Determination presence of people in accommodation using feature extraction and XGBoost method of energy data

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◆㧊䎆㦮 䔏㰫 㿪㿲 ⹥ XGBoost ⯒ 㧊㣿䞲

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*䞲ῃ㩚㧦䐋㔶㡆ῂ㤦

{kimed93, softgear, sson, oklee, bytelee}@etri.re.kr

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Determination presence of people in accommodation using

feature extraction and XGBoost method of energy data

Eden Kim*, Seok-Gap Ko*, Seung-Chul Son*, Hyung-Ok Lee*, Byung-Tak Lee* *Electronics and Tele-communications Research Institute (ETRI)

殚 殚 檃檃 㓺Ⱎ䔎⹎䎆㦮 ₆㑶 ⹲╂ὒ ⽊ 㦒⪲ 㧎䟊 㩚⩻◆㧊䎆㦮 㑮㰧㧊 ⽊┺ 㑮㤪 䟊㰦㠦 ➆⧒ ṗ 㔲 㓺䎲㠦 䣾㥾㩗㧎 ⰴ㿺 ㍲゚㓺 㩲Ὃ㦚 㥚䞲 㩚⩻ ◆㧊䎆 ⿚㍳ ₆㑶㠦 ὖ䞲 ┺㟧䞲 㡆ῂṖ 䢲⹲䞮 Ợ 㰚䟟♮ἶ 㧞┺. ὖ⩾䞮㡂 ⽎ ⏒ⶎ㠦㍲⓪ 㑯⹫㠛㏢㦮 ṗ ⹿Ⱎ┺ 㩚⩻㏢゚⨟㦚 䁷㩫 ⹥ 㑮㰧䞮 㡂 㩚⩻㏢゚䕾䎊㦚 ⿚㍳䞮ἶ 䔏㰫 㿪㿲 ⹥ XGBoost ⯒ 㧊㣿䞲 Ⲏ㔶⩂┳ ⿚㍳⹿⻫㦒⪲ ṗ ⹿㦮 ㌂ ⧢ 㨂㔺 㡂⿖⯒ 䕦⼚䞮⓪ ⹿⻫㦚 ㏢Ṳ䞲┺. 㧊㢖 ṯ㦖 㡆ῂ⯒ 䐋䟊 㿪䤚 㑯⹫㠛㏢ 䢏㦖 㑯⹫㠛㏢ ⯒ 㧊㣿䞮⓪ ㏢゚㧦✺㦮 ⰴ㿺 ㍲゚㓺 㩲Ὃ㠦 㦧㣿 ⹥ 㩗㣿 䞶 㑮 㧞┺. 1. 昢昢嵦 㾲⁒ 㠦⍞㰖 ⿚㟒㠦㍲ 㩚⩻㦮 䣾㥾㩗㧎 ㌂㣿ὒ ὖ Ⰲ⯒ 㥚䟊 㓺Ⱎ䔎 ⁎Ⰲ✲ ₆㑶㧊 䢲㎇䢪 ♮Ⳋ㍲ 㓺Ⱎ 䔎⹎䎆㦮 ⽊ 㧊 ⓮㠊⋮ἶ 㧞ἶ, 㧊㢖 䞾℮ 㰖⓻䡫 㔲㓺䎲㦚 㥚䞲 ₆㑶 Ṳ⹲㧊 ┺㟧䞮Ợ 㧊⬾㠊㰖ἶ 㧞 ┺ [1]. 䔏䧞, ⽊ ♲ 㓺Ⱎ䔎⹎䎆⯒ 㧊㣿䞮㡂 㠦⍞㰖 㩚⩻ ㌂㣿⨟㦚 㔺㔲Ṛ㦒⪲ Ἒ䁷䞮ἶ 㩫⽊⯒ 㭒ἶ⹱⓪ ₆㑶✺㧊 ⹲╂䞮Ⳋ㍲ 䞯ᾦ, 䣢㌂, ὖὋ㍲ ❇ ṗ ㌂㣿 㻮㠦 ⰴ㿺 ㍲゚㓺 㩲Ὃ㦚 㥚䟊 㩚⩻ ◆㧊䎆 ⿚㍳㦚 ゚⫅䞲 Ⱔ㦖 㡆ῂ✺㧊 㰚䟟♮ἶ 㧞┺ [2]. 㧊㠦 㧒䢮 㦒⪲, ⽎ ⏒ⶎ㠦㍲⓪ 㩚⩻ ◆㧊䎆⯒ 㧊㣿䞲 㑯⹫㠛㏢ 㠦㍲ ㌂⧢㦮 㨂㔺 㡂⿖ ⿚㍳ ₆㑶㦚 ㏢Ṳ䞲┺. ⽎ 㡆ῂ㠦㍲⓪ 㑯⹫㠛㏢㦮 ṗ ⹿㠦 䟊╏䞮⓪ 㩚⩻ ㏢゚ ◆㧊䎆㢖 䃊✲䋺 on/off 㩫⽊⯒ 㑮㰧䞲┺. 䃊✲ 䋺 㩫⽊⓪ ㌂⧢㦮 㨂㔺 㡂⿖⯒ 䕢㞛䞮⓪ 㩫⽊⪲ 䢲㣿 ♮Ⳇ ₆㫊 ὒỆ 㩚⩻ ◆㧊䎆㢖 䃊✲䋺 㩫⽊⯒ 䐋䞮㡂 Ⲏ㔶⩂┳ ⳾◎ 䞯㔋㦚 䞮ἶ, 㿪䤚 㩚⩻ ◆㧊䎆Ⱒ㦚 㧊㣿䞮㡂 㨂㔺 㥶ⶊ⯒ ⏨㦖 㩫䢫☚⪲ 䕦⼚ Ṗ⓻䞲㰖 㠦 ὖ䞲 㔺䠮㦚 㰚䟟䞮㡖┺. Ṗ㧻 Ṛ┾䞮Ợ⓪ 㩚⩻ 㔶䢎㢖 䃊✲䋺 㩫⽊⯒ ⰺ䃃䞮㡂 㧚Ἒ Ṩ ㍺㩫㦚 䐋䞲 䕦⼚ ⹿⻫ 䢏㦖 㤦㔲◆㧊䎆⯒ 㧊㣿䞲 Ⲏ㔶⩂┳ ⿚⮮ ⹿⻫ ❇㦚 ἶ㞞䟊 ⽒ 㑮 㧞┺. 䞮㰖Ⱒ ⽊┺ ⏨㦖 㩫 䢫☚⯒ ⽊㧊⓪ ⿚㍳ 㞢ἶⰂ㯮㦚 Ⱒ✺₆ 㥚䟊㍲ ⽎ 㡆 ῂ㠦㍲⓪, 㩚⩻ ◆㧊䎆⓪ 㔲Ἒ㡊 䔏㰫㦚 Ṗ㰖⓪ ộ㦚 ἶ⩺䞮㡂 㔲Ἒ㡊 ◆㧊䎆Ṗ Ṗ㰞 㑮 㧞⓪ 䔏㰫✺㦚 ㌳ ㎇ ⹥ 㿪㿲䞮㡖ἶ, 㤦㔲◆㧊䎆⯒ ⹪⪲ ㌂㣿䞮⓪ ộ㧊 㞚┢ ㌞⪲ 㿪㿲䞲 䔏㰫 ⻷䎆㢖 䃊✲䋺 㩫⽊㦮 ὖἚ ⰺ䃃㦚 䐋䟊 XGBoost ⹿⻫㦚 㩗㣿䞾㦒⪲㍲ ₆㫊 ┾㑲 䞲 䕦⼚ ₆㑶⽊┺ ▪ 㤆㑮䞲 ἆὒ⯒ ⋮䌖⌊⓪ ⿚㍳ 㞢ἶⰂ㯮㦚 Ṳ⹲ 䞮㡖┺. 2. 塶決瘶 朞滗 愕 洊熞庲 ⽎ 㡆ῂ⯒ 㰚䟟䞮₆ 㥚䟊 ㎒㍲⯒ 䐋䞮㡂 1 㽞┾㥚 㦮 㩚⩻ ◆㧊䎆㢖 䃊✲䋺 㩫⽊⯒ 㔺㔲Ṛ㦒⪲ 㑮㰧䞮 㡂 ◆㧊䎆⻶㧊㓺㠦 㩖㧻䞮㡖┺. 㑮㰧䞲 㩚⩻ ◆㧊䎆 㢖 䃊✲䋺 㩫⽊㦮 ὖἚ⓪ ┺㦢 ⁎Ⱂ 1 ὒ ṯ㧊 ㍲⪲ 㡆ὖ㎇㧊 㧞㦢㦚 ⽊㡂㭖┺. ڃ⁎Ⱂٻڌڄٻ㩚⩻◆㧊䎆㢖ٻ䃊✲䋺ٻ㩫⽊㦮ٻὖἚٻ

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ṗ ◆㧊䎆⓪ 㽞┾㥚⪲ 㑮㰧♮㠞㰖Ⱒ, 㨂㔺 㥶ⶊ⯒ 㽞 ┾㥚₢㰖 ἶ⩺䞮㡂 䕦⼚䞶 䞚㣪⓪ 㠜₆ ➢ⶎ㠦, ⳾✶ ◆㧊䎆⯒ 1 ⿚ ┾㥚⪲ 䘟‶㦚 䐋䞲 ┺㤊㌮䝢Ⱇ㦚 㰚䟟䞮㡖┺. 㩚⩻ ◆㧊䎆⓪ ⹿Ⱎ┺ ₆㩖 㩚⩻㧊 ┺⯊ ἶ 䞒䋂 㩚⩻⨟㧊 ┺⯊₆ ➢ⶎ㠦 min-max 㩫′䢪⯒ 䐋䞮㡂 ⳾✶ ⹿㦮 㩚⩻✺㦮 㓺䅖㧒㦚 㫆㩫䞮㡖┺. ⡦ 䞲, 䃊✲䋺 㩫⽊ 㭧㠦⓪ 1 ⿚ ┾㥚 ₆㭖㦒⪲ 30 ⿚ 㧊 ⌊ 㔲Ṛ ☯㞞 䃊✲䋺Ṗ on 㧊 ♮㠞┺ ┺㔲 off Ṗ ♮⓪ ἓ㤆Ṗ Ṛ䢏 㫊㨂䞮⓪◆, 㧊㦮 ἓ㤆⓪ 㔺㩲⪲ ㏦┮㧊 ╖㔺 䢏㦖 㑯⹫㦮 ⳿㩗㦒⪲ Ⲏⶊ⯊ἶ 㧞⓪ ㌗䢿㧊 㞚 ┞⧒ ⹿ 㼃㏢ 䢏㦖 㧶㔲 ⶒỊⰢ ⏪⚦ἶ ┺㔲 㣎㿲䞮 ⓪ ❇ 㑯⹫㠛㏢㠦 㨂㔺 䞮⓪ ἓ㤆㢖 ┺⯎ ㌗䢿㧒 䢫 ⮶㧊 ⏨㞚 30 ⿚ 㧊⌊ ₆㭖㦒⪲ on ♮㠊㧞⓪ Ṩ✺㦖 ⳾⚦ off ⪲ Ṛ㭒䞮☚⪳ 㩚㻮Ⰲ⯒ 㰚䟟䞮㡖┺. 3. 瞿瞿滛 犚犢 愕 XGBoost 庂 決殯穢 把昣 ⽎ 3 㧻㠦㍲⓪ 㩚㻮Ⰲ ♲ 㩚⩻ ◆㧊䎆㢖 䃊✲䋺 㩫 ⽊⯒ 䐋䞮㡂 㔲Ἒ㡊 䔏㎇㦚 ⹮㡗䞲 䔏㰫 ⻷䎆✺㦚 Ⲓ 㩖 㿪㿲䞲┺. 㔲Ἒ㡊 ◆㧊䎆㦮 䔏㰫 㿪㿲 ⹿⻫㠦 ὖ 䞮㡂 ┺㟧䞲 㡆ῂ✺㧊 㰚䟟 ♮㠞⓪◆, ⁎ 㭧㠦 䕢㧊 㗂 䕾䋺㰖㭧 䞮⋮㧎 tsfresh ⯒ 䐋䞲 䔏㰫 㿪㿲㦮 ⹿⻫ ✺㠦 ╖䞮㡂 㺎ἶ䞮㡖┺ [3]. 2 㧻㠦㍲ 30 ⿚㦚 ₆㭖㦒 ⪲ 㩚㻮Ⰲ⯒ 㰚䟟䞮㡖▮ ⹪㢖 ṯ㧊 ⽎ 㡆ῂ㠦㍲ ㌂㣿 䞮⓪ ◆㧊䎆⓪ 30 ⿚㦚 ₆㭖㦒⪲ ◆㧊䎆㦮 㦮⹎⯒ ⿖ 㡂䞮㡂 30 ⿚☯㞞㦮 㔲Ἒ㡊 㩚⩻ ◆㧊䎆 ㎎䔎⯒ 䐋䞮 㡂 䘟‶, 㾲╩Ṩ, 㾲㏵Ṩ ❇㦚 䙂䞾䞲 58 Ṗ㰖㦮 㔲Ἒ 㡊 䔏㰫 ⻷䎆✺㦚 㿪㿲䞮㡖┺. 䃊✲䋺 㩫⽊㦮 ἓ㤆⓪ 30 ⿚㦚 ₆㭖㦒⪲ 䞚䎆Ⱇ 䞮㡂 30 ⿚☯㞞 on 㧎 ἓ㤆Ṗ off 㧎 ἓ㤆⽊┺ Ⱔ㦒Ⳋ on, off 㧎 ἓ㤆Ṗ on 㧎 ἓ㤆⽊ ┺ Ⱔ㦒Ⳋ off ⪲ 䃊✲䋺 㩫⽊⯒ 㨂ῂ㎇ 䞮㡖┺. ⁎Ⱂ 2 ⓪ 㩚⩻◆㧊䎆㦮 䔏㰫 㿪㿲䞮⓪ ὒ㩫ὒ 䃊✲䋺 㩫⽊ ⯒ 䞚䎆Ⱇ 䞮⓪ ὒ㩫㦚 ⽊㡂㭖┺. ڃ⁎Ⱂٻڍڄٻ◆㧊䎆ٻ㎡㦮ٻ䔏㰫ٻ㿪㿲ٻ⹥ٻ䞚䎆Ⱇٻὒ㩫ٻ ┺㦢 㰚䟟㦒⪲⓪ 䔏㰫 㿪㿲♲ X’㢖 䞚䎆Ⱇ ♲ Y’ ◆㧊䎆 ㎡㦚 Ṗ㰖ἶ XGBoost ⳾◎㦚 ㌂㣿䞮㡂 䞯㔋 䞮㡖┺. XGBoost ⓪ 䃦⁖ 䄺ⷺ┞䕆㠦㍲ ṗὧ ⹱㞮▮ 䔎Ⰲ ₆⹮ ⿖㓺䕛 ⹿⻫㦒⪲ ザ⯊ἶ 㥶㡆䞮Ợ ㌂㣿 Ṗ ⓻䞮ἶ ⽊┺ 㰗ὖ㩗㧊┺ [4]. ڃ⁎Ⱂٻڎڄٻ⳾◎㦮ٻ㌗㥚ٻڌڋ Ṗ㰖ٻڡۀڼۏېۍۀٻڤۈۋۊۍۏڼۉھۀٻ 㥚 ⁎Ⱂ 3 㦖 X’㢖 Y’⯒ XGBoost ⪲ 䞯㔋䞲 ⳾◎㦮 䔏㰫 㭧㣪☚⯒ ⋮䌖⌎┺. ㍶䌳䞲 58 Ṗ㰖 㔲Ἒ㡊 䔏㰫 ✺ 㭧 Ṗ㧻 㭧㣪䞲 ㌗㥚 10 Ṗ㰖 䔏㰫✺㦚 ㌊䘊⽊Ⳋ ╖⿖⿚ Percentile 䔏㰫✺㧊 㨂㔺 㡂⿖⯒ 䕦⼚䞮⓪◆ 㭧㣪䞲 㩫⽊⯒ Ṗ㰖ἶ 㧞㦢㦚 㞢 㑮 㧞┺. 4. 柪竞 冶刂 愕 痆埿昷 円溣 3 㧻㠦 ₆㑶䞲 䔏㰫 㿪㿲 ⹥ XGBoost ⯒ 䐋䞲 ⿚㍳ ἆὒ Ỗ㯳㦚 㥚䟊 㞚⧮㦮 䚲 1 ὒ ṯ㧊 㔺䠮 ◆㧊䎆⯒ ῂ㎇䞮㡖┺. ڗ䚲ٻڌڙٻ㔺䠮ٻ◆㧊䎆ٻ㎡ٻῂ㎇ٻ 䢎 ⋶㰲 2019.10.23 ~2019.11.15 ~2020.02.12 2020.01.23 101 A1 A2 102 B1 B2 103 C1 C2 㔺䠮㠦 ㌂㣿♲ ◆㧊䎆⓪ 䞲 㑯⹫㠛㏢㦮 101 䢎, 102 䢎, 103 䢎 ⹿ ㎎ Ὁ㦮 㩚⩻ ⹥ 䃊✲䋺 㩫⽊⯒ 㑮㰧䞲 ộ㧊ἶ, ṗṗ㦖 19 ⎚ 10 㤪 23 㧒⿖䎆 11 㤪 15 㧒₢㰖 㦮 ◆㧊䎆㢖 20 ⎚ 1 㤪 23 㧒⿖䎆 20 ⎚ 2 㤪 12 㧒₢㰖 㦮 ◆㧊䎆⪲ ⋮⑚㠊 ṗṗ A1, A2, B1, B2, C1, C2 ⪲ 䚲 ₆䞮㡖┺. ┺㦢ὒ ṯ㦖 ◆㧊䎆 ㎡㦚 㧊㣿䞮㡂, 㼁⻞㱎⪲⓪ ṯ

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㦖 ⹿㦮 ◆㧊䎆⯒ ㌂㣿䞲 䔎⩞㧊┳ ⹥ 䎢㓺䔎⯒ 䞮⓪ ⹿㔳, ⚦⻞㱎⪲⓪ ⚦ ⹿㦮 ◆㧊䎆⯒ 䔎⩞㧊┳, ⚦ ⹿ 㧊㣎㦮 ⋮Ⲏ㰖 ┺⯎ ⹿㦚 䎢㓺䔎 䞮⓪ ⹿㔳㦒⪲ 㰚䟟 䞮㡂 ₆㫊 㩚⩻ 㤦㔲◆㧊䎆⯒ 㧊㣿䞲 XGBoost ⿚㍳ ὒ 㩲㞞♲ 䔏㰫 㿪㿲㦚 䐋䞲 XGBoost ⿚㍳㦚 ゚ᾦ 㔺䠮䞮㡖┺. 㞚⧮㦮 䚲 2 ⓪ 㔺䠮 ◆㧊䎆 ㎡㠦 ➆⯎ ⿚㍳ ἆὒ ゚ᾦ⯒ ⋮䌖⌎┺. ڗ䚲ٻڍڙٻ㔺䠮ٻ◆㧊䎆ٻ㎡㠦ٻ➆⯎ٻ䕦⼚ٻἆὒٻ゚ᾦٻ 㔺䠮 ◆㧊䎆 ㎡ 䕦⼚ 㩫䢫☚ ₆⽎ ⹿⻫ 㩲㞞 ⹿⻫ 1 A1 䔎⩞㧊┳ A2 䎢㓺䔎 97.28% 99.78% B1 䔎⩞㧊┳ B2 䎢㓺䔎 94.04% 98.22% C1 䔎⩞㧊┳ C2 䎢㓺䔎 81.45% 98.21% 2 A1, B1 䔎⩞㧊┳ C2 䎢㓺䔎 82.27% 98.22% B1, C1 䔎⩞㧊┳ A2 䎢㓺䔎 83.31% 99.88% C1, A1 䔎⩞㧊┳ B2 䎢㓺䔎 73.62% 93.86% 㩚⹮㩗㦒⪲ ṯ㦖 ⹿㦮 ◆㧊䎆⯒ 䔎⩞㧊┳ 䞮ἶ 䎢 㓺䔎 䞮⓪ ἓ㤆⓪ 㩫䢫☚Ṗ ╖⿖⿚ 90% 㧊㌗㦒⪲ ⏨ Ợ ⋮㢪㦒⋮, 䔎⩞㧊┳ὒ 䎢㓺䔎 䞮⓪ ⹿㦮 ◆㧊䎆Ṗ ┺⯎ ἓ㤆⓪ ゚ᾦ㩗 ⌄㦖 㩫䢫☚⯒ ⽊㡖┺. ⁎ 㭧㠦 ㍲☚ 㤦㔲◆㧊䎆⯒ 㧊㣿䞲 ₆⽎ ⿚㍳ ⹿⻫㦚 䀾䟞㦚 ➢⓪ 70~80%㦮 ⌄㦖 㩫䢫☚⯒ ⽊㡖㦒⋮, 㩲㞞♲ ⹿⻫ 㦖 ▪ ⏨㦖 㩫䢫☚⯒ ⽊㡂 㧊 ⏒ⶎ㠦㍲ 㩲㞞䞲 ⹿⻫ 㧊 ▪ 㤆㑮䞲 ㎇⓻㦚 ⽊㧊⓪ ộ㦚 䢫㧎䞮㡖ἶ ⚦⻞㱎 㢖 ṯ㦖 㔺䠮㦚 䐋䞮㡂 䔎⩞㧊┳ ㎡㠦 䙂䞾♮㰖 㞠㦖 ┺⯎ ⹿㦚 䎢㓺䔎 䞮⓪ ἓ㤆㠦☚ 㡂㩚䧞 90% 㧊㌗㦮 ⏨㦖 㩫䢫☚⯒ ⽊㡂 㔺㩲 䞯㔋㠦 ㌂㣿♮㰖 㞠㦖 ⹿㦮 ἓ㤆☚ 㩚⩻◆㧊䎆Ⱒ ⽊㥶䞮ἶ 㧞㦒Ⳋ ↺ ⏨㦖 㩫䢫 ☚⪲ ㌂⧢㦮 㨂㔺 㡂⿖⯒ 䕦⼚ 䞶 㑮 㧞┺. 5. 冶冶嵦 㓺Ⱎ䔎⹎䎆⪲⿖䎆 䁷㩫♮⓪ 㩚⩻◆㧊䎆⯒ 䐋䞮㡂 ⿚ ㍳䞾㦒⪲㖾 㡂⩂ ㍲゚㓺✺㦚 㥚䞲 㩫⽊⯒ 㩲Ὃ 䞶 㑮 㧞┺. ⽎ⶎ㠦㍲⓪, 䔏䧞 㑯⹫ 㠛㏢㠦 䁷㩫♮⓪ 㠦⍞㰖 㩚⩻ ◆㧊䎆⯒ 䐋䞮㡂 ㌂⧢ 㨂㔺 㡂⿖⯒ 䕦⼚䞮⓪ ⿚ ㍳ ⹿⻫㠦 ╖䞮㡂 ㏢Ṳ䞮㡖┺. ₆⽎㩗㧎 ⹿⻫㦒⪲ 㤦 㔲◆㧊䎆⯒ Ⲏ㔶⩂┳ 㩗㣿䞮㡂 䕦⼚䞮⓪ ộ⽊┺ ⽎ ⏒ⶎ㠦㍲ 㩲㔲♲ 㔲Ἒ㡊 䔏㰫㦚 㰖┢ 㩚⩻◆㧊䎆㦮 䔏㰫 ⻷䎆⯒ ㆧ㞚 䕦⼚ ⿚㍳䞮㡖㦚 ➢ ▪ ⥆㠊⋲ 㩫 䢫☚⯒ ⽊㧚㦚 㔺䠮㦚 䐋䞮㡂 Ỗ㯳䞮㡖┺. ⽎ 㡆ῂ㠦 ⋮㞚Ṗ㍲⓪ ⽎ ⏒ⶎ㠦㍲ ㍶䌳䟞▮ 58 Ṗ㰖㦮 䔏㰫⻷䎆 ✺㦚 㾲㩗䢪 䞮⓪ ⹿⻫, ἆὒ㦮 䤚㻮Ⰲ ❇㦚 䐋䞮㡂 㩫䢫㎇㦚 ▪ ⏨㧒 㑮 㧞㦚 ộ㦒⪲ ⽊㧊Ⳇ, 㧊㢖 ṯ㦖 㨂㔺 䕦⼚㦮 㩫䢫☚Ṗ ⏨㞚㰖Ⳋ 㑯⹫㠛㏢㢖 㑯⹫㠛㏢ ⯒ 㧊㣿䞮⓪ ㌂⧢✺㦚 㥚䞲 ㍲゚㓺 㦧㣿㠦 㩗㣿 Ṗ⓻ 䞶 ộ㦒⪲ ⽊㧎┺. 儖斲汞 匆 ⽎ 㡆ῂ⓪ ㌆㠛䐋㌗㧦㤦⿖(MOTIE)㢖 䞲ῃ㠦⍞㰖₆㑶 䘟Ṗ㤦(KETEP)㦮 㰖㤦㦚 ⹱㞚 㑮䟟䞲 㡆ῂ ὒ㩲㧛┞ ┺. (No. 20181210301570) 焾処怾竒 [1] 㧊㧒㤆, et al. “㓺Ⱎ䔎 ⁎Ⰲ✲ ₆㑶 ☯䟻.” 䞲ῃ䐋㔶 䞯䣢㰖 (㩫⽊㢖䐋㔶), vol. 26, no.9, pp.24-33, 2009. [2] Alahakoon, Damminda, and Xinghuo Yu. “Smart

electricity meter data intelligence for future energy systems: A survey.” IEEE Transactions on Industrial Informatics, vol.12, no.1, pp.425-436, 2015

[3] Christ, Maximilian, et al. “Time series feature extraction on basis of scalable hypothesis tests (tsfresh-a python package).” Neurocomputing, vol.307, pp.72-77, 2018. [4] Chen, Tianqi, and Carlos Guestrin. “Xgboost: A scalable

tree boosting system.” Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, San Francisco, USA, 2016, pp.785-794.

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