᄃ ᅩᄎ ᆯᅳ ᅮ ᆯ ᄋᄑ ᆯᄋ ᅵ ᅭᄅ ᅩᄒ ᆫᄃ ᅡ ᅡ (Kapoorᄋ ᅪ Bedi, 2013, Sweeney ᄃ ᆼ, 2013). ᄇ ᅳ ᆫᄋ ᅩ ᆫᄀ ᅧ ᅮᄋ ᅦ ᅥᄂ ᄉ ᆫᄉ ᅳ ᆯᅥ ᅥ ᆼ ᄌᄀ ᅵᄀ ᆫᄋ ᅡ ᆯᄋ ᅳ ᆯᅧ ᅵ ᆯ ᄇ, ᄀ ᅨᄌ ᆯᄇ ᅥ ᆯᅧ ᅧ ᆫ ᄇᄒ ᅪᄅ ᆯᄀ ᅳ ᅩᄅ ᅧᄒ ᅡᄋ ᅧ 5, 10, 20, 30ᄋ ᆯᄅ ᅵ ᅩᄉ ᆯᅥ ᅥ ᆼ ᄌᄒ ᅡᄋ ᅧᄆ ᆫᄀ ᅵ ᆷᄃ ᅡ ᅩᄉ ᆯᄒ ᅵ ᆷᄋ ᅥ ᆯᄉ ᅳ ᅮᄒ ᆼᄒ ᅢ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᅵᅨ ᄀ ᄀᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᆫᄒ ᅳ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅡᄅ ᅭᄋ ᅪᄉ ᆯᄌ ᅥ ᆼᄃ ᅥ ᅬᄂ ᆫᄋ ᅳ ᆫᄌ ᅵ ᅡᄋ ᅦᄌ ᆼᄉ ᅩ ᆨᄃ ᅩ ᅬᄂ ᆫᄐ ᅳ ᆨᄉ ᅳ ᆼᄋ ᅥ ᅵᄋ ᆻᄋ ᅵ ᅳᄆ ᅧ, ᄋ ᅵᄅ ᆯᄒ ᅳ ᆨᄋ ᅪ ᆫᄒ ᅵ ᅡᄀ ᅵᄋ ᅱᄒ ᅡᄋ ᅧᄋ ᆫᄀ ᅧ ᅮᄒ ᅳᄅ ᆷᄃ ᅳ ᅩ ᆫ Figure 2.4ᄋ ᅵ ᄋ ᅦᄄ ᅡᄅ ᅡ Table 2.1ᄋ ᅴᄋ ᆫᄌ ᅵ ᅡᄅ ᆯᄃ ᅳ ᅢᄉ ᆼᄋ ᅡ ᅳᄅ ᅩᄆ ᆫᄀ ᅵ ᆷᄃ ᅡ ᅩᄐ ᅦᄉ ᅳᄐ ᅳᄅ ᆯᄉ ᅳ ᅮᄒ ᆼᄒ ᅢ ᅡᄋ ᆻᄃ ᅧ ᅡ. 2.3. 기계학습 ᄇ ᄋ ᆫ ᅩ ᆫᄀ ᅧ ᅮᄋ ᅦ ᄉ ᅡᄋ ᆼᄃ ᅭ ᆫ ᄋ ᅬ ᆸᄎ ᅵ ᆯᄅ ᅮ ᆨ ᄎ ᅧ ᅥᄅ ᅵ ᄆ ᆾ ᄀ ᅵ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᆫ R ᄉ ᅳ ᅩᄑ ᅳᄐ ᅳᄋ ᅰᄋ ᅥᄅ ᆯ ᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᆻᄋ ᅧ ᅳᄆ ᅧ, ᄀ ᆨ ᄀ ᅡ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸ (DNN, ᅳ SVM, RF)ᄋ ᆯᄉ ᅳ ᅮᄒ ᆼᄒ ᅢ ᅡᄀ ᅵᄋ ᅱᄒ ᅡᄋ ᅧ R ᄉ ᅩᄑ ᅳᄐ ᅳᄋ ᅰᄋ ᅥᄋ ᅦᄉ ᅥᄌ ᅵᄋ ᆫᄒ ᅯ ᅡᄂ ᆫᄑ ᅳ ᅢᄏ ᅵᄌ ᅵᄌ ᆼᄋ ᅮ ᆯᄇ ᅵ ᆫᄌ ᅡ ᆨᄋ ᅥ ᅳᄅ ᅩᄉ ᅡᄋ ᆼᄃ ᅭ ᅬᄂ ᆫ “deepnet” ᅳ (Xiao, 2014), “e1071” (Meyer ᄃ ᆼ, 2019), “Randomforest” (Liawᄋ ᅳ ᅪ Wiener, 2018) ᄑ ᅢᄏ ᅵᄌ ᅵᄅ ᆯᄒ ᅳ ᆯᄋ ᅪ ᆼᄒ ᅭ ᅡᄋ ᆻ ᅧ ᅡ. ᄃ 2.3.1. 심층신경망 (deep neural network; DNN) ᆫᅩ ᅵ ᄋ ᆼ ᄀᄉ ᆫᅧ ᅵ ᆼ ᄀᄆ ᆼᄋ ᅡ ᆫ 3ᄀ ᅳ ᅢᄋ ᅴᄎ ᆼ (ᄋ ᅳ ᆸᄅ ᅵ ᆨᄎ ᅧ ᆼ, ᄋ ᅳ ᆫᄂ ᅳ ᆨᄎ ᅵ ᆼ, ᄎ ᅳ ᆯᄅ ᅮ ᆨᄎ ᅧ ᆼ)ᄋ ᅳ ᅳᄅ ᅩᄋ ᅵᄅ ᅮᄋ ᅥᄌ ᅵᄆ ᅧ, ᄀ ᆨᄎ ᅡ ᆼᄀ ᅳ ᆫᄋ ᅡ ᅴᄋ ᅵᄋ ᅥᄌ ᆫᄂ ᅵ ᅩᄃ ᅳᄋ ᅦᄉ ᅥᄀ ᅡᄌ ᆼᄎ ᅮ ᅵ ᄅᄌ ᆯ ᅳ ᅩᄌ ᆼᄒ ᅥ ᅡᄋ ᅧᄋ ᅨᄎ ᆨᅳ ᅳ ᆯ ᄋᄉ ᅮᄒ ᆼᄒ ᅢ ᅡᄂ ᆫᄀ ᅳ ᅮᄌ ᅩᄋ ᅵᄃ ᅡ. ᄋ ᅧᄀ ᅵᄉ ᅥ DNNᄋ ᆫᄋ ᅳ ᆫᄂ ᅳ ᆨᄎ ᅵ ᆼᄋ ᅳ ᅴᄀ ᇁᄋ ᅵ ᅵᄅ ᆯᄃ ᅳ ᅡᄎ ᆼᄋ ᅳ ᅳᄅ ᅩᄀ ᅮᄉ ᆼᄒ ᅥ ᅡᄂ ᆫᄀ ᅳ ᅵᄇ ᆸᄋ ᅥ ᅳᄅ ᅩ, ᅡᄉ ᄃ ᅮᅴ ᄋᄎ ᆼᄋ ᅳ ᅳᄅ ᅩᄀ ᅮᄎ ᆨᄃ ᅮ ᆫᄆ ᅬ ᅩᄒ ᆼᄋ ᅧ ᆫᄋ ᅳ ᆫᄂ ᅳ ᆨᄎ ᅵ ᆼᄋ ᅳ ᅦᄉ ᅥᄃ ᅦᄋ ᅵᄐ ᅥᄋ ᅴᄇ ᆫᄅ ᅮ ᅲ, ᄀ ᆫᄌ ᅮ ᆸᄒ ᅵ ᅢᄉ ᆨ, ᄃ ᅥ ᅦᄋ ᅵᄐ ᅥᄋ ᅴᄐ ᆨᄌ ᅳ ᆼᄑ ᅥ ᅢᄐ ᆫᄋ ᅥ ᆫᄉ ᅵ ᆨᄃ ᅵ ᆫᄀ ᅡ ᅨᄅ ᆯᄀ ᅳ ᅥ ᆫᄃ ᅵ ᄎ ᅡ. ᄄ ᅡᄅ ᅡᄉ ᅥ DNNᄋ ᆫᄃ ᅳ ᅡᄉ ᅮᄋ ᅴᄎ ᆼᄋ ᅳ ᅦᄉ ᅥᄃ ᅦᄋ ᅵᄐ ᅥᄋ ᅴᄐ ᆨᄌ ᅳ ᆼᄑ ᅥ ᅢᄐ ᆫᄋ ᅥ ᆯᄋ ᅳ ᆫᄉ ᅵ ᆨᄒ ᅵ ᅡᄀ ᅩᄒ ᆨᄉ ᅡ ᆸᄒ ᅳ ᅡᄆ ᅧᄌ ᆷᄌ ᅡ ᅢᄌ ᆨᄋ ᅥ ᆫᄃ ᅵ ᅦᄋ ᅵᄐ ᅥᄀ ᅮᄌ ᅩᄅ ᆯ ᅳ ᅡᄋ ᄑ ᆨᄒ ᅡ ᆯᄉ ᅡ ᅮᄋ ᆻᄂ ᅵ ᆫᄐ ᅳ ᆨᄌ ᅳ ᆼᄋ ᅵ ᅵᄋ ᆻᄃ ᅵ ᅡ. DNNᄋ ᅴᄎ ᆯᄅ ᅮ ᆨᄉ ᅧ ᆨᄋ ᅵ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄋ ᅡ ᅵᄌ ᆼᄋ ᅥ ᅴᄃ ᆫᄃ ᅬ ᅡ. X f (x) = K( wi xi ),. (2.3). i. ᄋᄀ ᅧ ᅵᅥ ᄉ, x i ᄂ ᆫᄋ ᅳ ᆸᄅ ᅵ ᆨᄃ ᅧ ᅦᄋ ᅵᄐ ᅥᄋ ᅵᄀ ᅩ, wi ᄂ ᆫ DNNᄋ ᅳ ᅴᄂ ᅩᄃ ᅳᄋ ᅦᄄ ᅡᄅ ᆫᄀ ᅳ ᅡᄌ ᆼᄎ ᅮ ᅵ (weight)ᄅ ᆯᄋ ᅳ ᅴᄆ ᅵᄒ ᆫᄃ ᅡ ᅡ. Kᄂ ᆫᄒ ᅳ ᆯᄉ ᅪ ᆼᄒ ᅥ ᆷᄉ ᅡ ᅮ (activation function)ᄅ ᆯᄋ ᅳ ᅴᄆ ᅵᄒ ᅡᄂ ᆫᄃ ᅳ ᅦ, DNNᄋ ᅴᄒ ᆯᄉ ᅪ ᆼᄒ ᅥ ᅪᅡ ᆷ ᄒᄉ ᅮᄅ ᅩᄂ ᆫ linear, simoid, ReLU (rectified linear ᅳ units) ᄃ ᆼᄋ ᅳ ᅵᄃ ᅢᄑ ᅭᄌ ᆨᄋ ᅥ ᅳᄅ ᅩᄉ ᅡᄋ ᆼᄃ ᅭ ᆫᄃ ᅬ ᅡ. ᄇ ᆫᄋ ᅩ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫ sigmoid ᄒ ᅳ ᆷᄉ ᅡ ᅮᄅ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᆻᄋ ᅧ ᅳᄆ ᅧ, sigmoid ᄒ ᆷᄉ ᅡ ᅮᄋ ᅴᄉ ᆨᄋ ᅵ ᆫ ᅳ ᅡᄋ ᄃ ᆷᄀ ᅳ ᅪᄀ ᇀᄃ ᅡ ᅡ. K=. 1 . 1 + e−x. (2.4).
(5) Short-term forecasting for wind speed based on machine learning using weather observation data. Figure 2.3 Diagram of sliding window learning.. Figure 2.4 Input-output structure of machine learning and procedure of wind speed forecasting. Table 2.1 Predictors of machine learning and test factor Parameter. Test parameter. Input data. Deep neural network. Support vector machine. Random forest. Percentage of Percentage of Percentage of sampling data, Node, sampling data, sampling data, Number of hidden kernel Nember of tree Layer Wind speed, Wind direction, Temperature, Relative humidity, Precipitation, Lat&lon of station, Hour (0∼23). 827.
(6) 828. Hyeong-Se Jeong. 2.3.2. 서포트 벡터 머신 (support vector machine; SVM) SVMᄋ ᆫᄃ ᅳ ᅦᄋ ᅵᄐ ᅥᄅ ᆯᄃ ᅳ ᅮᄏ ᆯᄅ ᅳ ᅢᄉ ᅳᄅ ᅩᄇ ᆫᄅ ᅮ ᅲᄒ ᅡᄋ ᅧᄆ ᅡᄌ ᆫ (magine)ᄋ ᅵ ᅵᄂ ᇁᄋ ᅩ ᆫᄎ ᅳ ᅩᄑ ᆼᅧ ᅧ ᆫ ᄆ (hyper-plane)ᄋ ᆯᄀ ᅳ ᅮᄒ ᆫᄒ ᅧ ᅡᄀ ᅵᄄ ᅢ ᄆᄋ ᆫ ᅮ ᅦᄇ ᅵᄉ ᆫᅧ ᅥ ᆼ ᄒᄌ ᆨᄋ ᅥ ᆫᄀ ᅵ ᆹᄃ ᅡ ᆯᄋ ᅳ ᅴᄂ ᅡᄋ ᆯᄋ ᅧ ᆯᄉ ᅳ ᆫᅧ ᅥ ᆼ ᄒᄌ ᆨᄋ ᅥ ᅳᄅ ᅩᄀ ᅮᄒ ᆫᄒ ᅧ ᅡᄋ ᅧᄃ ᅦᄋ ᅵᄐ ᅥᄀ ᆫᄀ ᅡ ᆫᄀ ᅪ ᅨᄉ ᆼᄋ ᅥ ᆯᄎ ᅳ ᆽᄂ ᅡ ᆫᄀ ᅳ ᆺᄋ ᅥ ᅵᄀ ᅡᄂ ᆼᄒ ᅳ ᅡᄃ ᅡ (Tongᄀ ᅪ Koller, 2011). ᄀ ᅳᄅ ᅥᄂ ᅡ SVMᄋ ᆫ ᄏ ᅳ ᅥᄂ ᆯ ᄐ ᅥ ᅳᄅ ᆨ (kernel trick) ᄒ ᅵ ᆷᄉ ᅡ ᅮᄋ ᅦ ᄄ ᅡᄅ ᅡ ᄆ ᅩᄋ ᅴ ᄀ ᆼᄅ ᅧ ᆼᄋ ᅣ ᅵ ᄃ ᆯᄅ ᅡ ᅵᄃ ᅬᄀ ᅵ ᄄ ᅢᄆ ᆫᄋ ᅮ ᅦ, ᅩᄋ ᄆ ᅴᄃ ᅬᄂ ᆫ ᄇ ᅳ ᆫᄉ ᅧ ᅮᄋ ᅦᄄ ᅡᄅ ᅡᄀ ᅳ ᄉ ᆼᄂ ᅥ ᆼ ᄄ ᅳ ᅩᄒ ᆫ ᄏ ᅡ ᆫ ᄎ ᅳ ᅡᄋ ᅵᄅ ᆯ ᄋ ᅳ ᅣᄀ ᅵᄒ ᆯ ᄉ ᅡ ᅮ ᄋ ᆻᄃ ᅵ ᅡ. ᄋ ᆯᄇ ᅵ ᆫᄌ ᅡ ᆨᄋ ᅥ ᅳᄅ ᅩ ᄉ ᅡᄋ ᆼᄃ ᅭ ᅬᄂ ᆫ ᄏ ᅳ ᅥᄂ ᆯᄋ ᅥ ᆫ ᄉ ᅳ ᆫᄒ ᅥ ᆼ ᄏ ᅧ ᅥ ᆯ (linear), ᄃ ᅥ ᄂ ᅡᄒ ᆼᄉ ᅡ ᆨᄏ ᅵ ᅥᄂ ᆯ (polynomial), ᄀ ᅥ ᅡᄋ ᅮᄉ ᅵᄋ ᆫᄏ ᅡ ᅥᄂ ᆯ (radial basis function network; RBF)ᄋ ᅥ ᅵᄋ ᆻᄃ ᅵ ᅡ (Smolaᄋ ᅪ Scholkopf, 2004; Muller ᄃ ᆼ, 2001). ᄇ ᅳ ᆫᄋ ᅩ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫᄉ ᅳ ᆫᅧ ᅥ ᆼ ᄒᄏ ᅥᄂ ᆯᄀ ᅥ ᅪᄀ ᅡᄋ ᅮᄉ ᅵᄋ ᆫᄏ ᅡ ᅥᄂ ᆯᄋ ᅥ ᆯᄒ ᅳ ᆯᄋ ᅪ ᆼᄒ ᅭ ᅡᄋ ᅧ ᆫᄀ ᅧ ᄋ ᅮᄅ ᆯᄉ ᅳ ᅮᄒ ᆼᄒ ᅢ ᅡᄋ ᆻᄋ ᅧ ᅳᄆ ᅧ, RBF ᄏ ᅥᄂ ᆯᄋ ᅥ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄋ ᅡ ᅵᄌ ᆼᄋ ᅥ ᅴᄃ ᆫᄃ ᅬ ᅡ. K(x, y) = exp(−γ ∥ x − y ∥2 ),. (2.5). ᄋᄀ ᅧ ᅵᅥ ᄉ, γᄂ ᆫᄀ ᅳ ᅡᄋ ᅮᄉ ᅵᄋ ᆫᄎ ᅡ ᅡᄋ ᆫᄋ ᅯ ᆯᄌ ᅳ ᅦᄒ ᆫᄒ ᅡ ᅡᄂ ᆫᄇ ᅳ ᆫᄉ ᅧ ᅮᄋ ᅵᄆ ᅧᄀ ᆹᄋ ᅡ ᆯᄏ ᅳ ᅳᄀ ᅦᄒ ᆯᄉ ᅡ ᅮᄅ ᆨ SVMᄋ ᅩ ᅴᄋ ᆼᄒ ᅧ ᆼᄋ ᅣ ᅵᄂ ᅡᄇ ᆷᄋ ᅥ ᅱᄀ ᅡᄌ ᆯᄋ ᅮ ᅥᄃ ᅳᄂ ᆫ ᅳ ᆨᄒ ᅧ ᄋ ᆯᄋ ᅡ ᆯᄒ ᅳ ᆫᄃ ᅡ ᅡ. ᄇ ᆫᄋ ᅩ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫ γᄅ ᅳ ᆯ 0.11ᄅ ᅳ ᅩᄉ ᆯᅥ ᅥ ᆼ ᄌᄒ ᅡᄋ ᆻᄃ ᅧ ᅡ. 2.3.3. 랜덤 포레스트 (Random forest, RF) 러 ᆫ ᅢ ᆷ ᄃᄑ ᅩᄅ ᅦᄉ ᅳᄐ ᅳᄂ ᆫᄏ ᅳ ᅳᄀ ᅦ 2ᄀ ᅡᄌ ᅵᄇ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅳᄅ ᅩᄀ ᅮᄉ ᆼᄃ ᅥ ᆫᄃ ᅬ ᅡ. ᄎ ᆺᅥ ᅥ ᆫ ᄇᄍ ᅢᄂ ᆫᄌ ᅳ ᅮᄋ ᅥᄌ ᆫᄃ ᅵ ᅦᄋ ᅵᄐ ᅥᄅ ᅩᄇ ᅮᄐ ᅥᄋ ᆯᄇ ᅵ ᅮᄅ ᆯᄇ ᅳ ᆨᄋ ᅩ ᆫᄎ ᅯ ᅮᄎ ᆯ ᅮ ᅡᄀ ᄒ ᅩᅮ ᄎᄎ ᆯᄃ ᅮ ᆫᄃ ᅬ ᅦᄋ ᅵᄐ ᅥᄆ ᆫᄋ ᅡ ᆯᄃ ᅳ ᅢᄉ ᆼᄋ ᅡ ᅳᄅ ᅩᄋ ᅴᄉ ᅡᄀ ᆯᅥ ᅧ ᆼ ᄌᄂ ᅡᄆ ᅮ (decision tree)ᄅ ᆯᄉ ᅳ ᆼᅥ ᅢ ᆼ ᄉᄒ ᅡᄂ ᆫᄀ ᅳ ᅪᄌ ᆼᄋ ᅥ ᅵᄆ ᅧ, ᄃ ᅮᄇ ᆫᄍ ᅥ ᅢᄂ ᆫᄂ ᅳ ᅩᄃ ᅳ ᅢᄃ ᄂ ᅦᄋ ᅵᄐ ᅥᄅ ᆯᄌ ᅳ ᅡᄉ ᆨᄂ ᅵ ᅩᄃ ᅳᄅ ᅩᄂ ᅡᄂ ᅮᄂ ᆫᄀ ᅳ ᅵᄌ ᆫᅳ ᅮ ᆯ ᄋᄌ ᆼᄒ ᅥ ᆯᄄ ᅡ ᅢᄌ ᆫᄎ ᅥ ᅦᄇ ᆫᄉ ᅧ ᅮᄀ ᅡᄋ ᅡᄂ ᆫᄋ ᅵ ᆷᄋ ᅵ ᅴᄋ ᅴᄋ ᆯᄇ ᅵ ᅮᄇ ᆫᄉ ᅧ ᅮᄆ ᆫᄋ ᅡ ᆯᄃ ᅳ ᅢᄉ ᆼᄋ ᅡ ᅳᄅ ᅩᄀ ᅡ ᅵᄅ ᄌ ᆯ ᄉ ᅳ ᆼᅥ ᅢ ᆼ ᄉᄒ ᅡᄂ ᆫ ᄀ ᅳ ᅵᄌ ᆫᅳ ᅮ ᆯ ᄋ ᄉ ᆯᅥ ᅥ ᆼ ᄌᄒ ᅡᄂ ᆫ ᄀ ᅳ ᆺᄋ ᅥ ᅵᄃ ᅡ. ᄋ ᆷᄋ ᅵ ᅴᄉ ᆼᄋ ᅥ ᅦ ᄋ ᅴᄒ ᅢ ᄉ ᆼᅥ ᅢ ᆼ ᄉᄃ ᆫ ᄃ ᅬ ᅡᄅ ᆫ ᄐ ᅳ ᆨᄉ ᅳ ᆼᄋ ᅥ ᆯ ᄀ ᅳ ᆽᄂ ᅡ ᆫ ᄃ ᅳ ᅡᄉ ᅮᄋ ᅴ ᄋ ᅴᄉ ᅡᄀ ᆯᅥ ᅧ ᆼ ᄌᄂ ᅡ ᅮᄂ ᄆ ᆫᄋ ᅳ ᆼᄉ ᅡ ᆼᄇ ᅡ ᆯᄒ ᅳ ᅡᄋ ᅧᄀ ᆯᄀ ᅧ ᅪᄋ ᅨᄎ ᆨᄋ ᅳ ᅦᄉ ᅡᄋ ᆼᄃ ᅭ ᆫᄃ ᅬ ᅡ (Liawᄋ ᅪ Wiener, 2002). ᄅ ᆫᅥ ᅢ ᆷ ᄃᄑ ᅩᄅ ᅦᄉ ᅳᄐ ᅳᄂ ᆫᄃ ᅳ ᅡᄉ ᅮᄋ ᅴᄋ ᅴᄉ ᅡᄀ ᆯᅥ ᅧ ᆼ ᄌᄂ ᅡ ᅮᄉ ᄆ ᆼᅥ ᅢ ᆼ ᄉᄆ ᆾᄃ ᅵ ᅦᄋ ᅵᄐ ᅥᄋ ᅪᄂ ᅩᄃ ᅳᄋ ᅴᄉ ᆷᄑ ᅢ ᆯᄅ ᅳ ᆼᄋ ᅵ ᆯᄉ ᅳ ᅮᄒ ᆼᄒ ᅢ ᅡᄀ ᅵᄄ ᅢᄆ ᆫᄋ ᅮ ᅦᄀ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄆ ᅳ ᅩᄒ ᆼᄋ ᅧ ᅦᄉ ᅥᄇ ᆫᅥ ᅵ ᆫ ᄇᄒ ᅡᄀ ᅦᄋ ᆯᄋ ᅵ ᅥᄂ ᅡᄂ ᆫᄀ ᅳ ᅪᄃ ᅢᄌ ᆨᄒ ᅥ ᆸ ᅡ (over-fitting) ᄆ ᆫᄌ ᅮ ᅦᄅ ᆯᄒ ᅳ ᅢᄀ ᆯᄒ ᅧ ᆯᄉ ᅡ ᅮᄋ ᆻᄂ ᅵ ᆫᄋ ᅳ ᅵᄌ ᆷᄋ ᅥ ᅵᄋ ᆻᄃ ᅵ ᅡ (Diaz, 2006).. 3. 결과 3.1. 최적 모형 선정 ᆫᄋ ᅩ ᄇ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫᄀ ᅳ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄇ ᅳ ᆯᄎ ᅧ ᅬᄌ ᆨᄋ ᅥ ᅴᄆ ᅩᄒ ᆼᄋ ᅧ ᆯᄉ ᅳ ᆫᅥ ᅥ ᆼ ᄌᄒ ᅡᄀ ᅵᄋ ᅱᄒ ᅡᄋ ᅧᄆ ᆫᄀ ᅵ ᆷᄃ ᅡ ᅩᄐ ᅦᄉ ᅳᄐ ᅳᄅ ᆯᄉ ᅳ ᅮᄒ ᆼᄒ ᅢ ᅡᄋ ᆻᄃ ᅧ ᅡ (Figure 3.1). ᄆᄀ ᆫ ᅵ ᆷᅩ ᅡ ᄃᄐ ᅦᄉ ᅳᄐ ᅳᄂ ᆫᄋ ᅳ ᇁᄉ ᅡ ᅥᄋ ᆫᄀ ᅥ ᆸᄒ ᅳ ᆫᄇ ᅡ ᅡᅪ ᄋᄀ ᇀᄋ ᅡ ᅵᄋ ᆫᄃ ᅱ ᅩᄋ ᅮᄀ ᆯᄋ ᅵ ᅵ (10, 20, 30), ᄒ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅡᄅ ᅭᄋ ᅴᄑ ᅭᄇ ᆫᄇ ᅩ ᅵᄋ ᆯ (10, 20, 30, ᅲ 40, 50), ᄀ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄇ ᅳ ᆯᄐ ᅧ ᅦᄉ ᅳᄐ ᅳᄋ ᆫᄌ ᅵ ᅡᄉ ᆯᅥ ᅥ ᆼ ᄌᄋ ᅦᄄ ᅡᄅ ᅡᄌ ᅩᄒ ᆸᄒ ᅡ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄐ ᅦᄉ ᅳᄐ ᅳᄋ ᆫᄌ ᅵ ᅡᄋ ᅴᄀ ᆼᄋ ᅧ ᅮ DNNᄋ ᆫᄂ ᅳ ᅩᄃ ᅳᄉ ᅮ (10, 20, 30)ᄋ ᅪᄎ ᆼ (Layer) ᄉ ᅳ ᅮ (1, 2, 3)ᄋ ᅵᄆ ᅧ, SVMᄋ ᆫᄏ ᅳ ᅥᄂ ᆯᄋ ᅥ ᅴᄌ ᆼᄅ ᅩ ᅲ (linear, RBF), RFᄂ ᆫᄂ ᅳ ᅡᄆ ᅮᄉ ᅮ (50, 100, 200, 300, 500, 1000)ᄋ ᅵᄃ ᅡ. ᄋ ᅵᄅ ᆯᄀ ᅳ ᆨᄋ ᅡ ᆫᄌ ᅵ ᅡᄋ ᅦᄄ ᅡᄅ ᅡᄌ ᅩᄒ ᆸᄃ ᅡ ᆫᄀ ᅬ ᆼᄋ ᅧ ᅮᄋ ᅴᄉ ᅮᄂ ᆫ DNN, SVM, RFᄀ ᅳ ᅡᄀ ᆨᄀ ᅡ ᆨ 180, 60, ᅡ 120ᄀ ᅢᅵ ᄋᄃ ᅡ. ᄀ ᆨᄌ ᅡ ᅩᄒ ᆸᄋ ᅡ ᅦᄃ ᅢᄒ ᅢᄆ ᆫᄀ ᅵ ᆷᄃ ᅡ ᅩᄐ ᅦᄉ ᅳᄐ ᅳᄅ ᆯᅮ ᅳ ᆫ ᄇᄉ ᆨᄒ ᅥ ᆫᅧ ᅡ ᆯ ᄀᄀ ᅪᄌ ᆫᄇ ᅥ ᆫᄌ ᅡ ᆨᄋ ᅥ ᅳᄅ ᅩᄋ ᆫᄃ ᅱ ᅩᄋ ᅮᄀ ᆯᄋ ᅵ ᅵᅪ 아 ᆨ ᄒᄉ ᆸᄌ ᅳ ᅡᄅ ᅭᄑ ᅭᄇ ᆫᄇ ᅩ ᅵ ᆯᄋ ᅲ ᄋ ᆫ RMSEᄋ ᅳ ᅪᄋ ᆨᄇ ᅧ ᅵᄅ ᅨᄒ ᅡᄂ ᆫᄀ ᅳ ᅮᄌ ᅩᄅ ᆯᄇ ᅳ ᅩᄋ ᆫᄃ ᅵ ᅡ. ᄋ ᅵᄂ ᆫᄒ ᅳ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅡᄅ ᅭᄀ ᅡᄀ ᅩᄅ ᅧᄒ ᆯᄉ ᅡ ᅮᄋ ᆻᄂ ᅵ ᆫᄇ ᅳ ᆫᄉ ᅧ ᅮᄆ ᆾᄀ ᅵ ᅵᄀ ᆫᄃ ᅡ ᆼᄋ ᅳ ᅵᄆ ᆭᄋ ᅡ ᆯᄉ ᅳ ᅮ ᆨᅩ ᅩ ᄅ ᇂ ᄌᄋ ᆯᄉ ᅳ ᆼᄂ ᅥ ᆼᅳ ᅳ ᆯ ᄋᄀ ᅵᄃ ᅢᄒ ᆯᄉ ᅡ ᅮᄋ ᆻᄂ ᅵ ᆫᄀ ᅳ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅴᄐ ᆨᄉ ᅳ ᆼᄋ ᅥ ᅳᄅ ᅩᄑ ᆫᄃ ᅡ ᆫᄃ ᅡ ᆫᄃ ᅬ ᅡ. ᄒ ᅡᄌ ᅵᄆ ᆫᄂ ᅡ ᅥᄆ ᅮᄆ ᆭᄋ ᅡ ᆫᄒ ᅳ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅡᄅ ᅭᄂ ᆫᄋ ᅳ ᅩᄒ ᅵᄅ ᅧᄉ ᆫ ᅡ ᆯᄉ ᅮ ᄎ ᅵᄀ ᆫᄀ ᅡ ᅪᄌ ᆫᄉ ᅥ ᆫᄌ ᅡ ᅡᄋ ᆫᄋ ᅯ ᆯᄀ ᅳ ᅪᄃ ᅡᄒ ᅡᄀ ᅦᄉ ᅡᄋ ᆼᄒ ᅭ ᆯᄉ ᅡ ᅮᄋ ᆻᄂ ᅵ ᆫᄋ ᅳ ᅭᄉ ᅩᄋ ᅵᄆ ᅳᄅ ᅩᄌ ᆨᅥ ᅥ ᆼ ᄌᄒ ᆫᄀ ᅡ ᆹᄋ ᅡ ᆯᄎ ᅳ ᆽᄂ ᅡ ᆫᄀ ᅳ ᆺᄋ ᅥ ᅵᄌ ᆼᄋ ᅮ ᅭᄒ ᅡᄃ ᅡ. ᅩᄒ ᄄ ᆫᄐ ᅡ ᆨᄌ ᅳ ᆼᄌ ᅥ ᅵᄌ ᆷᄋ ᅥ ᅴᄒ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅡᄅ ᅭᄀ ᅡᄇ ᅵᄋ ᆯᄋ ᅲ ᅵᄂ ᇁᄋ ᅩ ᅡᄌ ᅵᄆ ᆫᄇ ᅧ ᅵᄒ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅵᄌ ᆷᄋ ᅥ ᅴᄀ ᆼᄒ ᅧ ᆼᄋ ᅣ ᆯᄄ ᅳ ᅡᄅ ᅳᄌ ᅵᄆ ᆺᄒ ᅩ ᅡᄂ ᆫᄀ ᅳ ᅪᄃ ᅢᄌ ᆨᄒ ᅥ ᆸᄋ ᅡ ᅵᄂ ᅡᄐ ᅡ ᆯ ᄉ ᅡ ᄂ ᅮ ᄋ ᆻᄀ ᅵ ᅵ ᄄ ᅢᄆ ᆫᄋ ᅮ ᅦ ᄋ ᅵ ᄌ ᆷᄋ ᅥ ᆯ ᄋ ᅳ ᅲᄋ ᅴᄒ ᅡᄋ ᅧᄋ ᅣ ᄒ ᆫᄃ ᅡ ᅡ. ᄋ ᆫᄃ ᅱ ᅩᄋ ᅮ ᄀ ᆯᄋ ᅵ ᅵ 5ᄋ ᆯᄀ ᅵ ᅪ 10ᄋ ᆯᄋ ᅵ ᅴ ᄀ ᆼᄋ ᅧ ᅮ ᄒ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅡᄅ ᅭᄋ ᅴ ᄀ ᆯᄋ ᅵ ᅵᄀ ᅡ ᄍ ᆲ ᅡ ᅡᄒ ᄋ ᆨᅳ ᅡ ᆸ ᄉᄌ ᅡᄅ ᅭᄇ ᅮᄌ ᆨᄋ ᅩ ᅳᄅ ᅩᄋ ᆫᄒ ᅵ ᅢᄋ ᅨᄎ ᆨᄉ ᅳ ᆼᄂ ᅥ ᆼᄋ ᅳ ᅵᄃ ᅡᄉ ᅩᄄ ᆯᄋ ᅥ ᅥᄌ ᅧ RMSEᄀ ᅡᄌ ᆼᄀ ᅳ ᅡᄒ ᅡᄂ ᆫᄀ ᅳ ᆼᄒ ᅧ ᆼᄋ ᅣ ᆯᄂ ᅳ ᅡᄐ ᅡᄂ ᆫᄃ ᅢ ᅡ. ᄋ ᆫᄃ ᅱ ᅩᄋ ᅮᄀ ᆯᄋ ᅵ ᅵ ᆫ 20ᄋ ᅳ ᄂ ᆯᄀ ᅵ ᅪ 30ᄋ ᆯᄋ ᅵ ᅦᄉ ᅥ ᄉ ᅮᄅ ᆷᄒ ᅧ ᅡᄋ ᅧ ᄋ ᅲᄉ ᅡᄒ ᆫ ᅧ ᅡ ᆼ ᄀᄒ ᆼᄋ ᅣ ᆯ ᄇ ᅳ ᅩᄋ ᅵᄆ ᅧ, ᄒ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅡᄅ ᅭᄋ ᅴ ᄑ ᅭᄇ ᆫ ᄇ ᅩ ᅵᄋ ᆯᄋ ᅲ ᆫ 40%ᄋ ᅳ ᅦᄉ ᅥ 50%ᄅ ᅩ ᄀ ᆯᄉ ᅡ ᅮ ᆨ RMSEᄀ ᅩ ᄅ ᅡ ᄀ ᆷᄉ ᅡ ᅩᄒ ᅡᄋ ᅧ ᄉ ᅩᄉ ᅮᄌ ᆷ ᄃ ᅥ ᅮ ᄇ ᆫᄍ ᅥ ᅢ ᄌ ᅡᄅ ᅵ ᄌ ᆼᄃ ᅥ ᅩᄋ ᅴ ᄆ ᅵᄇ ᅵᄒ ᆫ ᄎ ᅡ ᅡᄋ ᅵᄅ ᆯ ᄇ ᅳ ᅩᄋ ᆫᄃ ᅵ ᅡ. ᄉ ᆯᅥ ᅥ ᆼ ᄌ ᄋ ᆫᄌ ᅵ ᅡᄋ ᅦ ᄄ ᅡᄅ ᆫ ᄀ ᅳ ᅵᄀ ᅨᄒ ᆨ ᅡ ᆸᄋ ᅳ ᄉ ᅴᄆ ᆫᄀ ᅵ ᆷᄃ ᅡ ᅩᄂ ᆫ DNNᄋ ᅳ ᅴᄀ ᆼᄋ ᅧ ᅮᄃ ᅢᄎ ᅦᄅ ᅩᄂ ᅩᄃ ᅳᄉ ᅮᄋ ᅪᄎ ᆼᄉ ᅳ ᅮᄀ ᅡᄌ ᆼᄀ ᅳ ᅡᄒ ᆯᄉ ᅡ ᅮᄅ ᆨ RMSEᄀ ᅩ ᅡᄂ ᇁᄋ ᅩ ᅡᄌ ᅵᄂ ᆫᄀ ᅳ ᆼᄒ ᅧ ᆼᄋ ᅣ ᆯᄇ ᅳ ᅩᄋ ᅵᄆ ᅧ, SVMᄋ ᆫ RBF ᄏ ᅳ ᅥᄂ ᆯᄋ ᅥ ᅵᄆ ᅩᄃ ᆫᄌ ᅳ ᅩᄒ ᆸᄋ ᅡ ᅦᄉ ᅥ linear ᄏ ᅥᄂ ᆯᄇ ᅥ ᅩᄃ ᅡᄄ ᅱᄋ ᅥᄂ ᆫᄉ ᅡ ᆼᄂ ᅥ ᆼᄋ ᅳ ᆯᄂ ᅳ ᅡᄐ ᅡᄂ ᆫᄃ ᅡ ᅡ. RFᄂ ᆫᄀ ᅳ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄂ ᅳ ᅢᄉ ᆯᅥ ᅥ ᆼ ᄌ.
(7) Short-term forecasting for wind speed based on machine learning using weather observation data. 829. Figure 3.1 RMSE of each model: (a) DNN, (b) SVM, (c) RF. Table 3.1 The factor of the selected machine learning model Machine learning model Deep neural network. Support vector machine. Random forest. Element. Configuration. Window length Percentage of sampling data Node Number of hidden layer Window length Percentage of sampling data Kernel Window length Percentage of sampling data Number of tree. 20 50 20 2 20 50 PBL 20 50 200. ᄋᄄ ᅦ ᅡᅳ ᆫ ᄅᄇ ᆫᄒ ᅧ ᅪᄇ ᅩᄃ ᅡᄀ ᅩᄅ ᅧᄃ ᅬᄂ ᆫᄒ ᅳ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅡᄅ ᅭᄋ ᅴᄅ ᆼᄋ ᅣ ᅦᄆ ᆫᄀ ᅵ ᆷᅡ ᅡ ᆫ ᄒᄇ ᆫᄒ ᅧ ᅪᄅ ᆯᄇ ᅳ ᅩᄋ ᆻᄃ ᅧ ᅡ. ᄄ ᅡᄅ ᅡᄉ ᅥᄎ ᅬᄌ ᆼᄌ ᅩ ᆨᄋ ᅥ ᅳᄅ ᅩᄉ ᆫᅥ ᅥ ᆼ ᄌᄒ ᆫᄎ ᅡ ᅬᄌ ᆨᄋ ᅥ ᅴ ᅵᄀ ᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄆ ᅳ ᅩᄒ ᆼᅥ ᅧ ᆯ 서 ᆼ ᄌᄋ ᆫ Table 3.1ᄀ ᅳ ᅪᄀ ᇀᄃ ᅡ ᅡ. 3.2. 비학습지점의 예측성능 평가 계 ᅵ ᄀᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᆫᄋ ᅳ ᇁᄉ ᅡ ᅥᄋ ᆫᄀ ᅥ ᆸᄒ ᅳ ᆫᄇ ᅡ ᅡᅪ ᄋᄀ ᇀᄋ ᅡ ᅵᄋ ᅨᄎ ᆨᄌ ᅳ ᅡᄅ ᅭᄀ ᅡᄒ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅡᄅ ᅭᄋ ᅦᄀ ᅪᄃ ᅩᄒ ᅡᄀ ᅦᄎ ᅵᄋ ᅮᄎ ᅧᄇ ᅵᄒ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅡᄅ ᅭᄋ ᅴᄀ ᆼᄒ ᅧ ᆼᄋ ᅣ ᆯᄄ ᅳ ᅡ ᅳᄌ ᄅ ᅵ ᅩ ᄆᄋ ᅴᄒ ᅡᄌ ᅵ ᄆ ᆺᄒ ᅩ ᅡᄂ ᆫ ᄀ ᅳ ᅪᄃ ᅢᄌ ᆨᄒ ᅥ ᆸᄒ ᅡ ᅡᄂ ᆫ ᄀ ᅳ ᆼᄒ ᅧ ᆼᄋ ᅣ ᆯ ᄀ ᅳ ᅡᄌ ᅵᄀ ᅩ ᄋ ᆻᄃ ᅵ ᅡ. ᄇ ᆫ ᄋ ᅩ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥ ᄉ ᅡᄋ ᆼᄃ ᅭ ᆫ ᄀ ᅬ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸ ᄆ ᅳ ᅩᄒ ᆼᄃ ᅧ ᆯᄋ ᅳ ᆫ ᄂ ᅳ ᆷ ᅡ.
(8) 830. Hyeong-Se Jeong. ᄒᄌ ᆫ ᅡ ᅵᄋ ᆨᄋ ᅧ ᅴ ASOS 95ᄀ ᅢᄌ ᅵᄌ ᆷᄋ ᅥ ᆯᄃ ᅳ ᅢᄉ ᆼᄋ ᅡ ᅳᄅ ᅩᄒ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅵᄌ ᆷᄀ ᅥ ᅪᄇ ᅵᄒ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅵᄌ ᆷᄋ ᅥ ᆯᄇ ᅳ ᆫᄅ ᅮ ᅲᄒ ᅡᄋ ᅧᄉ ᆷᄑ ᅢ ᆯᄅ ᅳ ᆼᄋ ᅵ ᆯᄉ ᅳ ᅮᄒ ᆼᄒ ᅢ ᅡᄋ ᆻᄀ ᅧ ᅵᄄ ᅢᄆ ᆫ ᅮ ᅦᄇ ᄋ ᅵᄒ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅵᄌ ᆷᄋ ᅥ ᅦᄃ ᅢᄒ ᆫᄌ ᅡ ᆼᄅ ᅥ ᆼᄌ ᅣ ᆨᄋ ᅥ ᆫᄋ ᅵ ᅨᄎ ᆨᄉ ᅳ ᆼᄂ ᅥ ᆼᄑ ᅳ ᆼᄀ ᅧ ᅡᄀ ᅡᄋ ᅭᄀ ᅮᄃ ᆫᄃ ᅬ ᅡ. ᄋ ᅵᄅ ᆯᄋ ᅳ ᅱᄒ ᅢᄋ ᇁᄉ ᅡ ᅥᄆ ᆫᄀ ᅵ ᆷᄃ ᅡ ᅩᄐ ᅦᄉ ᅳᄐ ᅳᄋ ᅦᄉ ᅥᄉ ᆫᅥ ᅥ ᆼ ᄌᄃ ᆫ ᅬ 50%ᄋ ᅴᄉ ᆷᄑ ᅢ ᆯᄅ ᅳ ᆼᄋ ᅵ ᆯᄀ ᅳ ᅵᄌ ᆫᄋ ᅮ ᅳᄅ ᅩᄌ ᆫᄎ ᅥ ᅦᄒ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅵᄌ ᆷ 50%ᄅ ᅥ ᆯᄆ ᅳ ᅮᄌ ᆨᄋ ᅡ ᅱᄅ ᅩᄎ ᅮᄎ ᆯᄒ ᅮ ᅡᄋ ᅧᄆ ᅩᄒ ᆼᄋ ᅧ ᆯᄀ ᅳ ᅮᄎ ᆨᄒ ᅮ ᅡᄀ ᅩ, ᄆ ᅩᄒ ᆼᄀ ᅧ ᅮᄎ ᆨᄋ ᅮ ᅦᄉ ᅡ ᆼᄃ ᅭ ᄋ ᅬᅵ ᄌᄋ ᆭᄋ ᅡ ᆫᄇ ᅳ ᅵᄒ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅵᄌ ᆷᄋ ᅥ ᆯᄃ ᅳ ᅢᄉ ᆼᄋ ᅡ ᅳᄅ ᅩᄆ ᅩᄒ ᆼᄋ ᅧ ᆯᄌ ᅳ ᆨᄋ ᅥ ᆼᄒ ᅭ ᅡᄋ ᅧᄌ ᅡᄅ ᅭᄉ ᆫᄎ ᅡ ᆯᄆ ᅮ ᆾᅥ ᅵ ᆷ ᄀᄌ ᆼᅳ ᅳ ᆯ ᄋ 100ᄒ ᅬᄉ ᅮᄒ ᆼᄒ ᅢ ᅡᄋ ᆻᄃ ᅧ ᅡ. Figure 3.2ᄂ ᆫᄒ ᅳ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅵᄌ ᆷᄀ ᅥ ᅪᄇ ᅵᄒ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅵᄌ ᆷᄋ ᅥ ᅦᄃ ᅢᄒ ᆫᄋ ᅡ ᆯᄑ ᅯ ᆼᄀ ᅧ ᆫ RMSEᄋ ᅲ ᅵᄃ ᅡ. ᄀ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄇ ᅳ ᆯᄒ ᅧ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅵᄌ ᆷᄀ ᅥ ᅪᄇ ᅵᄒ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅵᄌ ᆷᄋ ᅥ ᅴᄋ ᆯᄇ ᅯ ᆯ ᅧ RMSE ᄀ ᆼᄒ ᅧ ᆼᄋ ᅣ ᆫᄌ ᅳ ᆫᄎ ᅥ ᅦᄌ ᆨᄋ ᅥ ᅳᄅ ᅩᄋ ᅲᄉ ᅡᄒ ᅡᄆ ᅧ, ᄒ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅵᄌ ᆷᄃ ᅥ ᅢᄇ ᅵᄇ ᅵᄒ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅵᄌ ᆷᄋ ᅥ ᅴ RMSE ᄌ ᆼᄀ ᅳ ᅡᄋ ᆯᄋ ᅲ ᆫᄋ ᅳ ᆨ 10% ᄌ ᅣ ᆼᄃ ᅥ ᅩᄋ ᅵᄃ ᅡ. ᅡᄅ ᄄ ᅡᅥ ᄉᄇ ᅵᄒ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅵᄌ ᆷᄋ ᅥ ᅴᄋ ᅨᄎ ᆨᄋ ᅳ ᆫᄒ ᅳ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅵᄌ ᆷᄋ ᅥ ᅴᄋ ᅨᄎ ᆨᄆ ᅳ ᆫᄏ ᅡ ᆷᄋ ᅳ ᅴᄉ ᆼᄂ ᅥ ᆼᅳ ᅳ ᆯ ᄋᄇ ᅩᄌ ᆼᄒ ᅡ ᅡᄆ ᅧ, ᄆ ᅩᄃ ᆫᄀ ᅳ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄆ ᅳ ᅩᄒ ᆼᄋ ᅧ ᆫᄀ ᅳ ᅪᄃ ᅢᄌ ᆨᄒ ᅥ ᆸ ᅡ ᆼᄒ ᅵ ᄌ ᅮᄀ ᅡᄇ ᅩᄋ ᅵᄌ ᅵᄋ ᆭᄋ ᅡ ᆻᄃ ᅡ ᅡ.. Figure 3.2 Over-fitting test by machine learning techniques. 3.3. 예측 지점별 검증 Figure 3.3ᄋ ᆫᄀ ᅳ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄆ ᅳ ᅩᄒ ᆼᄋ ᅧ ᅦᄄ ᅡᄅ ᆫᄌ ᅳ ᅵᄋ ᆨᅧ ᅧ ᆯ ᄇ biasᄋ ᅪ RMSEᄅ ᆯᄂ ᅳ ᅡᄐ ᅡᄂ ᆫᄃ ᅢ ᅡ. ᄀ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᆫᄀ ᅳ ᅵᄇ ᆸᄋ ᅥ ᅦᄄ ᅡᄅ ᅡᄉ ᆼᄂ ᅥ ᆼ ᅳ ᄋᄎ ᅴ ᅡᄋ ᅵᄀ ᅡᄋ ᆻᄋ ᅵ ᅳᄂ ᅡ, ᄌ ᅵᄌ ᆷᅧ ᅥ ᆯ ᄇ bias (Figure 3.3a-c)ᄋ ᅪ RMSE (Figure 3.3d-f) ᄇ ᆫᄑ ᅮ ᅩᄋ ᅴᄆ ᅩᄃ ᆯᅧ ᅦ ᆯ 벼 ᆼ ᄀᄒ ᆼᄋ ᅣ ᆫᄋ ᅳ ᅲᄉ ᅡᄒ ᅡ ᆻᄃ ᅧ ᄋ ᅡ. ᄂ ᅢᄅ ᆨᄋ ᅲ ᅴᄀ ᆼᄋ ᅧ ᅮ biasᄂ ᆫᄃ ᅳ ᅡᄉ ᅩᄀ ᅪᄃ ᅢᄆ ᅩᄋ ᅴᄀ ᆼᄒ ᅧ ᆼᄋ ᅣ ᆯᄇ ᅳ ᅩᄋ ᅵᄌ ᅵᄆ ᆫ 0ᄋ ᅡ ᅦᄀ ᆫᄌ ᅳ ᆸᄀ ᅥ ᆹᄋ ᅡ ᆯᄇ ᅳ ᅩᄋ ᅵᄆ ᅧ, RMSEᄂ ᆫᄂ ᅳ ᆽᄋ ᅡ ᆫᄀ ᅳ ᆹᄋ ᅡ ᆯ ᅳ ᅡᄐ ᄂ ᅡᅢ ᄂᄃ ᆫ ᅡ. ᄇ ᆫᄆ ᅡ ᆫ, ᄉ ᅧ ᆷᄄ ᅥ ᅩᄂ ᆫᄒ ᅳ ᅢᄋ ᆫᄌ ᅡ ᅵᄋ ᆨᄋ ᅧ ᅦᄉ ᅥᄂ ᆫ biasᄀ ᅳ ᅡᄀ ᅪᄉ ᅩᄆ ᅩᄋ ᅴᄀ ᆼᄒ ᅧ ᆼᄋ ᅣ ᆯᄂ ᅳ ᅡᄐ ᅡᄂ ᅢᄆ ᅧ, RMSEᄂ ᆫᄃ ᅳ ᅡᄉ ᅩᄏ ᆫᄀ ᅳ ᆹᄋ ᅡ ᆯᄇ ᅳ ᅩ ᆫᄃ ᅵ ᄋ ᅡ. ᄋ ᅵᄂ ᆫᄒ ᅳ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅡᄅ ᅭᄅ ᅩᄉ ᅡᄋ ᆼᄃ ᅭ ᆫ ASOS 95ᄀ ᅬ ᅢᄌ ᅵᄌ ᆷᄋ ᅥ ᅴᄌ ᅡᄅ ᅭᄌ ᆼᄂ ᅮ ᅢᄅ ᆨᄌ ᅲ ᅵᄋ ᆨᄋ ᅧ ᅴᄇ ᅵᄋ ᆯᄋ ᅲ ᅵᄂ ᇁᄋ ᅩ ᆷᄋ ᅳ ᅦᄄ ᅡᄅ ᅡᄀ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅴ ᅩᄃ ᄆ ᆯᅦ ᅦ ᄋᄃ ᅩᄂ ᅢᄅ ᆨᄌ ᅲ ᅵᄋ ᆨᄋ ᅧ ᅴᄑ ᆼᄉ ᅮ ᆨᄀ ᅩ ᆼᄒ ᅧ ᆼᄋ ᅣ ᅵᄃ ᅡᄉ ᅮᄇ ᆫᄋ ᅡ ᆼᄃ ᅧ ᅬᄋ ᅥᄂ ᅢᄅ ᆨᄌ ᅲ ᅵᄋ ᆨᄋ ᅧ ᅦᄀ ᅡᄁ ᅡᄋ ᆫᄀ ᅮ ᆹᄋ ᅡ ᆯᄆ ᅳ ᅩᄋ ᅴᅡ ᆫ ᄒᄀ ᆯᄀ ᅧ ᅪᄅ ᅩᄇ ᅩᄋ ᆫᄃ ᅵ ᅡ. ᄄ ᅩᄒ ᆫ ᅡ ᅢᄅ ᄂ ᆨᅵ ᅲ ᄌᄋ ᆨᄋ ᅧ ᆫᅮ ᅳ ᆼ ᄑᄉ ᆨᄋ ᅩ ᅴᄐ ᆨᄉ ᅳ ᆼᄉ ᅥ ᆼᄒ ᅡ ᅢᄋ ᆫᄌ ᅡ ᅵᄋ ᆨᄀ ᅧ ᅪᄃ ᆯᄅ ᅡ ᅵᄂ ᇁᄋ ᅩ ᆫᄌ ᅳ ᅵᄑ ᅭᄆ ᆫᄀ ᅧ ᅥᄎ ᆯᄀ ᅵ ᅵᄀ ᆯᄋ ᅵ ᅵᅪ ᄋᄌ ᅵᄑ ᅭᄆ ᆫᄒ ᅧ ᆫᄀ ᅪ ᆼᄋ ᅧ ᅦᄄ ᅡᄅ ᆫᄋ ᅳ ᆯᅧ ᅵ ᆫ ᄇᄒ ᅪᄋ ᅴ ᆼᅡ ᅣ ᄋ ᆼ 시 ᄋᄃ ᆯᄅ ᅡ ᅡᄌ ᆫᄃ ᅵ ᅡ (Kim ᄃ ᆼ, 2018). ᄄ ᅳ ᅡᄅ ᅡᄉ ᅥᄒ ᅢᄋ ᆫᄌ ᅡ ᅵᄋ ᆨᄋ ᅧ ᅴᄀ ᆷᄌ ᅥ ᆼᄀ ᅳ ᆯᄀ ᅧ ᅪᄂ ᆫᄂ ᅳ ᅢᄅ ᆨᄌ ᅲ ᅵᄋ ᆨᄀ ᅧ ᅪᄒ ᅢᄋ ᆫᄌ ᅡ ᅵᄋ ᆨᄋ ᅧ ᅴᄑ ᆼᄉ ᅮ ᆨᄐ ᅩ ᆨᄉ ᅳ ᆼᄎ ᅥ ᅡ ᅵᄋ ᄋ ᅦᅡ ᄄᄅ ᅡᄃ ᅡᄉ ᅩᄂ ᆽᄋ ᅡ ᆫᄆ ᅳ ᅩᄋ ᅴᄉ ᆼᄂ ᅥ ᆼᅳ ᅳ ᆯ ᄋᄇ ᅩᄋ ᅵᄂ ᆫᄀ ᅳ ᆺᄋ ᅥ ᅳᄅ ᅩᄉ ᅡᄅ ᅭᄃ ᆫᄃ ᅬ ᅡ. 3.4. 구간별 검증 ᆷᄌ ᅥ ᄀ ᆼᄇ ᅳ ᆫᄉ ᅧ ᅮᄋ ᅴᄀ ᆫᄎ ᅪ ᆨᄀ ᅳ ᆹᄋ ᅡ ᅦᄃ ᅢᄒ ᆫᄀ ᅡ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄆ ᅳ ᅩᄃ ᆯᅧ ᅦ ᆯ ᄇᄆ ᆫᄀ ᅵ ᆷᄃ ᅡ ᅩᄇ ᆫᄉ ᅮ ᆨᄋ ᅥ ᆯᄒ ᅳ ᅡᄀ ᅵᄋ ᅱᄒ ᅡᄋ ᅧᄀ ᆫᄎ ᅪ ᆨᄀ ᅳ ᆹᄋ ᅡ ᆯᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄋ ᅡ ᅵᄇ ᆷ ᅥ ᄌᄒ ᅮ ᅪᅡ ᄒᄋ ᅧ ᄀ ᆨ ᄀ ᅡ ᅮᄀ ᆫᄋ ᅡ ᅦ ᄄ ᅡᄅ ᅡ ᄑ ᆼᄀ ᅧ ᆫ Bias, RMSE ᄆ ᅲ ᆾ ᄉ ᅵ ᆼᄃ ᅡ ᅢᄌ ᆨ ᄋ ᅥ ᅨᄎ ᆨᄉ ᅳ ᆼᄂ ᅥ ᆼᅳ ᅳ ᆯ ᄋ ᄑ ᆼᄀ ᅧ ᅡᄒ ᅡᄀ ᅵ ᄋ ᅱᄒ ᅡᄋ ᅧ ᄉ ᆼᄃ ᅡ ᅢᄑ ᆫᄎ ᅧ ᅡ (relative bias; rBias)ᄋ ᅪᄉ ᆼᄃ ᅡ ᅢᄑ ᆼᄀ ᅧ ᆫᄌ ᅲ ᅦᄀ ᆸᄀ ᅩ ᆫᄋ ᅳ ᅩᄎ ᅡ (relative Root mean square error; rRMSE)ᄅ ᆯᄇ ᅳ ᅵᄀ ᅭᄇ ᆫᄉ ᅮ ᆨᄒ ᅥ ᅡᄋ ᆻ ᅧ ᅡ (Figure 3.4). rBiasᄋ ᄃ ᅪ rRMSEᄂ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄋ ᅡ ᅵᄌ ᆼᄋ ᅥ ᅴᄃ ᆫᄃ ᅬ ᅡ. rBias =. RM SE Bias , rRM SE = , Omean Omean. (3.1).
(9) Short-term forecasting for wind speed based on machine learning using weather observation data. 831. ᄋᄀ ᅧ ᅵᅥ ᄉ, Omean ᄂ ᆫᄒ ᅳ ᅢᄃ ᆼᄀ ᅡ ᅮᄀ ᆫᄋ ᅡ ᅴᄀ ᆫᄎ ᅪ ᆨᄌ ᅳ ᅡᄅ ᅭᄑ ᆼᄀ ᅧ ᆫᄑ ᅲ ᆼᄉ ᅮ ᆨᄋ ᅩ ᆯᄂ ᅳ ᅡᄐ ᅡᄂ ᆫᄃ ᅢ ᅡ. ᄑ ᆼᄉ ᅮ ᆨᄋ ᅩ ᅴᄇ ᆷᄌ ᅥ ᅮᄂ ᆫᄆ ᅳ ᅮᄑ ᆼᄋ ᅮ ᅦᄉ ᅥᄇ ᅮᄐ ᅥ 10 m/sᄁ ᅡ ᅵ 2.5 m/s ᄊ ᄌ ᆨᄌ ᅵ ᆼᄀ ᅳ ᅡᄒ ᅡᄃ ᅩᄅ ᆨᄒ ᅩ ᅡᄋ ᆻᄋ ᅧ ᅳᄆ ᅧ, 10 m/s ᄋ ᅵᄉ ᆼᄋ ᅡ ᆫᄆ ᅳ ᅩᄃ ᅮᄀ ᇀᄋ ᅡ ᆫᄇ ᅳ ᆷᄌ ᅥ ᅮᄅ ᅩᄇ ᆫᄅ ᅮ ᅲᄒ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄀ ᅮᄀ ᆫᄇ ᅡ ᆯᅧ ᅧ ᆼ ᄑᄀ ᆫ biasᄂ ᅲ ᆫ ᅳ ᅩᄃ ᄆ ᆫᅩ ᅳ ᄆᄃ ᆯᄋ ᅦ ᅵ 0∼2.5 m/s ᄀ ᅮᄀ ᆫᄋ ᅡ ᅦᄉ ᅥᄆ ᆫᄋ ᅡ ᆼᄋ ᅣ ᅴᄑ ᆫᄎ ᅧ ᅡᄅ ᆯᄇ ᅳ ᅩᄋ ᅵᄀ ᅩᄋ ᅵᄒ ᅮᄋ ᅴᄑ ᆼᄉ ᅮ ᆨᄀ ᅩ ᅮᄀ ᆫᄋ ᅡ ᅦᄉ ᅥᄂ ᆫᄋ ᅳ ᆷᄋ ᅳ ᅴᄑ ᆫᄎ ᅧ ᅡᄀ ᅡᄃ ᅥᄋ ᆨᄏ ᅮ ᅥ ᅵᄂ ᄌ ᆫᅳ ᅳ ᆨ ᄐᄌ ᆼᄋ ᅵ ᆯᄂ ᅳ ᅡᄐ ᅡᄂ ᅢᄋ ᆻᄃ ᅥ ᅡ. ᄋ ᅵᄂ ᆫ bias ᄀ ᅳ ᅡᄀ ᆸᄀ ᅳ ᆨᄒ ᅧ ᅡᄀ ᅦᄌ ᆼᄀ ᅳ ᅡᄒ ᅡᄂ ᆫ 5 m/s ᄋ ᅳ ᅵᄉ ᆼᄋ ᅡ ᅴᄀ ᅮᄀ ᆫᄋ ᅡ ᅦᄉ ᅥᄒ ᅢᄃ ᆼᅡ ᅡ ᆨ ᄒᄉ ᆸᄌ ᅳ ᅡᄅ ᅭᄋ ᅴᄇ ᅵ ᆯᄋ ᅲ ᄋ ᅵᅩ ᄆᄃ ᅮ5%ᄋ ᅵᄒ ᅡᄋ ᆫᄀ ᅵ ᆺᄋ ᅥ ᅦᄀ ᅵᄋ ᆫᄒ ᅵ ᆫᄃ ᅡ ᅡ. ᄌ ᆨ, ᄒ ᅳ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅦᄉ ᅡᄋ ᆼᄃ ᅭ ᆫᄀ ᅬ ᅩᄑ ᆼᄉ ᅮ ᆨᄃ ᅩ ᅢᄋ ᅴᄒ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅡᄅ ᅭᄀ ᅡᄉ ᆼᄃ ᅡ ᅢᄌ ᆨᄋ ᅥ ᅳᄅ ᅩᄌ ᆨᄋ ᅥ ᆷᄋ ᅳ ᅦᄄ ᅡ ᅡ, ᄀ ᄅ ᅩᄑ ᆼᄉ ᅮ ᆨᄃ ᅩ ᅢᄋ ᅴᄑ ᆼᄉ ᅮ ᆨᄆ ᅩ ᅩᄋ ᅴᄋ ᅦᄉ ᅥᄂ ᆫᄃ ᅳ ᅡᄉ ᅩᄄ ᆯᄋ ᅥ ᅥᄌ ᅵᄂ ᆫᄉ ᅳ ᆼᄂ ᅥ ᆼᅳ ᅳ ᆯ ᄋᄇ ᅩᄋ ᅧᄀ ᅩᄑ ᆼᄉ ᅮ ᆨᄃ ᅩ ᅢᄅ ᅩᄀ ᆯᄉ ᅡ ᅮᄅ ᆨᄋ ᅩ ᆷᄋ ᅳ ᅴ biasᄀ ᅡᄏ ᅥᄌ ᅵᄂ ᆫᄀ ᅳ ᆺ ᅥ ᅳᄅ ᄋ ᅩᄑ ᆫᅡ ᅡ ᆫ ᄃᄃ ᆫᄃ ᅬ ᅡ. ᄀ ᅮᄀ ᆫᄇ ᅡ ᆯᄑ ᅧ ᆼᄀ ᅧ ᆫ RMSEᄂ ᅲ ᆫ biasᄋ ᅳ ᅪᄀ ᆼᄒ ᅧ ᆼᄋ ᅣ ᆫᄇ ᅳ ᅵᄉ ᆺᄒ ᅳ ᅡᄂ ᅡ RMSEᄋ ᅴᄐ ᆨᄉ ᅳ ᆼᄉ ᅥ ᆼᄆ ᅡ ᅩᄃ ᅮᄋ ᆼᄋ ᅣ ᅴᄀ ᆹᄋ ᅡ ᅳᄅ ᅩᄂ ᅡ ᅡᄂ ᄐ ᆻᅳ ᅡ ᄋᄆ ᅧ, ᄌ ᅥᄑ ᆼᄉ ᅮ ᆨᄋ ᅩ ᅦᄉ ᅥᄀ ᅩᄑ ᆼᄉ ᅮ ᆨᄋ ᅩ ᅳᄅ ᅩᄀ ᆯᄉ ᅡ ᅮᄅ ᆨ RMSEᄋ ᅩ ᅴᄀ ᆹᄋ ᅡ ᅵᄏ ᅥᄌ ᅵᄂ ᆫᄀ ᅳ ᆼᄒ ᅧ ᆼᄋ ᅣ ᅵᄃ ᅡ. ᄃ ᅢᄎ ᅦᄅ ᅩ 5 m/s ᄋ ᅵᄒ ᅡᄋ ᅴᄌ ᅥᄑ ᆼ ᅮ ᆨᄀ ᅩ ᄉ ᅮᄀ ᆫᄀ ᅡ ᅮᄀ ᆫᄋ ᅡ ᅦᄉ ᅥᄂ ᆫ RMSEᄋ ᅳ ᅴᄀ ᆹᄋ ᅡ ᅵ 1ᄋ ᅦᄀ ᅡᄁ ᅡᄋ ᆫᄀ ᅮ ᆹᄋ ᅡ ᆯᄂ ᅳ ᅡᄐ ᅡᄂ ᅢᄆ ᅧ, ᄉ ᆼᄃ ᅡ ᆼᄒ ᅡ ᅵᄄ ᅱᄋ ᅥᄂ ᆫᄋ ᅡ ᅨᄎ ᆨᄉ ᅳ ᆼᄂ ᅥ ᆼᅳ ᅳ ᆯ ᄋᄇ ᅩᄋ ᆫᄃ ᅵ ᅡ. ᄆ ᅩᄃ ᆯ ᅦ ᆯᄅ ᅧ ᄇ ᅩᄂ ᆫᄀ ᅳ ᆫᄉ ᅳ ᅩᄒ ᅡᄀ ᆫᄒ ᅵ ᅡᄂ ᅡ RF, SVM, DNN ᄉ ᆫᄋ ᅮ ᅳᄅ ᅩᄆ ᅩᄃ ᆫᅮ ᅳ ᆼ ᄑᄉ ᆨᄀ ᅩ ᅮᄀ ᆫᄋ ᅡ ᅦᄉ ᅥᄌ ᇂᄋ ᅩ ᆫᄉ ᅳ ᆼᄂ ᅥ ᆼᅳ ᅳ ᆯ ᄋᄂ ᅡᄐ ᅡᄂ ᅢᄋ ᆻᄃ ᅥ ᅡ. ᄀ ᅮᄀ ᆫᄇ ᅡ ᆯ ᅧ rBiasᄋ ᅪ rRMSEᄋ ᅴᄀ ᆼᄋ ᅧ ᅮ, 0∼2.5 m/s ᄋ ᅦᄉ ᅥᄂ ᆫ SVMᄋ ᅳ ᅵ DNNᄀ ᅪ RFᄋ ᅦᄇ ᅵᄒ ᅢᄀ ᅢᄉ ᆫᄃ ᅥ ᆫᄉ ᅬ ᆼᄂ ᅥ ᆼᅳ ᅳ ᆯ ᄋᄇ ᅩᄋ ᅵᄂ ᅡ, 2.5∼5 m/sᄋ ᅦᄉ ᅥᄂ ᆫᄋ ᅳ ᅩᄒ ᅵᄅ ᅧ SVMᄋ ᅵ DNNᄀ ᅪ RFᄋ ᅦᄇ ᅵᄒ ᅢᄂ ᆽᄋ ᅡ ᆫᄉ ᅳ ᆼᄂ ᅥ ᆼᅳ ᅳ ᆯ ᄋᄂ ᅡᄐ ᅡᄂ ᅢᄋ ᆻᄃ ᅥ ᅡ. 5 m/s ᄋ ᅵᄉ ᆼᄋ ᅡ ᅴᄑ ᆼᄉ ᅮ ᆨᄀ ᅩ ᅮᄀ ᆫᄋ ᅡ ᅦᄉ ᅥ ᆫᄆ ᅳ ᄂ ᅩᄃ ᆫᄀ ᅳ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄀ ᅳ ᅵᄇ ᆸᄋ ᅥ ᅴᄀ ᆹᄋ ᅡ ᅵᄌ ᆷᄎ ᅥ ᅡᄌ ᆨᄋ ᅥ ᅳᄅ ᅩᄌ ᆼᄀ ᅮ ᅡᄒ ᅡᄂ ᆫᄀ ᅳ ᆼᄒ ᅧ ᆼᄋ ᅣ ᅵᄃ ᅡ. ᄋ ᅵᄂ ᆫᄀ ᅳ ᆫᄎ ᅪ ᆨᄑ ᅳ ᆼᄉ ᅮ ᆨᄋ ᅩ ᅴᄇ ᆫᄑ ᅮ ᅩᄀ ᅡᄌ ᅥᄑ ᆼᄉ ᅮ ᆨᄃ ᅩ ᅢᄋ ᅦᄌ ᆸ ᅵ ᆼᄃ ᅮ ᄌ ᅬᅥ ᄋᄋ ᆻᄀ ᅵ ᅵᄄ ᅢᄆ ᆫᄋ ᅮ ᅦᄉ ᆼᄃ ᅡ ᅢᄌ ᆨᄋ ᅥ ᅳᄅ ᅩᄀ ᅩᄑ ᆼᄉ ᅮ ᆨᄃ ᅩ ᅢᄋ ᅴᄋ ᅨᄎ ᆨᄉ ᅳ ᆼᄂ ᅥ ᆼᄋ ᅳ ᅵᄌ ᅥᄒ ᅡᅬ ᄃᄂ ᆫᄀ ᅳ ᆺᄋ ᅥ ᅳᄅ ᅩᄇ ᅩᄋ ᅵᄆ ᅧ, ᄋ ᅵᄅ ᅥᄒ ᆫᄆ ᅡ ᆫᄌ ᅮ ᅦᄅ ᆯᄀ ᅳ ᅢᄉ ᆫᄒ ᅥ ᅡ ᅵᄋ ᄀ ᅱᅢ ᄒᄉ ᅥᄂ ᆫᄎ ᅳ ᅮᄀ ᅡᄌ ᆨᄋ ᅥ ᆫᅧ ᅵ ᆫ ᄋᄀ ᅮᄀ ᅡᄑ ᆯᄋ ᅵ ᅭᄒ ᆯᅥ ᅡ ᆺ ᄀᄋ ᅳᄅ ᅩᄉ ᅡᄅ ᅭᄃ ᆫᄃ ᅬ ᅡ.. Figure 3.3 Verification of wind speed of DNN (left), SVM (middle), RF (right) at ASOS sites using bias (top), RMSE (bottom).
(10) 832. Hyeong-Se Jeong. Figure 3.4 The verification and frequency of data each sectional for wind speed, a) Bias, b) RMSE, c) rBias, d) rRMSE. 3.5. 기계학습별 성능비교 Figure 3.5ᄂ ᆫᄉ ᅳ ᅵᄀ ᆫᄇ ᅡ ᆯᄀ ᅧ ᅪᄋ ᆯᅧ ᅵ ᆯ ᄇᄋ ᅦᄄ ᅡᄅ ᆫᄀ ᅳ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄇ ᅳ ᆯ RMSE ᄇ ᅧ ᆫᄑ ᅮ ᅩᄋ ᅵᄃ ᅡ. ᄆ ᅩᄃ ᆫᄀ ᅳ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄀ ᅳ ᅵᄇ ᆸᄋ ᅥ ᆫᄋ ᅳ ᅧᄅ ᆷ, ᄀ ᅳ ᅡᄋ ᆯ, ᅳ ᆷ, ᄀ ᅩ ᄇ ᅧᄋ ᆯᅮ ᅮ ᆫ ᄉᄋ ᅳᄅ ᅩ RMSEᄀ ᅡᄂ ᆽᄋ ᅡ ᆫᅮ ᅳ ᆫ ᄇᄑ ᅩᄅ ᆯᄇ ᅳ ᅩᄋ ᆫᄃ ᅵ ᅡ. ᄋ ᅵᄂ ᆫ Figure. 3.4ᄋ ᅳ ᅦᄉ ᅥᄋ ᆫᄀ ᅥ ᆸᄒ ᅳ ᆫᄇ ᅡ ᅡᅪ ᄋᄀ ᇀᄋ ᅡ ᅵᄒ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅡᄅ ᅭᄋ ᅦᄀ ᅩ ᆼᄉ ᅮ ᄑ ᆨᅢ ᅩ ᄃᄋ ᅴᄑ ᆼᄉ ᅮ ᆨᄇ ᅩ ᅵᄋ ᆯᄋ ᅲ ᅵᄌ ᆨᄀ ᅡ ᅵᄄ ᅢᄆ ᆫᄋ ᅮ ᅦᄀ ᅨᄌ ᆯᄌ ᅥ ᆼᄉ ᅮ ᅵᄇ ᅦᄅ ᅵᄋ ᅡᄀ ᅩᄀ ᅵᄋ ᆸᄋ ᅡ ᅴᄋ ᆼᄒ ᅧ ᆼᄋ ᅣ ᅳᄅ ᅩᄑ ᆼᄉ ᅮ ᆨᄋ ᅩ ᅵᄀ ᆼᄒ ᅡ ᅪᄃ ᅬᄂ ᆫᄀ ᅳ ᅧᄋ ᆯᄎ ᅮ ᆯᄑ ᅥ ᆼᄉ ᅮ ᆨᄋ ᅩ ᅴ ᅩᄋ ᄆ ᅴᄉ ᆼᄂ ᅥ ᆼᄋ ᅳ ᅵᄃ ᅡᄉ ᅩᄄ ᆯᄋ ᅥ ᅥᄌ ᅵᄂ ᆫᄀ ᅳ ᆺᄋ ᅥ ᅳᄅ ᅩᄑ ᆫᅡ ᅡ ᆫ ᄃᄃ ᆫᄃ ᅬ ᅡ. ᄋ ᆯᄇ ᅯ ᆯᄎ ᅧ ᅬᄃ ᅢ RMSEᄋ ᅴᄀ ᆼᄋ ᅧ ᅮ SVMᄀ ᅪ RFᄂ ᆫ 2018ᄂ ᅳ ᆫ 2ᄋ ᅧ ᆯ (ᄀ ᅯ ᆨᄀ ᅡ ᆨ ᅡ 1.80 m/sᄋ ᅪ 1.84 m/s)ᄋ ᅵᄆ ᅧ, DNNᄋ ᆫ 2018ᄂ ᅳ ᆫ 3ᄋ ᅧ ᆯ (1.90 m/s)ᄋ ᅯ ᅦᄂ ᅡᄐ ᅡᄂ ᆻᄃ ᅡ ᅡ. ᄋ ᆯᄇ ᅯ ᆯᄎ ᅧ ᅬᄉ ᅩ RMSEᄂ ᆫ DNNᄀ ᅳ ᅪ RFᄋ ᅵ 2018ᄂ ᆫ 6ᄋ ᅧ ᆯ (ᄀ ᅯ ᆨᄀ ᅡ ᆨ 1.36 m/sᄋ ᅡ ᅪ 1.29 m/s), SVMᄋ ᆫ 7ᄋ ᅳ ᆯ (1.30 m/s)ᄋ ᅯ ᅦᄂ ᅡᄐ ᅡᄂ ᆻᄃ ᅡ ᅡ. ᄉ ᅵᄀ ᆫᄇ ᅡ ᆯ RMSEᄂ ᅧ ᆫ ᅳ 0∼6ᄉ ᅵᄋ ᅦᄋ ᆯᅥ ᅵ ᆼ ᄌᄒ ᅡᄀ ᅦᄂ ᆽᄋ ᅡ ᆫᄀ ᅳ ᆹᄋ ᅡ ᆯᄇ ᅳ ᅩᄋ ᅵᄀ ᅩᄌ ᆷᄎ ᅥ ᅡᄀ ᆹᄋ ᅡ ᅵᄌ ᆼᄀ ᅳ ᅡᄒ ᅡᄋ ᅧ 15ᄉ ᅵᄋ ᅦᄎ ᅬᄃ ᅢᄀ ᆹᄋ ᅡ ᆯᄂ ᅳ ᅡᄐ ᅡᄂ ᅢᄀ ᅩᄋ ᅵᄒ ᅮᄉ ᅵᄀ ᆫᄃ ᅡ ᅢᄋ ᅦᄉ ᅥ ᆷᄉ ᅡ ᄀ ᅩᄒ ᅡᄃ ᅡᄀ ᅡᄋ ᅣᄀ ᆫᄋ ᅡ ᅵᅬ ᄃᄆ ᆫᄋ ᅧ ᆯᅥ ᅵ ᆼ ᄌᄒ ᅡᄀ ᅦᄋ ᅲᄌ ᅵᄃ ᆫᄃ ᅬ ᅡ. ᄋ ᆯᄇ ᅵ ᆫᄌ ᅡ ᆨᄋ ᅥ ᅳᄅ ᅩᄑ ᆼᄉ ᅮ ᆨᄋ ᅩ ᆫᄌ ᅳ ᅮᄀ ᆫᄋ ᅡ ᅦᄐ ᅢᄋ ᆼᄇ ᅣ ᆨᄉ ᅩ ᅡᄅ ᅩᄋ ᆫᄒ ᅵ ᅢᄃ ᅢᄀ ᅵᄀ ᅡᄇ ᆯᄋ ᅮ ᆫ ᅡ ᆼᄒ ᅥ ᄌ ᅢᄌ ᅵᄀ ᅩᄌ ᅵᄑ ᅭᄂ ᆫᄅ ᅡ ᅲᄀ ᅡᄀ ᆼᄒ ᅡ ᅢᄌ ᅧᄑ ᆼᄉ ᅮ ᆨᄋ ᅩ ᅵᄀ ᆼᄒ ᅡ ᅢᄌ ᅵᄆ ᅧ, ᄋ ᅣᄀ ᆫᄋ ᅡ ᅦᄂ ᆫᄃ ᅳ ᅢᄀ ᅵᄀ ᅡᄋ ᆫᄌ ᅡ ᆼᄒ ᅥ ᅢᄌ ᆷᄋ ᅵ ᅦᄄ ᅡᄅ ᅡᄑ ᆼᄉ ᅮ ᆨᄋ ᅩ ᅵᄉ ᆼᄃ ᅡ ᅢᄌ ᆨᄋ ᅥ ᅳᄅ ᅩᄂ ᆽ ᅡ ᅡᄌ ᄋ ᆫᅡ ᅵ ᄃ. ᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅦᄉ ᅡᄋ ᆼᄃ ᅭ ᆫᄑ ᅬ ᆼᄉ ᅮ ᆨᄌ ᅩ ᅡᄅ ᅭᄂ ᆫᄌ ᅳ ᅮᄀ ᆫᄋ ᅡ ᅴᄀ ᆼᄒ ᅡ ᅢᄌ ᅵᄂ ᆫᄑ ᅳ ᆼᄉ ᅮ ᆨᄋ ᅩ ᅵᄂ ᅡᄐ ᅡᄂ ᅡᄂ ᆫᄉ ᅳ ᅵᄀ ᆫᄃ ᅡ ᅢ (10∼20ᄉ ᅵ)ᄋ ᅴᄇ ᅵᄋ ᆯᄇ ᅲ ᅩᄃ ᅡᄂ ᆫ ᅳ ᆼᄉ ᅮ ᄑ ᆨᅵ ᅩ ᄋᄋ ᆨᄒ ᅣ ᅢᄌ ᅵᄂ ᆫᄋ ᅳ ᅣᄀ ᆫᄉ ᅡ ᅵᄀ ᆫᄃ ᅡ ᅢ (21ᄉ ᅵ∼9ᄉ ᅵ)ᄋ ᅴᄇ ᅵᄋ ᆯᄋ ᅲ ᅵᄃ ᅥᄂ ᇁᄋ ᅩ ᆻᄃ ᅡ ᅡ. ᄌ ᆨ, ᄀ ᅳ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᆫᄒ ᅳ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅡᄅ ᅭᄋ ᅦᄌ ᅵᄇ ᅢᄌ ᆨᄋ ᅥ ᆷᄋ ᅵ ᅦ ᅡᄅ ᄄ ᅡᅮ ᄌᄀ ᆫᄋ ᅡ ᅴᅡ ᆼ 가 ᆫ ᄒᄑ ᆼᄉ ᅮ ᆨᄇ ᅩ ᅩᄃ ᅡᄋ ᅣᄀ ᆫᄋ ᅡ ᅴᄋ ᆨᅡ ᅣ ᆫ ᄒᄑ ᆼᄉ ᅮ ᆨᅳ ᅩ ᆯ ᄋᄃ ᅥᄆ ᆭᄋ ᅡ ᅵᄒ ᆨᄉ ᅡ ᆸᄒ ᅳ ᅡᄋ ᆻᄀ ᅧ ᅵᄄ ᅢᄆ ᆫᄋ ᅮ ᅦᄀ ᆼᄒ ᅡ ᆫᄑ ᅡ ᆼᄉ ᅮ ᆨᄋ ᅩ ᆯᄆ ᅳ ᅩᄋ ᅴᄒ ᅡᄂ ᆫᄉ ᅳ ᆼᄂ ᅥ ᆼ ᅳ ᅵᄃ ᄋ ᅡᄉ ᅩᄄ ᆯᄋ ᅥ ᅥᄌ ᅵᄂ ᆫᄀ ᅳ ᆼᄒ ᅧ ᆼᄋ ᅣ ᆯᄇ ᅳ ᅩᄋ ᆫᄃ ᅵ ᅡ. ᄋ ᆯᅧ ᅵ ᆯ ᄇᄌ ᆼᄀ ᅮ ᅡᄌ ᆼᄏ ᅡ ᆫ RMSEᄂ ᅳ ᆫ 8ᄋ ᅳ ᆯ 24ᄋ ᅯ ᆯ (ᄋ ᅵ ᆨ 4 m/s)ᄋ ᅣ ᅦᄂ ᅡᄐ ᅡᄂ ᆻᄋ ᅡ ᅳᄆ ᅧ, ᄋ ᅵᄂ ᆫ ᅳ ᅦ 19ᄒ ᄌ ᅩᄐ ᅢᄑ ᆼᅩ ᅮ ᆯ ᄉᄅ ᆨ (SOULIK)ᄋ ᅵ ᅴᄋ ᆼᄒ ᅧ ᆼᄋ ᅣ ᅳᄅ ᅩᄇ ᆫᄉ ᅮ ᆨᄃ ᅥ ᅬᄋ ᆻᄃ ᅥ ᅡ. Table 3.2ᄂ ᆫᄀ ᅳ ᆨᄆ ᅡ ᅩᄃ ᆯᅧ ᅦ ᆯ ᄇ biasᄋ ᅪ RMSE ᄀ ᅳᄅ ᅵᄀ ᅩ CPU ᅡ ᄉᄋ ᆼᄉ ᅭ ᅵᄀ ᆫᄋ ᅡ ᆯᄂ ᅳ ᅡᄐ ᅡᄂ ᆫᅧ ᅢ ᆯ ᄀᄀ ᅪᄋ ᅵᄃ ᅡ. ᄆ ᅩᄃ ᆯᅧ ᅦ ᆯ ᄇ RMSEᄂ ᆫ SVMᄀ ᅳ ᅪ RF (1.66 m/s)ᄀ ᅡᄀ ᇀᄀ ᅡ ᅩ. ᄃ ᆯᄋ ᅮ ᅦᄇ ᅵᄒ ᅢ DNN (1.70 m/s)ᄋ ᆫᄃ ᅳ ᅡᄉ ᅩᄄ ᆯᄋ ᅥ ᅥᄌ ᅵᄂ ᆫᄀ ᅳ ᆯᄀ ᅧ ᅪᄅ ᆯᄇ ᅳ ᅩᄋ ᆻᄃ ᅧ ᅡ. ᄒ ᅡᄌ ᅵᄆ ᆫ SVMᄋ ᅡ ᆫᄆ ᅳ ᅩᄃ ᆯᄉ ᅦ ᆼᅥ ᅢ ᆼ ᄉᄉ ᅵᄉ ᅩᄋ ᅭᄃ ᅬᄂ ᆫᄉ ᅳ ᅵᄀ ᆫᄌ ᅡ ᅡᄋ ᆫᄋ ᅯ ᅵ RF ᄃ ᅢ ᅵᄋ ᄇ ᆨ 3ᄇ ᅣ ᅢᄋ ᆫᄌ ᅵ ᆷᄋ ᅥ ᆯᄀ ᅳ ᆷᅡ ᅡ ᆫ 아 ᆫ ᄒᄃ ᅡᄆ ᆫ, ᄃ ᅧ ᆫᄀ ᅡ ᅵᄑ ᆼᄉ ᅮ ᆨᅳ ᅩ ᆯ ᄋᄋ ᅨᄎ ᆨᄒ ᅳ ᅡᄂ ᆫᄀ ᅳ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄀ ᅳ ᅵᄇ ᆸᄋ ᅥ ᅳᄅ ᅩᄀ ᅡᄌ ᆼᄌ ᅡ ᆨᅥ ᅥ ᆯ ᄌᄒ ᆫᄆ ᅡ ᅩᄒ ᆼᄋ ᅧ ᆫ RFᄅ ᅳ ᅩᄑ ᆫᅡ ᅡ ᆫ ᄃ ᆫᄃ ᅬ ᄃ ᅡ..
(11) Short-term forecasting for wind speed based on machine learning using weather observation data. Figure 3.5 Month-time distribution of RMSE: (a) DNN, (b) SVM, (c) RF. 833.
(12) 834. Hyeong-Se Jeong. Table 3.2 A comparison of wind speed forecasting performance according to machine learning DNN SVM RF. bias 0.002 -0.250 -0.001. RMSE 1.70 1.66 1.66. cpu time (second) 35 58 21. 3.6. Random forest의 변수 중요도 ᅬᄌ ᄎ ᆼᄉ ᅩ ᆫᅥ ᅥ ᆼ ᄌᄃ ᆫ Random forestᄂ ᅬ ᆫᄋ ᅳ ᆫᄃ ᅱ ᅩᄋ ᅮᄉ ᆯᄅ ᅳ ᅡᄋ ᅵᄃ ᆼᄀ ᅵ ᅵᄇ ᆸᄋ ᅥ ᆯᄒ ᅳ ᆯᄋ ᅪ ᆼᄒ ᅭ ᆷᄋ ᅡ ᅦᄄ ᅡᄅ ᅡᄀ ᆨᄋ ᅡ ᆯᄆ ᅵ ᅡᄃ ᅡ 1ᄀ ᅢᄋ ᅴᄆ ᅩᄒ ᆼᄋ ᅧ ᅵᄀ ᅮᄎ ᆨ ᅮ ᄃᄋ ᆷ ᅬ ᅦᄄ ᅡᄅ ᅡᄌ ᆼᄋ ᅮ ᅭᄃ ᅩᄄ ᅩᄒ ᆫᄀ ᅡ ᆨᄆ ᅡ ᅩᄃ ᆯᄆ ᅦ ᅡᄃ ᅡ 1ᄀ ᅢᄊ ᆨᄋ ᅵ ᅴᄌ ᆼᄋ ᅮ ᅭᄃ ᅩᄀ ᅡᄉ ᆼᄉ ᅢ ᆼᄃ ᅥ ᆫᄃ ᅬ ᅡ. Figure 3.6ᄋ ᆫᄀ ᅳ ᆨᄆ ᅡ ᅩᄒ ᆼᄋ ᅧ ᅴᄇ ᆫᄉ ᅧ ᅮᄌ ᆼᄋ ᅮ ᅭᄃ ᅩ ᆯᄉ ᅳ ᄅ ᆫᄎ ᅡ ᆯᄒ ᅮ ᅮᄑ ᆼᄀ ᅧ ᆫᄒ ᅲ ᅡᄀ ᅩ, ᄀ ᅳᄌ ᆼᄀ ᅮ ᅡᄌ ᆼᄂ ᅡ ᇁᄋ ᅩ ᆫᄇ ᅳ ᆫᄉ ᅧ ᅮᄅ ᆯᄀ ᅳ ᅵᄌ ᆫᄋ ᅮ ᅳᄅ ᅩᄇ ᆫᄉ ᅧ ᅮᄇ ᆯᄉ ᅧ ᆼᄃ ᅡ ᅢᄌ ᆨᄏ ᅥ ᅳᄀ ᅵᄅ ᆯᄂ ᅳ ᅡᄐ ᅡᄂ ᆫᄀ ᅢ ᅳᄅ ᆷᄋ ᅵ ᅵᄃ ᅡ. ᄃ ᆫᄀ ᅡ ᅵᄑ ᆼ ᅮ ᆨᄋ ᅩ ᄉ ᅴᅨ ᄋᄎ ᆨᄋ ᅳ ᅦᄉ ᅥᄇ ᆫᄉ ᅧ ᅮᄇ ᆯᄌ ᅧ ᆼᄋ ᅮ ᅭᄃ ᅩᄂ ᆫᄉ ᅳ ᆼᄃ ᅡ ᅢᄉ ᆸᄃ ᅳ ᅩ, ᄑ ᆼᄒ ᅮ ᆼ, ᄋ ᅣ ᅱᄃ ᅩ, ᄀ ᆼᄃ ᅧ ᅩᄉ ᆫᄋ ᅮ ᅳᄅ ᅩ 80% ᄋ ᅵᄉ ᆼᄋ ᅡ ᅴᄂ ᇁᄋ ᅩ ᆫᄀ ᅳ ᆹᄋ ᅡ ᆯᄇ ᅳ ᅩᄋ ᆫᄃ ᅵ ᅡ. ᄋ ᅧ ᅵᄉ ᄀ ᅥᄉ ᆼᄃ ᅡ ᅢᄉ ᆸᄃ ᅳ ᅩᄂ ᆫᄂ ᅳ ᆯᄊ ᅡ ᅵᄋ ᅦᄄ ᅡᄅ ᆫᅮ ᅳ ᆼ ᄑᄉ ᆨᄇ ᅩ ᆫᄒ ᅧ ᅪᄅ ᆯᄇ ᅳ ᆫᄋ ᅡ ᆼᄒ ᅧ ᅡᄆ ᅧ, ᄑ ᆼᄒ ᅮ ᆼᄋ ᅣ ᆫᄀ ᅳ ᅨᄌ ᆯᄑ ᅥ ᆼᄄ ᅮ ᅩᄂ ᆫᄒ ᅳ ᅢᄅ ᆨᅮ ᅲ ᆼ ᄑᄋ ᅳᄅ ᅩᄋ ᆫᄒ ᅵ ᆫᄑ ᅡ ᆼᄉ ᅮ ᆨᄇ ᅩ ᆫᄒ ᅧ ᅪᄅ ᆯ ᅳ ᅩᄅ ᄀ ᅧᅡ ᄒᄂ ᆫᄀ ᅳ ᆺᄋ ᅥ ᅳᄑ ᅩᄇ ᅩᄋ ᆫᄃ ᅵ ᅡ. ᄄ ᅩᄒ ᆫ, ᄋ ᅡ ᅱᄃ ᅩᅪ ᄋᄀ ᆼᄃ ᅧ ᅩᄂ ᆫᄋ ᅳ ᅨᄎ ᆨᄌ ᅳ ᅵᄌ ᆷᄋ ᅥ ᅴᄋ ᆸᄌ ᅵ ᅵᄒ ᆫᄀ ᅪ ᆼ(ᄒ ᅧ ᅢᄋ ᆼ, ᄉ ᅣ ᆫᄋ ᅡ ᆨ, ᄋ ᅡ ᆨᄌ ᅲ ᅵᄃ ᆼ)ᄋ ᅳ ᅦᄄ ᅡᄅ ᆫᄑ ᅳ ᆼᄉ ᅮ ᆨ ᅩ ᆫᄒ ᅧ ᄇ ᅪᄅ ᆯᄆ ᅳ ᅩᄃ ᆯᄋ ᅦ ᅦᄇ ᆫᄋ ᅡ ᆼᄒ ᅧ ᅡᄂ ᆫᄀ ᅳ ᆺᄋ ᅥ ᅳᄅ ᅩᄑ ᆫᅡ ᅡ ᆫ ᄃᄃ ᆫᄃ ᅬ ᅡ. ᄇ ᆫᄆ ᅡ ᆫ, ᄀ ᅧ ᆼᄉ ᅡ ᅮᄅ ᆼᄋ ᅣ ᆫᄀ ᅳ ᆼᄉ ᅡ ᅮᄋ ᅴᄋ ᅲᄆ ᅮᄋ ᅦᄄ ᅡᄅ ᅡᄇ ᆫᄃ ᅧ ᆼᄋ ᅩ ᅴᄑ ᆨᄋ ᅩ ᅵᄆ ᅢᄋ ᅮᄏ ᆫᄇ ᅳ ᆫ ᅧ ᅮᄋ ᄉ ᅵᅳ ᄆᄅ ᅩᄑ ᆼᄉ ᅮ ᆨᄋ ᅩ ᅨᄎ ᆨᄋ ᅳ ᅦᄉ ᅥᄉ ᅡᄋ ᆼᄃ ᅭ ᆫᄇ ᅬ ᆫᄉ ᅧ ᅮᄌ ᆼᄀ ᅮ ᅡᄌ ᆼᄌ ᅡ ᆼᄋ ᅮ ᅭᄃ ᅩᄀ ᅡᄂ ᆽᄀ ᅡ ᅦᄂ ᅡᄐ ᅡᄂ ᅡᄂ ᆫᄐ ᅳ ᆨᄌ ᅳ ᆼᄋ ᅵ ᆯᄇ ᅳ ᅩᄋ ᆻᄃ ᅧ ᅡ.. Figure 3.6 Variable importance based on random foreset. 4. 요약 및 결론 ᆫᄋ ᅩ ᄇ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫᄀ ᅳ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸ (ᄉ ᅳ ᆷᄎ ᅵ ᆼᄉ ᅳ ᆫᅧ ᅵ ᆼ ᄀᄆ ᆼ, ᄉ ᅡ ᅥᄑ ᅩᄐ ᅳᄇ ᆨᄐ ᅦ ᅥᄆ ᅥᄉ ᆫ, ᄅ ᅵ ᆫᅥ ᅢ ᆷ ᄃᄑ ᅩᄅ ᅦᄉ ᅳᄐ ᅳ)ᄋ ᆯᄒ ᅳ ᆯᄋ ᅪ ᆼᄒ ᅭ ᅡᄋ ᅧᄃ ᆫᄀ ᅡ ᅵᄑ ᆼᄉ ᅮ ᆨᄋ ᅩ ᅨᄎ ᆨ ᅳ ᄆᄃ ᅩ ᆯᄋ ᅦ ᆯᄉ ᅳ ᆼᄉ ᅢ ᆼᄒ ᅥ ᅡᄀ ᅩ, ᄋ ᅵᄅ ᆯᄑ ᅳ ᆼᄀ ᅧ ᅡᄒ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄒ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅡᄅ ᅭᄅ ᅩᄂ ᆫ ASOS 95ᄀ ᅳ ᅢᄌ ᅵᄌ ᆷᄋ ᅥ ᅴᄆ ᅢᄉ ᅵᄀ ᆫᄑ ᅡ ᆼᄉ ᅮ ᆨᄋ ᅩ ᆯᄌ ᅳ ᆼᄉ ᅩ ᆨᄇ ᅩ ᆫᄉ ᅧ ᅮᄅ ᅩᄉ ᆯᅥ ᅥ ᆼ ᄌ ᅡᄋ ᄒ ᆻᅩ ᅧ ᄀ, ᄋ ᅵᄋ ᅬᄋ ᅴ 4ᄀ ᅡᄌ ᅵ (ᄀ ᅵᄋ ᆫ, ᄑ ᅩ ᆼᄒ ᅮ ᆼ, ᄉ ᅣ ᆸᄃ ᅳ ᅩ, ᄀ ᆼᄉ ᅡ ᅮ) ᄀ ᅵᄉ ᆼᄇ ᅡ ᆫᄉ ᅧ ᅮᅪ ᄋᄌ ᅵᄌ ᆷᄋ ᅥ ᅴᄋ ᅱᄀ ᆼᄃ ᅧ ᅩ, ᄉ ᅵᄀ ᆫᄇ ᅡ ᆫᄉ ᅧ ᅮᄅ ᆯᄃ ᅳ ᆨᄅ ᅩ ᆸᄇ ᅵ ᆫᄉ ᅧ ᅮᄅ ᅩᄉ ᅡ ᆼᄒ ᅭ ᄋ ᅡᅧ ᆻ ᄋᄃ ᅡ. ᄇ ᆫᄋ ᅩ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫᄀ ᅳ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄇ ᅳ ᆯᅧ ᅧ ᆼ ᄑᄀ ᅡᄅ ᆯᄋ ᅳ ᅱᄒ ᅡᄋ ᅧᄆ ᆫᄀ ᅵ ᆷᄃ ᅡ ᅩᄐ ᅦᄉ ᅳᄐ ᅳᄅ ᆯᄉ ᅳ ᆫᄒ ᅥ ᆼᄒ ᅢ ᅡᄋ ᅧᅬ ᄎᄌ ᆨᄆ ᅥ ᅩᄒ ᆼᄋ ᅧ ᆯᄉ ᅳ ᆫᅥ ᅥ ᆼ ᄌᄒ ᅡᄀ ᅩᄌ ᅡ ᅡᄋ ᄒ ᆻᅡ ᅧ ᄃ. ᄉ ᆫᅥ ᅥ ᆼ ᄌᄃ ᆫᄎ ᅬ ᅬᄌ ᆨᄆ ᅥ ᅩᄒ ᆼᄋ ᅧ ᆫᄒ ᅳ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅵᄌ ᆷᄀ ᅥ ᅪᄇ ᅵᄒ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅵᄌ ᆷᄋ ᅥ ᆯᄋ ᅳ ᆷᄋ ᅵ ᅴᄅ ᅩᄉ ᆫᅥ ᅥ ᆼ ᄌᄒ ᅡᄋ ᅧᄇ ᆫᄉ ᅮ ᆨᄒ ᅥ ᆫᅧ ᅡ ᆯ ᄀᄀ ᅪᄀ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᆯᄋ ᅳ ᅵᄋ ᆼ ᅭ ᆫᄋ ᅡ ᄒ ᅨᅳ ᆨ ᄎᄋ ᅦᄉ ᅥᄌ ᅮᄋ ᅴᄒ ᅡᄋ ᅧᄋ ᅣᄒ ᆯᄀ ᅡ ᅪᄃ ᅢᄌ ᆨᄒ ᅥ ᆸᄋ ᅡ ᅴᄆ ᆫᄌ ᅮ ᅦᄀ ᅡᄋ ᆹᄂ ᅥ ᆫᄀ ᅳ ᆺᄋ ᅥ ᅳᄅ ᅩᄑ ᆫᄃ ᅡ ᆫᄒ ᅡ ᅡᄋ ᅧᄇ ᆫᄉ ᅮ ᆨᄋ ᅥ ᅦᄒ ᆯᄋ ᅪ ᆼᄒ ᅭ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄀ ᆨᄀ ᅡ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸ ᅳ ᆯᄇ ᅧ ᄇ ᆫᄉ ᅮ ᆨᅧ ᅥ ᆯ ᄀᄀ ᅪ, ᄌ ᅵᄋ ᆨᄇ ᅧ ᆯᄅ ᅧ ᅩᄂ ᆫᄆ ᅳ ᅩᄃ ᆫᄀ ᅳ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅵᄉ ᆷᄄ ᅥ ᅩᄂ ᆫᄒ ᅳ ᅢᄋ ᆫᄌ ᅡ ᅵᄋ ᆨᄋ ᅧ ᅦᄉ ᅥᅪ ᄀᄉ ᅩᄆ ᅩᄋ ᅴᄒ ᅡᄂ ᆫᄀ ᅳ ᆼᄒ ᅧ ᆼᄋ ᅣ ᆯᄇ ᅳ ᅩᄋ ᅵᄆ ᅧ, ᄆ ᅩᄃ ᆫᄀ ᅳ ᆷ ᅥ.
(13) Short-term forecasting for wind speed based on machine learning using weather observation data. 835. ᄌᄌ ᆼ ᅳ ᅵᄉ ᅮᄀ ᅡᄏ ᆫᄀ ᅳ ᆹᄋ ᅡ ᆯᄇ ᅳ ᅩᄋ ᆻᄃ ᅧ ᅡ. ᄋ ᅵᄂ ᆫᄀ ᅳ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅴᄒ ᆨᄉ ᅡ ᆸᄌ ᅳ ᅡᄅ ᅭᄅ ᅩᄉ ᅡᄋ ᆼᄃ ᅭ ᅬᄂ ᆫᄌ ᅳ ᅡᄅ ᅭᄋ ᅴᄇ ᅵᄋ ᆯᄋ ᅲ ᅵᄉ ᆷᄄ ᅥ ᅩᄂ ᆫᄒ ᅳ ᅢᄋ ᆫᄌ ᅡ ᅵᄋ ᆨᄋ ᅧ ᅴᄌ ᅡ ᅭᄇ ᄅ ᅩᄃ ᅡᄂ ᅢᄅ ᆨᄌ ᅲ ᅵᄋ ᆨᄋ ᅧ ᅴᄇ ᅵᄋ ᆯᄋ ᅲ ᅵᄂ ᇁᄋ ᅩ ᅡᄀ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅦᄂ ᅢᄅ ᆨᄌ ᅲ ᅵᄋ ᆨᄋ ᅧ ᅴᄑ ᆼᄉ ᅮ ᆨᄇ ᅩ ᅵᄋ ᆯᄋ ᅲ ᅵᄂ ᇁᄀ ᅩ ᅦᄌ ᆨᄋ ᅥ ᆼᄃ ᅭ ᆫᄀ ᅬ ᆺᄋ ᅥ ᅳᄅ ᅩᄑ ᆫᅡ ᅡ ᆫ ᄃᄃ ᆫᄃ ᅬ ᅡ. ᄑ ᆼ ᅮ ᆨᄀ ᅩ ᄉ ᅮᄀ ᆫᄇ ᅡ ᆯᅥ ᅧ ᆷ ᄀᄌ ᆼᄋ ᅳ ᅦᄉ ᅥᄂ ᆫᄌ ᅳ ᅥᄑ ᆼᄉ ᅮ ᆨᄃ ᅩ ᅢ (0∼5 m/s)ᄋ ᅴᄇ ᅵᄋ ᆯᄋ ᅲ ᅵᄋ ᆨ 93%ᄋ ᅣ ᅵᄆ ᅳᄅ ᅩ, ᄌ ᅥᄑ ᆼᄉ ᅮ ᆨᄃ ᅩ ᅢᄋ ᅦᄉ ᅥᄂ ᆫᅩ ᅳ ᇁ ᄂᄋ ᆫᄉ ᅳ ᆼᄂ ᅥ ᆼᅳ ᅳ ᆯ ᄋᄇ ᅩ ᆻᄋ ᅧ ᄋ ᅳᄂ ᅡ, ᄀ ᅩᄑ ᆼᄉ ᅮ ᆨᄃ ᅩ ᅢᄅ ᅩᄀ ᆯᄉ ᅡ ᅮᄅ ᆨᄂ ᅩ ᆽᄋ ᅡ ᆫᄒ ᅳ ᆨᄉ ᅡ ᆸᄃ ᅳ ᅦᄋ ᅵᄐ ᅥᄋ ᅴᄇ ᅵᄋ ᆯᄅ ᅲ ᅩᄋ ᆫᄒ ᅵ ᅡᄋ ᅧᄌ ᆷᄎ ᅥ ᅡᄂ ᆽᄋ ᅡ ᆫᄉ ᅳ ᆼᄂ ᅥ ᆼᅳ ᅳ ᆯ ᄋᄇ ᅩᄋ ᆻᄃ ᅧ ᅡ. ᄀ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄇ ᅳ ᆯᄎ ᅧ ᆼ ᅩ RMSEᄂ ᆫ RF (1.66), SVM (1.66), DNN (1.70) ᄉ ᅳ ᆫᄋ ᅮ ᅳᄅ ᅩᄋ ᅮᄉ ᅮᄒ ᅡᄋ ᆻᄃ ᅧ ᅡ. RFᄋ ᅪ SVMᄋ ᆫ RMSEᄀ ᅳ ᅡᄇ ᅵᄉ ᆺᄒ ᅳ ᅡᄀ ᅦ ᅡᄐ ᄂ ᅡᅡ ᆻ ᄂᄋ ᅳᄂ ᅡ, ᄃ ᅡᄅ ᆫᄀ ᅳ ᆷᄌ ᅥ ᆼᄌ ᅳ ᅵᄉ ᅮᄋ ᆫ biasᄋ ᅵ ᅦᄉ ᅥᄂ ᆫ SVMᄇ ᅳ ᅩᄃ ᅡ RFᄋ ᅴᄉ ᆼᄂ ᅥ ᆼᄋ ᅳ ᅵᄃ ᅥᄂ ᇁᄀ ᅩ ᅩ, ᄉ ᅵᄀ ᆫᄌ ᅡ ᅡᄋ ᆫᄋ ᅯ ᅴᄉ ᅩᄆ ᅩᄅ ᆼᄋ ᅣ ᅵ RF ᅢᄇ ᄃ ᅵ SVMᄋ ᅵᄋ ᆨ 3ᄇ ᅣ ᅢᄋ ᆫᄌ ᅵ ᆷᄋ ᅥ ᆯᄃ ᅳ ᆯᄋ ᅳ ᅥᅬ ᄎᄌ ᆼᄌ ᅩ ᆨᄋ ᅥ ᅳᄅ ᅩᄂ ᆫᄀ ᅳ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᅮ ᅳ ᆼ ᄌ RF ᄆ ᅩᄃ ᆯᄋ ᅦ ᅵᄀ ᅡᄌ ᆼᄋ ᅡ ᅮᄉ ᅮᄒ ᆫᅥ ᅡ ᆼ ᄉᄂ ᆼᅳ ᅳ ᆯ ᄋᄇ ᅩᄋ ᆻᄃ ᅧ ᅡ. ᆫᄋ ᅩ ᄇ ᆫᄀ ᅧ ᅮᄂ ᆫᄀ ᅳ ᅵᄉ ᆼᄀ ᅡ ᆫᄎ ᅪ ᆨᄌ ᅳ ᅡᄅ ᅭᄅ ᆯᄀ ᅳ ᅵᄇ ᆫᄋ ᅡ ᅳᄅ ᅩᄀ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄆ ᅳ ᅩᄃ ᆯᄋ ᅦ ᅴᄐ ᆨᄉ ᅳ ᆼᄋ ᅥ ᆯᅮ ᅳ ᆫ ᄇᄉ ᆨᄒ ᅥ ᆫᄀ ᅡ ᅵᄎ ᅩᄋ ᆫᄀ ᅧ ᅮᄅ ᅩᄊ ᅥ, ᄎ ᅮᄒ ᅮᄀ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅴ ᆨᄉ ᅡ ᄒ ᆸᅡ ᅳ ᄌᄅ ᅭᄅ ᆯᄉ ᅳ ᅵ·ᄀ ᆼᄀ ᅩ ᆫᄌ ᅡ ᆨᄋ ᅥ ᅳᄅ ᅩᄀ ᅩᄅ ᅧᄒ ᅡᄋ ᅧᄀ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄆ ᅳ ᅩᄒ ᆼᄀ ᅧ ᅮᄎ ᆨᄆ ᅮ ᆾᄌ ᅵ ᆨᄋ ᅥ ᆼᄒ ᅭ ᆫᄃ ᅡ ᅡᄆ ᆫᄃ ᅧ ᅥᄋ ᆨᄋ ᅮ ᅮᄉ ᅮᄒ ᆫᄉ ᅡ ᆼᄂ ᅥ ᆼᄋ ᅳ ᅴᄃ ᆫᄀ ᅡ ᅵᄑ ᆼᄉ ᅮ ᆨ ᅩ ᅨᄎ ᄋ ᆨᅩ ᅳ ᄆᄒ ᆼᄋ ᅧ ᆯᄀ ᅳ ᅮᄎ ᆨᄒ ᅮ ᆯᄉ ᅡ ᅮᄋ ᆻᄋ ᅵ ᆯᄀ ᅳ ᆺᄋ ᅥ ᅳᄅ ᅩᄑ ᆫᅡ ᅡ ᆫ ᄃᄃ ᆫᄃ ᅬ ᅡ.. References Choo, S., Lee, Y., Ahn, K. and Chung, K. (2013). Development of wind forecast model over Korean peninsula using harmony search algorithm. Proceedings of KIIS conference, 23, 198-199. Diaz-Uriarte, R. and De Andres, S. A. (2006). Gene selection and classification of microarray data using random forest. BMC Bioinformatics, https://doi.org/10.1186/1471-2105-7-3 . Hui, L., Hong-Qi, T., Chao, C. and Yan-fi, L. (2009). A hybrid statistical method to predict wind speed and wind power. Renew. Energ, 35, 1851-1861. Jeong, J. H. and Chae, Y. T. (2018). Imporvement for forecasting of photovoltaic power output using real time weather data based on machine learning. J. Krean Soc. Living Envion. Sys, 25, 119-124. Kapoor, P. and Bedi, S. S. (2013). Weather forecasting using sliding window algorithm, Hindawi. Kim, D. Y., Jeong, H. S., Kim, Y. H. and Kim, B. J. (2018). Comparative assessment of wind resources between west offshore and onshore regions in Korea. Atmosphere Korean meteorological society, 28, 1-13. Kim, Y. E., Lee, K. E. and Kim, G. (2020). Forecast of drought index using decision tree based methods. Journal of the Korean Data & Information Science Society, 31, 273-288. Korea Meteorological Society (2009). Introduction to atmospheric science, Sigma Press. Lee, D. J., Lee, J. P., Lee, C. S., Lim, J. Y. and Ji, P. S. (2015). Development of PV power prediction algorithm using adaptive neuro-fuzzy model. The Transactions of The Korean Institute of Electrical Engineers, 64, 246-250. Lee, K. H. and Kim, W. J. (2016). Forecasting of 24 hours ahead photovoltaic power output using support vector regression. Journal of Korean Institute of Information, 14, 175-183. Lee, Y. S., Kim, J., Jang, M. S. and Kim, H. G. (2013). A study on comparing short-term wind power prediction models Gunsan farm. Journal of the Korean Data & Information Science Society, 24, 585-592. Liaw, A. and Wiener, M. (2002). Classification and regression by random forest. R News, 2, 18-22. Liaw, A. and Wiener, M. (2002). Breiman and cutler’s random forest for classificaion and regression, R package version 4.6-14, https://CRAN.R-project.org/web/packages/randomForest/randomForest. pdf Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch, F. Chang, C. C. and Lin, C. C. (2019). Misc functions of the department of statistics, probability theory group, R package version 1.7-3, https: //CRAN.R-project.org/web/packages/e1071/e1071.pdf Moon, S., Kim, B. and Park, T. (1998). Simulation and forecast of wind speed using time series model. Asis-Pacific Journal of Atmospheric Sciences, 34, 147-153. Muller, K. R., Mika, S., Ratsch, G., Tsuda, K. and Scholkopf, B. (2001). An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks, 12, 181-201. Palutikof, J. P., Holt, T. and Osborn, T. J. (2002). Seasonal forecasting of strong winds over Europe. Symposium on Global Change and Climate Variations, 13, 125-128. Park, H. W., Lee, S. H., Lee, E. J., Cho, Y. S., Park, Y. S., Lee, J. H., Yu, D. H. (2020). Short-term forecasting for sea surface temperature based on tidal observatory observations. Journal of the Korean Data & Information Science Society, 31, 255-271..
(14) 836. Hyeong-Se Jeong. Shin, D. H. and Kim, C. B. (2018). Short term forecast model for solar power generation using RNN-LSTM. J. Adv. Navig. Technol, 22, 233-239. Smola, A. J. and Scholkopf, B. (2004). A tutorial on support vection regression. Statistics and Computing, 14, 199-222. Sweeney, C. P., Lynch, P. and Nolan, P. (2013). Reducing errors of wind speed forecasts by an optimal combination of post processing methods. Meteorol. Appl., 20, 32-40. Tong, S. and Koller, D. (2011). Support vector machine active learning with applications to text classification. Journal of Machine Learning Research, 2, 45-66. Yona, A., Senjyu, T., Funabashi, T., Mandal, P. and Kim, C. H. (2013). Decision technique of solar radiation prediction applying recurrent neural network for short-term ahead power output of photovoltaic system. Smart Grid and Renewable Energy, 4, 32-38. Xiao, R. (2015). Deep learning toolkit in R, R package version 0.2, https://CRAN.R-project.org/web/ packages/deepnet/deepnet.pdf.
(15) Journal of the Korean Data & Information Science Society 2020, 31(5), 823–837. http://dx.doi.org/10.7465/jkdi.2020.31.5.823 ᆫᄀ ᅡ ᄒ ᆨᄃ ᅮ ᅦᄋ ᅵᄐ ᅥᄌ ᆼᄇ ᅥ ᅩᅪ ᄀᄒ ᆨᄒ ᅡ ᅬᄌ ᅵ. Short-term forecasting for Wind Speed based on †. machine learning using weather observation data Hyeong-Se Jeong1 1. Innovative Meteorological Research Department, National Institute of Meteorological Sciences Received 14 July 2020, revised 27 August 2020, accepted 1 September 2020. Abstract In this paper, we propose a method that utilizes machine learning (deep neural network, support vector machine, random forest) learned from weather observation data to increase the accuracy of short-term prediction of wind speed. The proposed method selects an optimal model after a sensitivity experiment, and then performs verification with the observed wind speed. The sensitivity experiment targets the data extracted by applying the sliding window method and the set factor values of machine learning. The elements used in the learning materials are time, spatial and meteorological factors (temperature, wind direction, humidity, precipitation). The meteorological data used was the value of ASOS 95 sites provided by the Korea Meteorological Administration from August 2017 to August 2018. As a result, the optimal machine learning method showed excellent predictive performance in the low wind speed section and the land terrain. In particular, it can be seen that the Random forest is the best in performance and time resources compared to Supporter vector machines and Deep neural networks. Keywords: Machine learning, meteorological observation data, sliding window method, wind speed prediction.. † 1. I would like to express my gratitude to Dr. YunAm Seo for his assistance in the study. Corresponding author: Researcher, Innovative Meteorological Research Department, National Institude of Meteorological Sciences, Seguipo 63568, Korea, E-mail: [email protected]..
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