2020-2-capstone-design1 / Synergy_project

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1 +{
2 + "nbformat": 4,
3 + "nbformat_minor": 0,
4 + "metadata": {
5 + "colab": {
6 + "name": "generate_prediction.ipynb",
7 + "provenance": []
8 + },
9 + "kernelspec": {
10 + "name": "python3",
11 + "display_name": "Python 3"
12 + }
13 + },
14 + "cells": [
15 + {
16 + "cell_type": "code",
17 + "metadata": {
18 + "id": "TB2mZGOKD5lp",
19 + "outputId": "71c0f9b6-c119-41bb-d792-9f75c925e787",
20 + "colab": {
21 + "base_uri": "https://localhost:8080/"
22 + }
23 + },
24 + "source": [
25 + "from google.colab import drive\n",
26 + "drive.mount('/content/gdrive/')"
27 + ],
28 + "execution_count": null,
29 + "outputs": [
30 + {
31 + "output_type": "stream",
32 + "text": [
33 + "Mounted at /content/gdrive/\n"
34 + ],
35 + "name": "stdout"
36 + }
37 + ]
38 + },
39 + {
40 + "cell_type": "code",
41 + "metadata": {
42 + "id": "6toBcxODD60f"
43 + },
44 + "source": [
45 + "folder = '/content/gdrive/My Drive/colab_data/cap_di/'\n",
46 + "weather = folder+'세종시 날씨.csv'\n",
47 + "generate = folder+'세종시.csv'"
48 + ],
49 + "execution_count": null,
50 + "outputs": []
51 + },
52 + {
53 + "cell_type": "code",
54 + "metadata": {
55 + "id": "kkN1rP34E7WC"
56 + },
57 + "source": [
58 + "import pandas as pd"
59 + ],
60 + "execution_count": null,
61 + "outputs": []
62 + },
63 + {
64 + "cell_type": "code",
65 + "metadata": {
66 + "id": "8NENGSIfE9rH",
67 + "outputId": "83e74f5a-52f7-41bb-eead-0b56d0f5cab0",
68 + "colab": {
69 + "base_uri": "https://localhost:8080/",
70 + "height": 204
71 + }
72 + },
73 + "source": [
74 + "wt = pd.read_csv(weather,encoding='CP949')\n",
75 + "wt.head()"
76 + ],
77 + "execution_count": null,
78 + "outputs": [
79 + {
80 + "output_type": "execute_result",
81 + "data": {
82 + "text/html": [
83 + "<div>\n",
84 + "<style scoped>\n",
85 + " .dataframe tbody tr th:only-of-type {\n",
86 + " vertical-align: middle;\n",
87 + " }\n",
88 + "\n",
89 + " .dataframe tbody tr th {\n",
90 + " vertical-align: top;\n",
91 + " }\n",
92 + "\n",
93 + " .dataframe thead th {\n",
94 + " text-align: right;\n",
95 + " }\n",
96 + "</style>\n",
97 + "<table border=\"1\" class=\"dataframe\">\n",
98 + " <thead>\n",
99 + " <tr style=\"text-align: right;\">\n",
100 + " <th></th>\n",
101 + " <th>일시</th>\n",
102 + " <th>기온(°C)</th>\n",
103 + " <th>강수량(mm)</th>\n",
104 + " <th>습도(%)</th>\n",
105 + " <th>현지기압(hPa)</th>\n",
106 + " <th>일조(hr)</th>\n",
107 + " <th>지면온도(°C)</th>\n",
108 + " </tr>\n",
109 + " </thead>\n",
110 + " <tbody>\n",
111 + " <tr>\n",
112 + " <th>0</th>\n",
113 + " <td>2019-07-01 1:00</td>\n",
114 + " <td>22.2</td>\n",
115 + " <td>NaN</td>\n",
116 + " <td>90</td>\n",
117 + " <td>993.5</td>\n",
118 + " <td>NaN</td>\n",
119 + " <td>22.0</td>\n",
120 + " </tr>\n",
121 + " <tr>\n",
122 + " <th>1</th>\n",
123 + " <td>2019-07-01 2:00</td>\n",
124 + " <td>22.4</td>\n",
125 + " <td>NaN</td>\n",
126 + " <td>89</td>\n",
127 + " <td>993.4</td>\n",
128 + " <td>NaN</td>\n",
129 + " <td>22.2</td>\n",
130 + " </tr>\n",
131 + " <tr>\n",
132 + " <th>2</th>\n",
133 + " <td>2019-07-01 3:00</td>\n",
134 + " <td>22.3</td>\n",
135 + " <td>NaN</td>\n",
136 + " <td>90</td>\n",
137 + " <td>993.4</td>\n",
138 + " <td>NaN</td>\n",
139 + " <td>22.1</td>\n",
140 + " </tr>\n",
141 + " <tr>\n",
142 + " <th>3</th>\n",
143 + " <td>2019-07-01 4:00</td>\n",
144 + " <td>21.9</td>\n",
145 + " <td>NaN</td>\n",
146 + " <td>91</td>\n",
147 + " <td>993.4</td>\n",
148 + " <td>NaN</td>\n",
149 + " <td>21.4</td>\n",
150 + " </tr>\n",
151 + " <tr>\n",
152 + " <th>4</th>\n",
153 + " <td>2019-07-01 5:00</td>\n",
154 + " <td>21.8</td>\n",
155 + " <td>NaN</td>\n",
156 + " <td>91</td>\n",
157 + " <td>993.9</td>\n",
158 + " <td>NaN</td>\n",
159 + " <td>21.2</td>\n",
160 + " </tr>\n",
161 + " </tbody>\n",
162 + "</table>\n",
163 + "</div>"
164 + ],
165 + "text/plain": [
166 + " 일시 기온(°C) 강수량(mm) 습도(%) 현지기압(hPa) 일조(hr) 지면온도(°C)\n",
167 + "0 2019-07-01 1:00 22.2 NaN 90 993.5 NaN 22.0\n",
168 + "1 2019-07-01 2:00 22.4 NaN 89 993.4 NaN 22.2\n",
169 + "2 2019-07-01 3:00 22.3 NaN 90 993.4 NaN 22.1\n",
170 + "3 2019-07-01 4:00 21.9 NaN 91 993.4 NaN 21.4\n",
171 + "4 2019-07-01 5:00 21.8 NaN 91 993.9 NaN 21.2"
172 + ]
173 + },
174 + "metadata": {
175 + "tags": []
176 + },
177 + "execution_count": 4
178 + }
179 + ]
180 + },
181 + {
182 + "cell_type": "code",
183 + "metadata": {
184 + "id": "YRZ9hz42FdDE",
185 + "outputId": "c205e81a-87f0-49b8-8602-1a54d5b88b26",
186 + "colab": {
187 + "base_uri": "https://localhost:8080/",
188 + "height": 235
189 + }
190 + },
191 + "source": [
192 + "variables=[\"기온(°C)\",\"강수량(mm)\",\"습도(%)\",\"현지기압(hPa)\",\"지면온도(°C)\"]\n",
193 + "wtData=wt[variables]\n",
194 + "wtData.index=wt['일시']\n",
195 + "wtData.head()"
196 + ],
197 + "execution_count": null,
198 + "outputs": [
199 + {
200 + "output_type": "execute_result",
201 + "data": {
202 + "text/html": [
203 + "<div>\n",
204 + "<style scoped>\n",
205 + " .dataframe tbody tr th:only-of-type {\n",
206 + " vertical-align: middle;\n",
207 + " }\n",
208 + "\n",
209 + " .dataframe tbody tr th {\n",
210 + " vertical-align: top;\n",
211 + " }\n",
212 + "\n",
213 + " .dataframe thead th {\n",
214 + " text-align: right;\n",
215 + " }\n",
216 + "</style>\n",
217 + "<table border=\"1\" class=\"dataframe\">\n",
218 + " <thead>\n",
219 + " <tr style=\"text-align: right;\">\n",
220 + " <th></th>\n",
221 + " <th>기온(°C)</th>\n",
222 + " <th>강수량(mm)</th>\n",
223 + " <th>습도(%)</th>\n",
224 + " <th>현지기압(hPa)</th>\n",
225 + " <th>지면온도(°C)</th>\n",
226 + " </tr>\n",
227 + " <tr>\n",
228 + " <th>일시</th>\n",
229 + " <th></th>\n",
230 + " <th></th>\n",
231 + " <th></th>\n",
232 + " <th></th>\n",
233 + " <th></th>\n",
234 + " </tr>\n",
235 + " </thead>\n",
236 + " <tbody>\n",
237 + " <tr>\n",
238 + " <th>2019-07-01 1:00</th>\n",
239 + " <td>22.2</td>\n",
240 + " <td>NaN</td>\n",
241 + " <td>90</td>\n",
242 + " <td>993.5</td>\n",
243 + " <td>22.0</td>\n",
244 + " </tr>\n",
245 + " <tr>\n",
246 + " <th>2019-07-01 2:00</th>\n",
247 + " <td>22.4</td>\n",
248 + " <td>NaN</td>\n",
249 + " <td>89</td>\n",
250 + " <td>993.4</td>\n",
251 + " <td>22.2</td>\n",
252 + " </tr>\n",
253 + " <tr>\n",
254 + " <th>2019-07-01 3:00</th>\n",
255 + " <td>22.3</td>\n",
256 + " <td>NaN</td>\n",
257 + " <td>90</td>\n",
258 + " <td>993.4</td>\n",
259 + " <td>22.1</td>\n",
260 + " </tr>\n",
261 + " <tr>\n",
262 + " <th>2019-07-01 4:00</th>\n",
263 + " <td>21.9</td>\n",
264 + " <td>NaN</td>\n",
265 + " <td>91</td>\n",
266 + " <td>993.4</td>\n",
267 + " <td>21.4</td>\n",
268 + " </tr>\n",
269 + " <tr>\n",
270 + " <th>2019-07-01 5:00</th>\n",
271 + " <td>21.8</td>\n",
272 + " <td>NaN</td>\n",
273 + " <td>91</td>\n",
274 + " <td>993.9</td>\n",
275 + " <td>21.2</td>\n",
276 + " </tr>\n",
277 + " </tbody>\n",
278 + "</table>\n",
279 + "</div>"
280 + ],
281 + "text/plain": [
282 + " 기온(°C) 강수량(mm) 습도(%) 현지기압(hPa) 지면온도(°C)\n",
283 + "일시 \n",
284 + "2019-07-01 1:00 22.2 NaN 90 993.5 22.0\n",
285 + "2019-07-01 2:00 22.4 NaN 89 993.4 22.2\n",
286 + "2019-07-01 3:00 22.3 NaN 90 993.4 22.1\n",
287 + "2019-07-01 4:00 21.9 NaN 91 993.4 21.4\n",
288 + "2019-07-01 5:00 21.8 NaN 91 993.9 21.2"
289 + ]
290 + },
291 + "metadata": {
292 + "tags": []
293 + },
294 + "execution_count": 5
295 + }
296 + ]
297 + },
298 + {
299 + "cell_type": "code",
300 + "metadata": {
301 + "id": "4DGaculbN3PN",
302 + "outputId": "c5a1efb0-c363-4bff-b42d-7b6b2e23901c",
303 + "colab": {
304 + "base_uri": "https://localhost:8080/"
305 + }
306 + },
307 + "source": [
308 + "print(wtData)"
309 + ],
310 + "execution_count": 60,
311 + "outputs": [
312 + {
313 + "output_type": "stream",
314 + "text": [
315 + " 기온(°C) 강수량(mm) 습도(%) 현지기압(hPa) 지면온도(°C)\n",
316 + "일시 \n",
317 + "2019-07-01 1:00 22.2 NaN 90 993.5 22.0\n",
318 + "2019-07-01 2:00 22.4 NaN 89 993.4 22.2\n",
319 + "2019-07-01 3:00 22.3 NaN 90 993.4 22.1\n",
320 + "2019-07-01 4:00 21.9 NaN 91 993.4 21.4\n",
321 + "2019-07-01 5:00 21.8 NaN 91 993.9 21.2\n",
322 + "... ... ... ... ... ...\n",
323 + "2020-06-29 20:00 20.0 6.1 93 986.6 21.2\n",
324 + "2020-06-29 21:00 19.9 11.1 94 984.7 20.9\n",
325 + "2020-06-29 22:00 20.2 6.0 94 983.4 20.9\n",
326 + "2020-06-29 23:00 20.2 5.1 94 981.2 20.7\n",
327 + "2020-06-30 0:00 20.3 2.0 91 979.8 20.6\n",
328 + "\n",
329 + "[8760 rows x 5 columns]\n"
330 + ],
331 + "name": "stdout"
332 + }
333 + ]
334 + },
335 + {
336 + "cell_type": "code",
337 + "metadata": {
338 + "id": "6m7FW6rLFXBP",
339 + "outputId": "f95c389e-1f64-43df-9d51-5d5b81970f06",
340 + "colab": {
341 + "base_uri": "https://localhost:8080/",
342 + "height": 204
343 + }
344 + },
345 + "source": [
346 + "gnt=pd.read_csv(generate,encoding='CP949')\n",
347 + "gnt.head()"
348 + ],
349 + "execution_count": null,
350 + "outputs": [
351 + {
352 + "output_type": "execute_result",
353 + "data": {
354 + "text/html": [
355 + "<div>\n",
356 + "<style scoped>\n",
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366 + " text-align: right;\n",
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368 + "</style>\n",
369 + "<table border=\"1\" class=\"dataframe\">\n",
370 + " <thead>\n",
371 + " <tr style=\"text-align: right;\">\n",
372 + " <th></th>\n",
373 + " <th>일자</th>\n",
374 + " <th>1:00</th>\n",
375 + " <th>2:00</th>\n",
376 + " <th>3:00</th>\n",
377 + " <th>4:00</th>\n",
378 + " <th>5:00</th>\n",
379 + " <th>6:00</th>\n",
380 + " <th>7:00</th>\n",
381 + " <th>8:00</th>\n",
382 + " <th>9:00</th>\n",
383 + " <th>10:00</th>\n",
384 + " <th>11:00</th>\n",
385 + " <th>12:00</th>\n",
386 + " <th>13:00</th>\n",
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388 + " <th>15:00</th>\n",
389 + " <th>16:00</th>\n",
390 + " <th>17:00</th>\n",
391 + " <th>18:00</th>\n",
392 + " <th>19:00</th>\n",
393 + " <th>20:00</th>\n",
394 + " <th>21:00</th>\n",
395 + " <th>22:00</th>\n",
396 + " <th>23:00</th>\n",
397 + " <th>0:00</th>\n",
398 + " </tr>\n",
399 + " </thead>\n",
400 + " <tbody>\n",
401 + " <tr>\n",
402 + " <th>0</th>\n",
403 + " <td>2019-07-01</td>\n",
404 + " <td>0</td>\n",
405 + " <td>0</td>\n",
406 + " <td>0</td>\n",
407 + " <td>0</td>\n",
408 + " <td>0</td>\n",
409 + " <td>360</td>\n",
410 + " <td>6000</td>\n",
411 + " <td>92880</td>\n",
412 + " <td>244560</td>\n",
413 + " <td>357480</td>\n",
414 + " <td>464880</td>\n",
415 + " <td>720840</td>\n",
416 + " <td>744000</td>\n",
417 + " <td>532200</td>\n",
418 + " <td>462240</td>\n",
419 + " <td>433200</td>\n",
420 + " <td>348120</td>\n",
421 + " <td>336720</td>\n",
422 + " <td>109920</td>\n",
423 + " <td>0</td>\n",
424 + " <td>0</td>\n",
425 + " <td>0</td>\n",
426 + " <td>0</td>\n",
427 + " <td>0</td>\n",
428 + " </tr>\n",
429 + " <tr>\n",
430 + " <th>1</th>\n",
431 + " <td>2019-07-02</td>\n",
432 + " <td>0</td>\n",
433 + " <td>0</td>\n",
434 + " <td>0</td>\n",
435 + " <td>0</td>\n",
436 + " <td>0</td>\n",
437 + " <td>120</td>\n",
438 + " <td>81720</td>\n",
439 + " <td>313800</td>\n",
440 + " <td>625680</td>\n",
441 + " <td>831360</td>\n",
442 + " <td>1062240</td>\n",
443 + " <td>861240</td>\n",
444 + " <td>849600</td>\n",
445 + " <td>785280</td>\n",
446 + " <td>915960</td>\n",
447 + " <td>843240</td>\n",
448 + " <td>565320</td>\n",
449 + " <td>178080</td>\n",
450 + " <td>115800</td>\n",
451 + " <td>6720</td>\n",
452 + " <td>0</td>\n",
453 + " <td>0</td>\n",
454 + " <td>0</td>\n",
455 + " <td>0</td>\n",
456 + " </tr>\n",
457 + " <tr>\n",
458 + " <th>2</th>\n",
459 + " <td>2019-07-03</td>\n",
460 + " <td>0</td>\n",
461 + " <td>0</td>\n",
462 + " <td>0</td>\n",
463 + " <td>0</td>\n",
464 + " <td>0</td>\n",
465 + " <td>0</td>\n",
466 + " <td>54480</td>\n",
467 + " <td>219720</td>\n",
468 + " <td>320400</td>\n",
469 + " <td>602400</td>\n",
470 + " <td>805440</td>\n",
471 + " <td>917280</td>\n",
472 + " <td>815760</td>\n",
473 + " <td>569520</td>\n",
474 + " <td>613080</td>\n",
475 + " <td>726480</td>\n",
476 + " <td>510120</td>\n",
477 + " <td>313440</td>\n",
478 + " <td>68400</td>\n",
479 + " <td>0</td>\n",
480 + " <td>0</td>\n",
481 + " <td>0</td>\n",
482 + " <td>0</td>\n",
483 + " <td>0</td>\n",
484 + " </tr>\n",
485 + " <tr>\n",
486 + " <th>3</th>\n",
487 + " <td>2019-07-04</td>\n",
488 + " <td>0</td>\n",
489 + " <td>0</td>\n",
490 + " <td>0</td>\n",
491 + " <td>0</td>\n",
492 + " <td>0</td>\n",
493 + " <td>120</td>\n",
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497 + " <td>877440</td>\n",
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508 + " <td>0</td>\n",
509 + " <td>0</td>\n",
510 + " <td>0</td>\n",
511 + " <td>0</td>\n",
512 + " </tr>\n",
513 + " <tr>\n",
514 + " <th>4</th>\n",
515 + " <td>2019-07-05</td>\n",
516 + " <td>0</td>\n",
517 + " <td>0</td>\n",
518 + " <td>0</td>\n",
519 + " <td>0</td>\n",
520 + " <td>0</td>\n",
521 + " <td>0</td>\n",
522 + " <td>57120</td>\n",
523 + " <td>310320</td>\n",
524 + " <td>520320</td>\n",
525 + " <td>847800</td>\n",
526 + " <td>1031760</td>\n",
527 + " <td>1145280</td>\n",
528 + " <td>1186680</td>\n",
529 + " <td>1146360</td>\n",
530 + " <td>970200</td>\n",
531 + " <td>664920</td>\n",
532 + " <td>506520</td>\n",
533 + " <td>304200</td>\n",
534 + " <td>64560</td>\n",
535 + " <td>0</td>\n",
536 + " <td>0</td>\n",
537 + " <td>0</td>\n",
538 + " <td>0</td>\n",
539 + " <td>0</td>\n",
540 + " </tr>\n",
541 + " </tbody>\n",
542 + "</table>\n",
543 + "</div>"
544 + ],
545 + "text/plain": [
546 + " 일자 1:00 2:00 3:00 4:00 ... 20:00 21:00 22:00 23:00 0:00\n",
547 + "0 2019-07-01 0 0 0 0 ... 0 0 0 0 0\n",
548 + "1 2019-07-02 0 0 0 0 ... 6720 0 0 0 0\n",
549 + "2 2019-07-03 0 0 0 0 ... 0 0 0 0 0\n",
550 + "3 2019-07-04 0 0 0 0 ... 2760 0 0 0 0\n",
551 + "4 2019-07-05 0 0 0 0 ... 0 0 0 0 0\n",
552 + "\n",
553 + "[5 rows x 25 columns]"
554 + ]
555 + },
556 + "metadata": {
557 + "tags": []
558 + },
559 + "execution_count": 7
560 + }
561 + ]
562 + },
563 + {
564 + "cell_type": "code",
565 + "metadata": {
566 + "id": "okcsLKdqGqSZ",
567 + "outputId": "2657961c-9e5d-4bda-ecd1-356635ac98a7",
568 + "colab": {
569 + "base_uri": "https://localhost:8080/",
570 + "height": 235
571 + }
572 + },
573 + "source": [
574 + "GNTvariables=['1:00','2:00','3:00','4:00','5:00','6:00','7:00','8:00','9:00','10:00','11:00','12:00','13:00','14:00','15:00','16:00','17:00','18:00','19:00','20:00','21:00','22:00','23:00','0:00']\n",
575 + "tempGNT=gnt[GNTvariables]\n",
576 + "tempGNT.index=gnt['일자']\n",
577 + "tempGNT.head()"
578 + ],
579 + "execution_count": null,
580 + "outputs": [
581 + {
582 + "output_type": "execute_result",
583 + "data": {
584 + "text/html": [
585 + "<div>\n",
586 + "<style scoped>\n",
587 + " .dataframe tbody tr th:only-of-type {\n",
588 + " vertical-align: middle;\n",
589 + " }\n",
590 + "\n",
591 + " .dataframe tbody tr th {\n",
592 + " vertical-align: top;\n",
593 + " }\n",
594 + "\n",
595 + " .dataframe thead th {\n",
596 + " text-align: right;\n",
597 + " }\n",
598 + "</style>\n",
599 + "<table border=\"1\" class=\"dataframe\">\n",
600 + " <thead>\n",
601 + " <tr style=\"text-align: right;\">\n",
602 + " <th></th>\n",
603 + " <th>1:00</th>\n",
604 + " <th>2:00</th>\n",
605 + " <th>3:00</th>\n",
606 + " <th>4:00</th>\n",
607 + " <th>5:00</th>\n",
608 + " <th>6:00</th>\n",
609 + " <th>7:00</th>\n",
610 + " <th>8:00</th>\n",
611 + " <th>9:00</th>\n",
612 + " <th>10:00</th>\n",
613 + " <th>11:00</th>\n",
614 + " <th>12:00</th>\n",
615 + " <th>13:00</th>\n",
616 + " <th>14:00</th>\n",
617 + " <th>15:00</th>\n",
618 + " <th>16:00</th>\n",
619 + " <th>17:00</th>\n",
620 + " <th>18:00</th>\n",
621 + " <th>19:00</th>\n",
622 + " <th>20:00</th>\n",
623 + " <th>21:00</th>\n",
624 + " <th>22:00</th>\n",
625 + " <th>23:00</th>\n",
626 + " <th>0:00</th>\n",
627 + " </tr>\n",
628 + " <tr>\n",
629 + " <th>일자</th>\n",
630 + " <th></th>\n",
631 + " <th></th>\n",
632 + " <th></th>\n",
633 + " <th></th>\n",
634 + " <th></th>\n",
635 + " <th></th>\n",
636 + " <th></th>\n",
637 + " <th></th>\n",
638 + " <th></th>\n",
639 + " <th></th>\n",
640 + " <th></th>\n",
641 + " <th></th>\n",
642 + " <th></th>\n",
643 + " <th></th>\n",
644 + " <th></th>\n",
645 + " <th></th>\n",
646 + " <th></th>\n",
647 + " <th></th>\n",
648 + " <th></th>\n",
649 + " <th></th>\n",
650 + " <th></th>\n",
651 + " <th></th>\n",
652 + " <th></th>\n",
653 + " <th></th>\n",
654 + " </tr>\n",
655 + " </thead>\n",
656 + " <tbody>\n",
657 + " <tr>\n",
658 + " <th>2019-07-01</th>\n",
659 + " <td>0</td>\n",
660 + " <td>0</td>\n",
661 + " <td>0</td>\n",
662 + " <td>0</td>\n",
663 + " <td>0</td>\n",
664 + " <td>360</td>\n",
665 + " <td>6000</td>\n",
666 + " <td>92880</td>\n",
667 + " <td>244560</td>\n",
668 + " <td>357480</td>\n",
669 + " <td>464880</td>\n",
670 + " <td>720840</td>\n",
671 + " <td>744000</td>\n",
672 + " <td>532200</td>\n",
673 + " <td>462240</td>\n",
674 + " <td>433200</td>\n",
675 + " <td>348120</td>\n",
676 + " <td>336720</td>\n",
677 + " <td>109920</td>\n",
678 + " <td>0</td>\n",
679 + " <td>0</td>\n",
680 + " <td>0</td>\n",
681 + " <td>0</td>\n",
682 + " <td>0</td>\n",
683 + " </tr>\n",
684 + " <tr>\n",
685 + " <th>2019-07-02</th>\n",
686 + " <td>0</td>\n",
687 + " <td>0</td>\n",
688 + " <td>0</td>\n",
689 + " <td>0</td>\n",
690 + " <td>0</td>\n",
691 + " <td>120</td>\n",
692 + " <td>81720</td>\n",
693 + " <td>313800</td>\n",
694 + " <td>625680</td>\n",
695 + " <td>831360</td>\n",
696 + " <td>1062240</td>\n",
697 + " <td>861240</td>\n",
698 + " <td>849600</td>\n",
699 + " <td>785280</td>\n",
700 + " <td>915960</td>\n",
701 + " <td>843240</td>\n",
702 + " <td>565320</td>\n",
703 + " <td>178080</td>\n",
704 + " <td>115800</td>\n",
705 + " <td>6720</td>\n",
706 + " <td>0</td>\n",
707 + " <td>0</td>\n",
708 + " <td>0</td>\n",
709 + " <td>0</td>\n",
710 + " </tr>\n",
711 + " <tr>\n",
712 + " <th>2019-07-03</th>\n",
713 + " <td>0</td>\n",
714 + " <td>0</td>\n",
715 + " <td>0</td>\n",
716 + " <td>0</td>\n",
717 + " <td>0</td>\n",
718 + " <td>0</td>\n",
719 + " <td>54480</td>\n",
720 + " <td>219720</td>\n",
721 + " <td>320400</td>\n",
722 + " <td>602400</td>\n",
723 + " <td>805440</td>\n",
724 + " <td>917280</td>\n",
725 + " <td>815760</td>\n",
726 + " <td>569520</td>\n",
727 + " <td>613080</td>\n",
728 + " <td>726480</td>\n",
729 + " <td>510120</td>\n",
730 + " <td>313440</td>\n",
731 + " <td>68400</td>\n",
732 + " <td>0</td>\n",
733 + " <td>0</td>\n",
734 + " <td>0</td>\n",
735 + " <td>0</td>\n",
736 + " <td>0</td>\n",
737 + " </tr>\n",
738 + " <tr>\n",
739 + " <th>2019-07-04</th>\n",
740 + " <td>0</td>\n",
741 + " <td>0</td>\n",
742 + " <td>0</td>\n",
743 + " <td>0</td>\n",
744 + " <td>0</td>\n",
745 + " <td>120</td>\n",
746 + " <td>65160</td>\n",
747 + " <td>310560</td>\n",
748 + " <td>623400</td>\n",
749 + " <td>877440</td>\n",
750 + " <td>1044240</td>\n",
751 + " <td>1170240</td>\n",
752 + " <td>1202400</td>\n",
753 + " <td>1159920</td>\n",
754 + " <td>1071600</td>\n",
755 + " <td>680280</td>\n",
756 + " <td>467640</td>\n",
757 + " <td>267000</td>\n",
758 + " <td>130560</td>\n",
759 + " <td>2760</td>\n",
760 + " <td>0</td>\n",
761 + " <td>0</td>\n",
762 + " <td>0</td>\n",
763 + " <td>0</td>\n",
764 + " </tr>\n",
765 + " <tr>\n",
766 + " <th>2019-07-05</th>\n",
767 + " <td>0</td>\n",
768 + " <td>0</td>\n",
769 + " <td>0</td>\n",
770 + " <td>0</td>\n",
771 + " <td>0</td>\n",
772 + " <td>0</td>\n",
773 + " <td>57120</td>\n",
774 + " <td>310320</td>\n",
775 + " <td>520320</td>\n",
776 + " <td>847800</td>\n",
777 + " <td>1031760</td>\n",
778 + " <td>1145280</td>\n",
779 + " <td>1186680</td>\n",
780 + " <td>1146360</td>\n",
781 + " <td>970200</td>\n",
782 + " <td>664920</td>\n",
783 + " <td>506520</td>\n",
784 + " <td>304200</td>\n",
785 + " <td>64560</td>\n",
786 + " <td>0</td>\n",
787 + " <td>0</td>\n",
788 + " <td>0</td>\n",
789 + " <td>0</td>\n",
790 + " <td>0</td>\n",
791 + " </tr>\n",
792 + " </tbody>\n",
793 + "</table>\n",
794 + "</div>"
795 + ],
796 + "text/plain": [
797 + " 1:00 2:00 3:00 4:00 5:00 ... 20:00 21:00 22:00 23:00 0:00\n",
798 + "일자 ... \n",
799 + "2019-07-01 0 0 0 0 0 ... 0 0 0 0 0\n",
800 + "2019-07-02 0 0 0 0 0 ... 6720 0 0 0 0\n",
801 + "2019-07-03 0 0 0 0 0 ... 0 0 0 0 0\n",
802 + "2019-07-04 0 0 0 0 0 ... 2760 0 0 0 0\n",
803 + "2019-07-05 0 0 0 0 0 ... 0 0 0 0 0\n",
804 + "\n",
805 + "[5 rows x 24 columns]"
806 + ]
807 + },
808 + "metadata": {
809 + "tags": []
810 + },
811 + "execution_count": 8
812 + }
813 + ]
814 + },
815 + {
816 + "cell_type": "code",
817 + "metadata": {
818 + "id": "tCCfCxurIETg",
819 + "outputId": "5be25bb4-050b-4fbe-cafa-615f489c1b9b",
820 + "colab": {
821 + "base_uri": "https://localhost:8080/"
822 + }
823 + },
824 + "source": [
825 + "print(tempGNT.columns)"
826 + ],
827 + "execution_count": null,
828 + "outputs": [
829 + {
830 + "output_type": "stream",
831 + "text": [
832 + "Index(['1:00', '2:00', '3:00', '4:00', '5:00', '6:00', '7:00', '8:00', '9:00',\n",
833 + " '10:00', '11:00', '12:00', '13:00', '14:00', '15:00', '16:00', '17:00',\n",
834 + " '18:00', '19:00', '20:00', '21:00', '22:00', '23:00', '0:00'],\n",
835 + " dtype='object')\n"
836 + ],
837 + "name": "stdout"
838 + }
839 + ]
840 + },
841 + {
842 + "cell_type": "code",
843 + "metadata": {
844 + "id": "i7DnCIAaM_lq"
845 + },
846 + "source": [
847 + "temp=pd.DataFrame(columns=['발전량'])"
848 + ],
849 + "execution_count": null,
850 + "outputs": []
851 + },
852 + {
853 + "cell_type": "code",
854 + "metadata": {
855 + "id": "iCbGuA5oKX46"
856 + },
857 + "source": [
858 + "t={}"
859 + ],
860 + "execution_count": null,
861 + "outputs": []
862 + },
863 + {
864 + "cell_type": "code",
865 + "metadata": {
866 + "id": "vsPgEOdzL63t",
867 + "outputId": "0a722ead-28ce-4540-baae-60d860a9b10b",
868 + "colab": {
869 + "base_uri": "https://localhost:8080/"
870 + }
871 + },
872 + "source": [
873 + "print(tempGNT['0:00']['2019-07-01'])"
874 + ],
875 + "execution_count": null,
876 + "outputs": [
877 + {
878 + "output_type": "stream",
879 + "text": [
880 + "0\n"
881 + ],
882 + "name": "stdout"
883 + }
884 + ]
885 + },
886 + {
887 + "cell_type": "code",
888 + "metadata": {
889 + "id": "DY2K5OagIB8A"
890 + },
891 + "source": [
892 + "for date in tempGNT.index:\n",
893 + " for hour in tempGNT.columns:\n",
894 + " t[date+' '+hour]=tempGNT[hour][date]"
895 + ],
896 + "execution_count": null,
897 + "outputs": []
898 + },
899 + {
900 + "cell_type": "code",
901 + "metadata": {
902 + "id": "9jwM6hWaN-lu",
903 + "outputId": "e90c4ab0-3a10-4989-8ddc-aef8f078f578",
904 + "colab": {
905 + "base_uri": "https://localhost:8080/"
906 + }
907 + },
908 + "source": [
909 + "print(t.values())"
910 + ],
911 + "execution_count": null,
912 + "outputs": [
913 + {
914 + "output_type": "stream",
915 + "text": [
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928 + "temp=pd.DataFrame(t.values(), index=t.keys())"
929 + ],
930 + "execution_count": null,
931 + "outputs": []
932 + },
933 + {
934 + "cell_type": "code",
935 + "metadata": {
936 + "id": "p2a5g_jRMqwy",
937 + "outputId": "dccec0e2-884a-43c9-b294-4933a6b5de1e",
938 + "colab": {
939 + "base_uri": "https://localhost:8080/"
940 + }
941 + },
942 + "source": [
943 + "print(temp)"
944 + ],
945 + "execution_count": null,
946 + "outputs": [
947 + {
948 + "output_type": "stream",
949 + "text": [
950 + " 0\n",
951 + "2019-07-01 1:00 0\n",
952 + "2019-07-01 2:00 0\n",
953 + "2019-07-01 3:00 0\n",
954 + "2019-07-01 4:00 0\n",
955 + "2019-07-01 5:00 0\n",
956 + "... ..\n",
957 + "2020-06-30 20:00 0\n",
958 + "2020-06-30 21:00 0\n",
959 + "2020-06-30 22:00 0\n",
960 + "2020-06-30 23:00 0\n",
961 + "2020-06-30 0:00 0\n",
962 + "\n",
963 + "[8784 rows x 1 columns]\n"
964 + ],
965 + "name": "stdout"
966 + }
967 + ]
968 + },
969 + {
970 + "cell_type": "code",
971 + "metadata": {
972 + "id": "yv-wtS5tNLNv",
973 + "outputId": "351f0e0c-3e97-4e4a-a982-3571c0a64e07",
974 + "colab": {
975 + "base_uri": "https://localhost:8080/",
976 + "height": 235
977 + }
978 + },
979 + "source": [
980 + "temp.columns=['발전량']\n",
981 + "temp.index.names=['일자']\n",
982 + "temp.head()"
983 + ],
984 + "execution_count": null,
985 + "outputs": [
986 + {
987 + "output_type": "execute_result",
988 + "data": {
989 + "text/html": [
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1006 + " <tr style=\"text-align: right;\">\n",
1007 + " <th></th>\n",
1008 + " <th>발전량</th>\n",
1009 + " </tr>\n",
1010 + " <tr>\n",
1011 + " <th>일자</th>\n",
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1017 + " <th>2019-07-01 1:00</th>\n",
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1035 + " </tr>\n",
1036 + " </tbody>\n",
1037 + "</table>\n",
1038 + "</div>"
1039 + ],
1040 + "text/plain": [
1041 + " 발전량\n",
1042 + "일자 \n",
1043 + "2019-07-01 1:00 0\n",
1044 + "2019-07-01 2:00 0\n",
1045 + "2019-07-01 3:00 0\n",
1046 + "2019-07-01 4:00 0\n",
1047 + "2019-07-01 5:00 0"
1048 + ]
1049 + },
1050 + "metadata": {
1051 + "tags": []
1052 + },
1053 + "execution_count": 57
1054 + }
1055 + ]
1056 + },
1057 + {
1058 + "cell_type": "code",
1059 + "metadata": {
1060 + "id": "sYP_krzqQ7OS"
1061 + },
1062 + "source": [
1063 + "def min_max_normalize(lst):\n",
1064 + " normalize = []\n",
1065 + "\n",
1066 + " for value in lst:\n",
1067 + " normalized_num=(value - min(lst))/(max(lst)-min(lst))\n",
1068 + " normalize.append(normalized_num)\n",
1069 + "\n",
1070 + " return normalize"
1071 + ],
1072 + "execution_count": null,
1073 + "outputs": []
1074 + },
1075 + {
1076 + "cell_type": "code",
1077 + "metadata": {
1078 + "id": "nRTowLdHWfHp",
1079 + "outputId": "15589397-2570-437f-e421-b1f5c9e8a8b1",
1080 + "colab": {
1081 + "base_uri": "https://localhost:8080/",
1082 + "height": 343
1083 + }
1084 + },
1085 + "source": [
1086 + "wtData['발전량']=0\n",
1087 + "wtData.head()"
1088 + ],
1089 + "execution_count": 61,
1090 + "outputs": [
1091 + {
1092 + "output_type": "stream",
1093 + "text": [
1094 + "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
1095 + "A value is trying to be set on a copy of a slice from a DataFrame.\n",
1096 + "Try using .loc[row_indexer,col_indexer] = value instead\n",
1097 + "\n",
1098 + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
1099 + " \"\"\"Entry point for launching an IPython kernel.\n"
1100 + ],
1101 + "name": "stderr"
1102 + },
1103 + {
1104 + "output_type": "execute_result",
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1106 + "text/html": [
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1120 + "</style>\n",
1121 + "<table border=\"1\" class=\"dataframe\">\n",
1122 + " <thead>\n",
1123 + " <tr style=\"text-align: right;\">\n",
1124 + " <th></th>\n",
1125 + " <th>기온(°C)</th>\n",
1126 + " <th>강수량(mm)</th>\n",
1127 + " <th>습도(%)</th>\n",
1128 + " <th>현지기압(hPa)</th>\n",
1129 + " <th>지면온도(°C)</th>\n",
1130 + " <th>발전량</th>\n",
1131 + " </tr>\n",
1132 + " <tr>\n",
1133 + " <th>일시</th>\n",
1134 + " <th></th>\n",
1135 + " <th></th>\n",
1136 + " <th></th>\n",
1137 + " <th></th>\n",
1138 + " <th></th>\n",
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1141 + " </thead>\n",
1142 + " <tbody>\n",
1143 + " <tr>\n",
1144 + " <th>2019-07-01 1:00</th>\n",
1145 + " <td>22.2</td>\n",
1146 + " <td>NaN</td>\n",
1147 + " <td>90</td>\n",
1148 + " <td>993.5</td>\n",
1149 + " <td>22.0</td>\n",
1150 + " <td>0</td>\n",
1151 + " </tr>\n",
1152 + " <tr>\n",
1153 + " <th>2019-07-01 2:00</th>\n",
1154 + " <td>22.4</td>\n",
1155 + " <td>NaN</td>\n",
1156 + " <td>89</td>\n",
1157 + " <td>993.4</td>\n",
1158 + " <td>22.2</td>\n",
1159 + " <td>0</td>\n",
1160 + " </tr>\n",
1161 + " <tr>\n",
1162 + " <th>2019-07-01 3:00</th>\n",
1163 + " <td>22.3</td>\n",
1164 + " <td>NaN</td>\n",
1165 + " <td>90</td>\n",
1166 + " <td>993.4</td>\n",
1167 + " <td>22.1</td>\n",
1168 + " <td>0</td>\n",
1169 + " </tr>\n",
1170 + " <tr>\n",
1171 + " <th>2019-07-01 4:00</th>\n",
1172 + " <td>21.9</td>\n",
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1176 + " <td>21.4</td>\n",
1177 + " <td>0</td>\n",
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1179 + " <tr>\n",
1180 + " <th>2019-07-01 5:00</th>\n",
1181 + " <td>21.8</td>\n",
1182 + " <td>NaN</td>\n",
1183 + " <td>91</td>\n",
1184 + " <td>993.9</td>\n",
1185 + " <td>21.2</td>\n",
1186 + " <td>0</td>\n",
1187 + " </tr>\n",
1188 + " </tbody>\n",
1189 + "</table>\n",
1190 + "</div>"
1191 + ],
1192 + "text/plain": [
1193 + " 기온(°C) 강수량(mm) 습도(%) 현지기압(hPa) 지면온도(°C) 발전량\n",
1194 + "일시 \n",
1195 + "2019-07-01 1:00 22.2 NaN 90 993.5 22.0 0\n",
1196 + "2019-07-01 2:00 22.4 NaN 89 993.4 22.2 0\n",
1197 + "2019-07-01 3:00 22.3 NaN 90 993.4 22.1 0\n",
1198 + "2019-07-01 4:00 21.9 NaN 91 993.4 21.4 0\n",
1199 + "2019-07-01 5:00 21.8 NaN 91 993.9 21.2 0"
1200 + ]
1201 + },
1202 + "metadata": {
1203 + "tags": []
1204 + },
1205 + "execution_count": 61
1206 + }
1207 + ]
1208 + },
1209 + {
1210 + "cell_type": "code",
1211 + "metadata": {
1212 + "id": "xAfRDfGsS5Gh",
1213 + "outputId": "d7433755-bf6e-4e49-d0b8-cf99339883bf",
1214 + "colab": {
1215 + "base_uri": "https://localhost:8080/"
1216 + }
1217 + },
1218 + "source": [
1219 + "for date1 in wtData.index:\n",
1220 + " for date2 in temp.index:\n",
1221 + " if date1==date2:\n",
1222 + " wtData['발전량'][date1]=temp['발전량'][date2]"
1223 + ],
1224 + "execution_count": 62,
1225 + "outputs": [
1226 + {
1227 + "output_type": "stream",
1228 + "text": [
1229 + "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
1230 + "A value is trying to be set on a copy of a slice from a DataFrame\n",
1231 + "\n",
1232 + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
1233 + " after removing the cwd from sys.path.\n",
1234 + "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2882: SettingWithCopyWarning: \n",
1235 + "A value is trying to be set on a copy of a slice from a DataFrame\n",
1236 + "\n",
1237 + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
1238 + " exec(code_obj, self.user_global_ns, self.user_ns)\n"
1239 + ],
1240 + "name": "stderr"
1241 + }
1242 + ]
1243 + },
1244 + {
1245 + "cell_type": "code",
1246 + "metadata": {
1247 + "id": "QcgiXXo0XJfY",
1248 + "outputId": "3910dc06-297d-4caf-d18a-a0a0c9f30377",
1249 + "colab": {
1250 + "base_uri": "https://localhost:8080/",
1251 + "height": 235
1252 + }
1253 + },
1254 + "source": [
1255 + "wtData.head()"
1256 + ],
1257 + "execution_count": 63,
1258 + "outputs": [
1259 + {
1260 + "output_type": "execute_result",
1261 + "data": {
1262 + "text/html": [
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1276 + "</style>\n",
1277 + "<table border=\"1\" class=\"dataframe\">\n",
1278 + " <thead>\n",
1279 + " <tr style=\"text-align: right;\">\n",
1280 + " <th></th>\n",
1281 + " <th>기온(°C)</th>\n",
1282 + " <th>강수량(mm)</th>\n",
1283 + " <th>습도(%)</th>\n",
1284 + " <th>현지기압(hPa)</th>\n",
1285 + " <th>지면온도(°C)</th>\n",
1286 + " <th>발전량</th>\n",
1287 + " </tr>\n",
1288 + " <tr>\n",
1289 + " <th>일시</th>\n",
1290 + " <th></th>\n",
1291 + " <th></th>\n",
1292 + " <th></th>\n",
1293 + " <th></th>\n",
1294 + " <th></th>\n",
1295 + " <th></th>\n",
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1297 + " </thead>\n",
1298 + " <tbody>\n",
1299 + " <tr>\n",
1300 + " <th>2019-07-01 1:00</th>\n",
1301 + " <td>22.2</td>\n",
1302 + " <td>NaN</td>\n",
1303 + " <td>90</td>\n",
1304 + " <td>993.5</td>\n",
1305 + " <td>22.0</td>\n",
1306 + " <td>0</td>\n",
1307 + " </tr>\n",
1308 + " <tr>\n",
1309 + " <th>2019-07-01 2:00</th>\n",
1310 + " <td>22.4</td>\n",
1311 + " <td>NaN</td>\n",
1312 + " <td>89</td>\n",
1313 + " <td>993.4</td>\n",
1314 + " <td>22.2</td>\n",
1315 + " <td>0</td>\n",
1316 + " </tr>\n",
1317 + " <tr>\n",
1318 + " <th>2019-07-01 3:00</th>\n",
1319 + " <td>22.3</td>\n",
1320 + " <td>NaN</td>\n",
1321 + " <td>90</td>\n",
1322 + " <td>993.4</td>\n",
1323 + " <td>22.1</td>\n",
1324 + " <td>0</td>\n",
1325 + " </tr>\n",
1326 + " <tr>\n",
1327 + " <th>2019-07-01 4:00</th>\n",
1328 + " <td>21.9</td>\n",
1329 + " <td>NaN</td>\n",
1330 + " <td>91</td>\n",
1331 + " <td>993.4</td>\n",
1332 + " <td>21.4</td>\n",
1333 + " <td>0</td>\n",
1334 + " </tr>\n",
1335 + " <tr>\n",
1336 + " <th>2019-07-01 5:00</th>\n",
1337 + " <td>21.8</td>\n",
1338 + " <td>NaN</td>\n",
1339 + " <td>91</td>\n",
1340 + " <td>993.9</td>\n",
1341 + " <td>21.2</td>\n",
1342 + " <td>0</td>\n",
1343 + " </tr>\n",
1344 + " </tbody>\n",
1345 + "</table>\n",
1346 + "</div>"
1347 + ],
1348 + "text/plain": [
1349 + " 기온(°C) 강수량(mm) 습도(%) 현지기압(hPa) 지면온도(°C) 발전량\n",
1350 + "일시 \n",
1351 + "2019-07-01 1:00 22.2 NaN 90 993.5 22.0 0\n",
1352 + "2019-07-01 2:00 22.4 NaN 89 993.4 22.2 0\n",
1353 + "2019-07-01 3:00 22.3 NaN 90 993.4 22.1 0\n",
1354 + "2019-07-01 4:00 21.9 NaN 91 993.4 21.4 0\n",
1355 + "2019-07-01 5:00 21.8 NaN 91 993.9 21.2 0"
1356 + ]
1357 + },
1358 + "metadata": {
1359 + "tags": []
1360 + },
1361 + "execution_count": 63
1362 + }
1363 + ]
1364 + },
1365 + {
1366 + "cell_type": "code",
1367 + "metadata": {
1368 + "id": "p27JCDz8XRaA"
1369 + },
1370 + "source": [
1371 + ""
1372 + ],
1373 + "execution_count": null,
1374 + "outputs": []
1375 + }
1376 + ]
1377 +}
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