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Example k-means reduction for AT and BE input data.
2021-01-11 22:12:55 +00:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# example k-means reduced zonal dataset "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
Added init.py file. Added csv output lines in main.
2021-01-17 01:29:06 +00:00
"# custom scripts \n",
Example k-means reduction for AT and BE input data.
2021-01-11 22:12:55 +00:00
"import scripts.read as read\n",
"import scripts.prep as prep\n",
"import scripts.kmeans as kmeans"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INTERCONN is identically zero.\n"
]
},
{
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>BE_ND</th>\n",
" <th>BE_WIND_ONSHORE</th>\n",
" <th>BE_WIND_OFFSHORE</th>\n",
" <th>BE_SOLAR</th>\n",
" <th>BE_HYDRO</th>\n",
" <th>BE_BIOMASS</th>\n",
" <th>BE_NUCLEAR</th>\n",
" <th>BE_OTHER_GEN</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>8760.000000</td>\n",
" <td>8760.000000</td>\n",
" <td>8760.000000</td>\n",
" <td>8760.000000</td>\n",
" <td>8760.000000</td>\n",
" <td>8760.000000</td>\n",
" <td>8760.000000</td>\n",
" <td>8760.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>2.975173</td>\n",
" <td>0.171542</td>\n",
" <td>0.408345</td>\n",
" <td>0.102262</td>\n",
" <td>0.081661</td>\n",
" <td>0.462982</td>\n",
" <td>0.478261</td>\n",
" <td>0.498569</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.378897</td>\n",
" <td>0.160393</td>\n",
" <td>0.330119</td>\n",
" <td>0.151439</td>\n",
" <td>0.067165</td>\n",
" <td>0.117066</td>\n",
" <td>0.125715</td>\n",
" <td>0.045038</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>2.055689</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.060649</td>\n",
" <td>0.236078</td>\n",
" <td>0.255435</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.721740</td>\n",
" <td>0.043827</td>\n",
" <td>0.099719</td>\n",
" <td>0.000000</td>\n",
" <td>0.017391</td>\n",
" <td>0.444288</td>\n",
" <td>0.412420</td>\n",
" <td>0.470109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>3.016437</td>\n",
" <td>0.116049</td>\n",
" <td>0.334270</td>\n",
" <td>0.002615</td>\n",
" <td>0.069565</td>\n",
" <td>0.469676</td>\n",
" <td>0.507256</td>\n",
" <td>0.502717</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>3.234711</td>\n",
" <td>0.257407</td>\n",
" <td>0.745787</td>\n",
" <td>0.173244</td>\n",
" <td>0.147826</td>\n",
" <td>0.554302</td>\n",
" <td>0.579312</td>\n",
" <td>0.532609</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>4.000000</td>\n",
" <td>0.998148</td>\n",
" <td>0.967697</td>\n",
" <td>0.660135</td>\n",
" <td>0.208696</td>\n",
" <td>0.630465</td>\n",
" <td>0.822140</td>\n",
" <td>0.614130</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" BE_ND BE_WIND_ONSHORE BE_WIND_OFFSHORE BE_SOLAR \\\n",
"count 8760.000000 8760.000000 8760.000000 8760.000000 \n",
"mean 2.975173 0.171542 0.408345 0.102262 \n",
"std 0.378897 0.160393 0.330119 0.151439 \n",
"min 2.055689 0.000000 0.000000 0.000000 \n",
"25% 2.721740 0.043827 0.099719 0.000000 \n",
"50% 3.016437 0.116049 0.334270 0.002615 \n",
"75% 3.234711 0.257407 0.745787 0.173244 \n",
"max 4.000000 0.998148 0.967697 0.660135 \n",
"\n",
" BE_HYDRO BE_BIOMASS BE_NUCLEAR BE_OTHER_GEN \n",
"count 8760.000000 8760.000000 8760.000000 8760.000000 \n",
"mean 0.081661 0.462982 0.478261 0.498569 \n",
"std 0.067165 0.117066 0.125715 0.045038 \n",
"min 0.000000 0.060649 0.236078 0.255435 \n",
"25% 0.017391 0.444288 0.412420 0.470109 \n",
"50% 0.069565 0.469676 0.507256 0.502717 \n",
"75% 0.147826 0.554302 0.579312 0.532609 \n",
"max 0.208696 0.630465 0.822140 0.614130 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# load BE data\n",
"df_BE=read.zonal_data('zonal_data/INPUT_DATA_ZONAL_BE.xlsx')\n",
"# normalisation factor of 4 for BE_ND to increase its importance in the subsequent clustering\n",
"df_BE['ND']=prep.max_normalise(df_BE['ND'], 4) \n",
"# add 'BE' in front of every column in the dataframe to to distinguish between countries in merged dataframe\n",
"df_BE=prep.add_country_name(df_BE, 'BE')\n",
"df_BE.describe()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WIND_OFFSHORE is identically zero.\n",
"NUCLEAR is identically zero.\n",
"OTHER_GEN is identically zero.\n"
]
},
{
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AT_ND</th>\n",
" <th>AT_WIND_ONSHORE</th>\n",
" <th>AT_SOLAR</th>\n",
" <th>AT_HYDRO</th>\n",
" <th>AT_BIOMASS</th>\n",
" <th>AT_INTERCONN</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>8760.000000</td>\n",
" <td>8760.000000</td>\n",
" <td>8760.000000</td>\n",
" <td>8760.000000</td>\n",
" <td>8760.000000</td>\n",
" <td>8760.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>2.985608</td>\n",
" <td>0.253861</td>\n",
" <td>0.159438</td>\n",
" <td>0.398663</td>\n",
" <td>0.644243</td>\n",
" <td>0.237638</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.474555</td>\n",
" <td>0.143749</td>\n",
" <td>0.224376</td>\n",
" <td>0.133731</td>\n",
" <td>0.043179</td>\n",
" <td>0.247113</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.868206</td>\n",
" <td>0.003713</td>\n",
" <td>0.000000</td>\n",
" <td>0.165434</td>\n",
" <td>0.521552</td>\n",
" <td>-0.697967</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.654007</td>\n",
" <td>0.142456</td>\n",
" <td>0.000000</td>\n",
" <td>0.299161</td>\n",
" <td>0.620690</td>\n",
" <td>0.074954</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>3.034726</td>\n",
" <td>0.235946</td>\n",
" <td>0.017462</td>\n",
" <td>0.364130</td>\n",
" <td>0.655172</td>\n",
" <td>0.242514</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>3.318725</td>\n",
" <td>0.345979</td>\n",
" <td>0.281090</td>\n",
" <td>0.491588</td>\n",
" <td>0.672414</td>\n",
" <td>0.409242</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>4.000000</td>\n",
" <td>0.841730</td>\n",
" <td>0.867547</td>\n",
" <td>0.791855</td>\n",
" <td>0.741379</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AT_ND AT_WIND_ONSHORE AT_SOLAR AT_HYDRO AT_BIOMASS \\\n",
"count 8760.000000 8760.000000 8760.000000 8760.000000 8760.000000 \n",
"mean 2.985608 0.253861 0.159438 0.398663 0.644243 \n",
"std 0.474555 0.143749 0.224376 0.133731 0.043179 \n",
"min 1.868206 0.003713 0.000000 0.165434 0.521552 \n",
"25% 2.654007 0.142456 0.000000 0.299161 0.620690 \n",
"50% 3.034726 0.235946 0.017462 0.364130 0.655172 \n",
"75% 3.318725 0.345979 0.281090 0.491588 0.672414 \n",
"max 4.000000 0.841730 0.867547 0.791855 0.741379 \n",
"\n",
" AT_INTERCONN \n",
"count 8760.000000 \n",
"mean 0.237638 \n",
"std 0.247113 \n",
"min -0.697967 \n",
"25% 0.074954 \n",
"50% 0.242514 \n",
"75% 0.409242 \n",
"max 1.000000 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# load AT data\n",
"df_AT=read.zonal_data('zonal_data/INPUT_DATA_ZONAL_AT.xlsx')\n",
"# normalisation factor of 4 for BE_ND to increase its importance in the subsequent clustering\n",
"df_AT['ND']=prep.max_normalise(df_AT['ND'], 4)\n",
"# AT_INTERCONN has normalisation factor of 1 (default)\n",
"df_AT['INTERCONN']=prep.max_normalise(df_AT['INTERCONN'])\n",
"# add 'AT' in front of every column in the dataframe\n",
"df_AT=prep.add_country_name(df_AT, 'AT')\n",
"df_AT.describe()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>BE_ND</th>\n",
" <th>BE_WIND_ONSHORE</th>\n",
" <th>BE_WIND_OFFSHORE</th>\n",
" <th>BE_SOLAR</th>\n",
" <th>BE_HYDRO</th>\n",
" <th>BE_BIOMASS</th>\n",
" <th>BE_NUCLEAR</th>\n",
" <th>BE_OTHER_GEN</th>\n",
" <th>AT_ND</th>\n",
" <th>AT_WIND_ONSHORE</th>\n",
" <th>AT_SOLAR</th>\n",
" <th>AT_HYDRO</th>\n",
" <th>AT_BIOMASS</th>\n",
" <th>AT_INTERCONN</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>8760.000000</td>\n",
" <td>8760.000000</td>\n",
" <td>8760.000000</td>\n",
" <td>8760.000000</td>\n",
" <td>8760.000000</td>\n",
" <td>8760.000000</td>\n",
" <td>8760.000000</td>\n",
" <td>8760.000000</td>\n",
" <td>8760.000000</td>\n",
" <td>8760.000000</td>\n",
" <td>8760.000000</td>\n",
" <td>8760.000000</td>\n",
" <td>8760.000000</td>\n",
" <td>8760.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>2.975173</td>\n",
" <td>0.171542</td>\n",
" <td>0.408345</td>\n",
" <td>0.102262</td>\n",
" <td>0.081661</td>\n",
" <td>0.462982</td>\n",
" <td>0.478261</td>\n",
" <td>0.498569</td>\n",
" <td>2.985608</td>\n",
" <td>0.253861</td>\n",
" <td>0.159438</td>\n",
" <td>0.398663</td>\n",
" <td>0.644243</td>\n",
" <td>0.237638</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.378897</td>\n",
" <td>0.160393</td>\n",
" <td>0.330119</td>\n",
" <td>0.151439</td>\n",
" <td>0.067165</td>\n",
" <td>0.117066</td>\n",
" <td>0.125715</td>\n",
" <td>0.045038</td>\n",
" <td>0.474555</td>\n",
" <td>0.143749</td>\n",
" <td>0.224376</td>\n",
" <td>0.133731</td>\n",
" <td>0.043179</td>\n",
" <td>0.247113</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>2.055689</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.060649</td>\n",
" <td>0.236078</td>\n",
" <td>0.255435</td>\n",
" <td>1.868206</td>\n",
" <td>0.003713</td>\n",
" <td>0.000000</td>\n",
" <td>0.165434</td>\n",
" <td>0.521552</td>\n",
" <td>-0.697967</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.721740</td>\n",
" <td>0.043827</td>\n",
" <td>0.099719</td>\n",
" <td>0.000000</td>\n",
" <td>0.017391</td>\n",
" <td>0.444288</td>\n",
" <td>0.412420</td>\n",
" <td>0.470109</td>\n",
" <td>2.654007</td>\n",
" <td>0.142456</td>\n",
" <td>0.000000</td>\n",
" <td>0.299161</td>\n",
" <td>0.620690</td>\n",
" <td>0.074954</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>3.016437</td>\n",
" <td>0.116049</td>\n",
" <td>0.334270</td>\n",
" <td>0.002615</td>\n",
" <td>0.069565</td>\n",
" <td>0.469676</td>\n",
" <td>0.507256</td>\n",
" <td>0.502717</td>\n",
" <td>3.034726</td>\n",
" <td>0.235946</td>\n",
" <td>0.017462</td>\n",
" <td>0.364130</td>\n",
" <td>0.655172</td>\n",
" <td>0.242514</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>3.234711</td>\n",
" <td>0.257407</td>\n",
" <td>0.745787</td>\n",
" <td>0.173244</td>\n",
" <td>0.147826</td>\n",
" <td>0.554302</td>\n",
" <td>0.579312</td>\n",
" <td>0.532609</td>\n",
" <td>3.318725</td>\n",
" <td>0.345979</td>\n",
" <td>0.281090</td>\n",
" <td>0.491588</td>\n",
" <td>0.672414</td>\n",
" <td>0.409242</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>4.000000</td>\n",
" <td>0.998148</td>\n",
" <td>0.967697</td>\n",
" <td>0.660135</td>\n",
" <td>0.208696</td>\n",
" <td>0.630465</td>\n",
" <td>0.822140</td>\n",
" <td>0.614130</td>\n",
" <td>4.000000</td>\n",
" <td>0.841730</td>\n",
" <td>0.867547</td>\n",
" <td>0.791855</td>\n",
" <td>0.741379</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" BE_ND BE_WIND_ONSHORE BE_WIND_OFFSHORE BE_SOLAR \\\n",
"count 8760.000000 8760.000000 8760.000000 8760.000000 \n",
"mean 2.975173 0.171542 0.408345 0.102262 \n",
"std 0.378897 0.160393 0.330119 0.151439 \n",
"min 2.055689 0.000000 0.000000 0.000000 \n",
"25% 2.721740 0.043827 0.099719 0.000000 \n",
"50% 3.016437 0.116049 0.334270 0.002615 \n",
"75% 3.234711 0.257407 0.745787 0.173244 \n",
"max 4.000000 0.998148 0.967697 0.660135 \n",
"\n",
" BE_HYDRO BE_BIOMASS BE_NUCLEAR BE_OTHER_GEN AT_ND \\\n",
"count 8760.000000 8760.000000 8760.000000 8760.000000 8760.000000 \n",
"mean 0.081661 0.462982 0.478261 0.498569 2.985608 \n",
"std 0.067165 0.117066 0.125715 0.045038 0.474555 \n",
"min 0.000000 0.060649 0.236078 0.255435 1.868206 \n",
"25% 0.017391 0.444288 0.412420 0.470109 2.654007 \n",
"50% 0.069565 0.469676 0.507256 0.502717 3.034726 \n",
"75% 0.147826 0.554302 0.579312 0.532609 3.318725 \n",
"max 0.208696 0.630465 0.822140 0.614130 4.000000 \n",
"\n",
" AT_WIND_ONSHORE AT_SOLAR AT_HYDRO AT_BIOMASS AT_INTERCONN \n",
"count 8760.000000 8760.000000 8760.000000 8760.000000 8760.000000 \n",
"mean 0.253861 0.159438 0.398663 0.644243 0.237638 \n",
"std 0.143749 0.224376 0.133731 0.043179 0.247113 \n",
"min 0.003713 0.000000 0.165434 0.521552 -0.697967 \n",
"25% 0.142456 0.000000 0.299161 0.620690 0.074954 \n",
"50% 0.235946 0.017462 0.364130 0.655172 0.242514 \n",
"75% 0.345979 0.281090 0.491588 0.672414 0.409242 \n",
"max 0.841730 0.867547 0.791855 0.741379 1.000000 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# merge BE and AT data for clustering\n",
"dfs=[df_BE, df_AT]\n",
"df=prep.merge(dfs)\n",
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# train kmeans model \n",
"labels, centres=kmeans.clustering(df) # using default 20 clusters"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" vertical-align: middle;\n",
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" }\n",
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" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>daytype</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2018-01-01</th>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-02</th>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-03</th>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-04</th>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-05</th>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-12-27</th>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-12-28</th>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-12-29</th>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-12-30</th>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-12-31</th>\n",
" <td>17</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>365 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" daytype\n",
"2018-01-01 16\n",
"2018-01-02 17\n",
"2018-01-03 19\n",
"2018-01-04 19\n",
"2018-01-05 19\n",
"... ...\n",
"2018-12-27 17\n",
"2018-12-28 17\n",
"2018-12-29 17\n",
"2018-12-30 17\n",
"2018-12-31 17\n",
"\n",
"[365 rows x 1 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# output daily day type table\n",
"df_labels=kmeans.df_daily_label(labels, 2018)\n",
"df_labels"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>BE_ND</th>\n",
" <th>BE_WIND_ONSHORE</th>\n",
" <th>BE_WIND_OFFSHORE</th>\n",
" <th>BE_SOLAR</th>\n",
" <th>BE_HYDRO</th>\n",
" <th>BE_BIOMASS</th>\n",
" <th>BE_NUCLEAR</th>\n",
" <th>BE_OTHER_GEN</th>\n",
" <th>AT_ND</th>\n",
" <th>AT_WIND_ONSHORE</th>\n",
" <th>AT_SOLAR</th>\n",
" <th>AT_HYDRO</th>\n",
" <th>AT_BIOMASS</th>\n",
" <th>AT_INTERCONN</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Time</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2.838218</td>\n",
" <td>0.182099</td>\n",
" <td>0.794242</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.160870</td>\n",
" <td>0.227786</td>\n",
" <td>0.508016</td>\n",
" <td>0.551630</td>\n",
" <td>2.546124</td>\n",
" <td>0.109984</td>\n",
" <td>0.0</td>\n",
" <td>0.228238</td>\n",
" <td>0.659483</td>\n",
" <td>0.568392</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2.616419</td>\n",
" <td>0.185494</td>\n",
" <td>0.798455</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.160870</td>\n",
" <td>0.228491</td>\n",
" <td>0.506244</td>\n",
" <td>0.555707</td>\n",
" <td>2.410350</td>\n",
" <td>0.104818</td>\n",
" <td>0.0</td>\n",
" <td>0.227246</td>\n",
" <td>0.659483</td>\n",
" <td>0.629205</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2.469548</td>\n",
" <td>0.183951</td>\n",
" <td>0.761236</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.160870</td>\n",
" <td>0.224260</td>\n",
" <td>0.509450</td>\n",
" <td>0.551630</td>\n",
" <td>2.315056</td>\n",
" <td>0.098545</td>\n",
" <td>0.0</td>\n",
" <td>0.224202</td>\n",
" <td>0.659483</td>\n",
" <td>0.635860</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2.350503</td>\n",
" <td>0.201852</td>\n",
" <td>0.556180</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.160870</td>\n",
" <td>0.226375</td>\n",
" <td>0.510715</td>\n",
" <td>0.552989</td>\n",
" <td>2.206895</td>\n",
" <td>0.095795</td>\n",
" <td>0.0</td>\n",
" <td>0.225893</td>\n",
" <td>0.658405</td>\n",
" <td>0.587246</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2.281765</td>\n",
" <td>0.187963</td>\n",
" <td>0.509129</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.160870</td>\n",
" <td>0.224965</td>\n",
" <td>0.510547</td>\n",
" <td>0.554348</td>\n",
" <td>2.190770</td>\n",
" <td>0.103490</td>\n",
" <td>0.0</td>\n",
" <td>0.226073</td>\n",
" <td>0.657328</td>\n",
" <td>0.544362</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <tr>\n",
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" <td>3.163095</td>\n",
" <td>0.325679</td>\n",
" <td>0.780337</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.129565</td>\n",
" <td>0.392525</td>\n",
" <td>0.707239</td>\n",
" <td>0.538859</td>\n",
" <td>3.217167</td>\n",
" <td>0.332767</td>\n",
" <td>0.0</td>\n",
" <td>0.224811</td>\n",
" <td>0.664871</td>\n",
" <td>0.372569</td>\n",
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" <tr>\n",
" <th>8757</th>\n",
" <td>2.987369</td>\n",
" <td>0.320926</td>\n",
" <td>0.799157</td>\n",
" <td>-3.469447e-18</td>\n",
" <td>0.126087</td>\n",
" <td>0.381946</td>\n",
" <td>0.708826</td>\n",
" <td>0.539674</td>\n",
" <td>3.037690</td>\n",
" <td>0.332606</td>\n",
" <td>0.0</td>\n",
" <td>0.213553</td>\n",
" <td>0.666164</td>\n",
" <td>0.371608</td>\n",
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" <tr>\n",
" <th>8758</th>\n",
" <td>2.890612</td>\n",
" <td>0.300185</td>\n",
" <td>0.788904</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.122609</td>\n",
" <td>0.368406</td>\n",
" <td>0.709467</td>\n",
" <td>0.540489</td>\n",
" <td>2.865606</td>\n",
" <td>0.331732</td>\n",
" <td>0.0</td>\n",
" <td>0.204794</td>\n",
" <td>0.667241</td>\n",
" <td>0.352717</td>\n",
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" <tr>\n",
" <th>8759</th>\n",
" <td>2.998012</td>\n",
" <td>0.296543</td>\n",
" <td>0.744803</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.123478</td>\n",
" <td>0.359661</td>\n",
" <td>0.709838</td>\n",
" <td>0.541033</td>\n",
" <td>2.918659</td>\n",
" <td>0.329311</td>\n",
" <td>0.0</td>\n",
" <td>0.198548</td>\n",
" <td>0.667672</td>\n",
" <td>0.390684</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8760</th>\n",
" <td>2.983878</td>\n",
" <td>0.257654</td>\n",
" <td>0.736517</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.125217</td>\n",
" <td>0.348096</td>\n",
" <td>0.705451</td>\n",
" <td>0.537500</td>\n",
" <td>2.724385</td>\n",
" <td>0.324402</td>\n",
" <td>0.0</td>\n",
" <td>0.196496</td>\n",
" <td>0.667241</td>\n",
" <td>0.453457</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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"</div>"
],
"text/plain": [
" BE_ND BE_WIND_ONSHORE BE_WIND_OFFSHORE BE_SOLAR BE_HYDRO \\\n",
"Time \n",
"1 2.838218 0.182099 0.794242 0.000000e+00 0.160870 \n",
"2 2.616419 0.185494 0.798455 0.000000e+00 0.160870 \n",
"3 2.469548 0.183951 0.761236 0.000000e+00 0.160870 \n",
"4 2.350503 0.201852 0.556180 0.000000e+00 0.160870 \n",
"5 2.281765 0.187963 0.509129 0.000000e+00 0.160870 \n",
"... ... ... ... ... ... \n",
"8756 3.163095 0.325679 0.780337 0.000000e+00 0.129565 \n",
"8757 2.987369 0.320926 0.799157 -3.469447e-18 0.126087 \n",
"8758 2.890612 0.300185 0.788904 0.000000e+00 0.122609 \n",
"8759 2.998012 0.296543 0.744803 0.000000e+00 0.123478 \n",
"8760 2.983878 0.257654 0.736517 0.000000e+00 0.125217 \n",
"\n",
" BE_BIOMASS BE_NUCLEAR BE_OTHER_GEN AT_ND AT_WIND_ONSHORE \\\n",
"Time \n",
"1 0.227786 0.508016 0.551630 2.546124 0.109984 \n",
"2 0.228491 0.506244 0.555707 2.410350 0.104818 \n",
"3 0.224260 0.509450 0.551630 2.315056 0.098545 \n",
"4 0.226375 0.510715 0.552989 2.206895 0.095795 \n",
"5 0.224965 0.510547 0.554348 2.190770 0.103490 \n",
"... ... ... ... ... ... \n",
"8756 0.392525 0.707239 0.538859 3.217167 0.332767 \n",
"8757 0.381946 0.708826 0.539674 3.037690 0.332606 \n",
"8758 0.368406 0.709467 0.540489 2.865606 0.331732 \n",
"8759 0.359661 0.709838 0.541033 2.918659 0.329311 \n",
"8760 0.348096 0.705451 0.537500 2.724385 0.324402 \n",
"\n",
" AT_SOLAR AT_HYDRO AT_BIOMASS AT_INTERCONN \n",
"Time \n",
"1 0.0 0.228238 0.659483 0.568392 \n",
"2 0.0 0.227246 0.659483 0.629205 \n",
"3 0.0 0.224202 0.659483 0.635860 \n",
"4 0.0 0.225893 0.658405 0.587246 \n",
"5 0.0 0.226073 0.657328 0.544362 \n",
"... ... ... ... ... \n",
"8756 0.0 0.224811 0.664871 0.372569 \n",
"8757 0.0 0.213553 0.666164 0.371608 \n",
"8758 0.0 0.204794 0.667241 0.352717 \n",
"8759 0.0 0.198548 0.667672 0.390684 \n",
"8760 0.0 0.196496 0.667241 0.453457 \n",
"\n",
"[8760 rows x 14 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# reconstruct the data table with cluster centres\n",
"df_reduced=kmeans.df_centres(df, labels, centres)\n",
"df_reduced"
]
},
Added init.py file. Added csv output lines in main.
2021-01-17 01:29:06 +00:00
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# output files\n",
"# df_labels.to_csv('daytype.csv')\n",
"# df_reduced.to_csv('reduced_data.csv')"
]
},
Example k-means reduction for AT and BE input data.
2021-01-11 22:12:55 +00:00
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# inspect output graphically\n",
"def plot_compare(col_name):\n",
" plt.figure(figsize=(20,8)) \n",
" plt.plot(df[col_name], label='data')\n",
" plt.plot(df_reduced[col_name], label='kmeans centre')\n",
" plt.title(col_name.replace('_', ' '), size=16)\n",
" plt.xticks(size=16) # x-axis font size\n",
" plt.yticks(size=16) # y-axis font size\n",
" plt.xlabel('time step', size=16) # x-axis label\n",
" plt.ylabel('factor', size=16) # y-axis label\n",
" plt.legend(prop={'size':16})\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1440x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_compare('BE_ND')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABJ0AAAH8CAYAAAB7IN4fAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy86wFpkAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOydd7gkR3X235q5d++CQJIJNgYMAgMmmGgwORlsMMYYbAyfJTKYDCYJBAgQQiAQAoEkhAJCEeWEslZhg1Zxg7S7Wmnz3s159+Y43fX90dNdp6bPmeqZOzfu+T2PdHu7q6uqe7qrq94655Sx1kJRFEVRFEVRFEVRFEVRWklpsiugKIqiKIqiKIqiKIqizDxUdFIURVEURVEURVEURVFajopOiqIoiqIoiqIoiqIoSstR0UlRFEVRFEVRFEVRFEVpOSo6KYqiKIqiKIqiKIqiKC1HRSdFURRFURRFURRFURSl5ajopCiKoiiKMkaMMX8wxlhjzK/JvrdV94X+O79OvudX0yxgjr2zeuxtZN9xNXn3GmPWGmMuMca8q8WXrSiKoiiKUpe2ya6AoiiKoijKdMYY8zgA/13951HGmG9baysAlgJ4PUn61wCuAXAigOvJ/j0FinmzMebd1tpbC1brTQAiAI8H8BwAHwRwqzHmYgAft9bGBfNRFEVRFEVpGhWdFEVRFEVRxsYHABwK4GYA7wHwbgA3Wmt7ANyfJjLGHFHd3GCtvb82kzrsALAXwAnGmNustbbAOQ9Uha+Uc40xXwfwawAPA/hVA+UriqIoiqI0hbrXKYqiKIqijI2PAzgA4BMABgF8rMX5xwB+AOAfAPxns5lYa08B8BCAr7WmWoqiKIqiKPVR0UlRFEVRFKVJjDFPB/BOAJdba/cAuA7A+4wxf9HKcqy1fwbwAIDjjTFj6b/dAuCZxphntaZmiqIoiqIoMio6KYqiKIqiNM9HkfSnLqz++wIAHQA+PA5lfR/AiwF8ZAx5bK7+/euxV0dRFEVRFKU+KjopiqIoiqI0z8cArLXW3lf99x0AtqP1Lnaw1t4J4C4Axxlj2pvMxqTZtaZWiqIoiqIoMio6KYqiKIqiNIEx5jVILI+uMcYcbow5HMATkaxQ93pjzAvGodjvIVmN7jNNnv831b87WlMdRVEURVEUGRWdFEVRFEVRmuPj1b/fQRJIPP3vy9X942Ht9ACA6wEcC+BxTWTxHgCbrbVbWloxRVEURVEUBhWdFEVRFEVRGsQYMwvA/0MS3PvtzH8PA/ioMcZIeYyBYwE8DcCXGjnJGPN1AK8A8OtxqJOiKIqiKEqOtsmugKIoiqIoyjTkvQCeDOCb1tp5tQeNMWcB+D2AtwGY28qCrbUrjDGXATiyTrLXGmMiALMBPBfABwH8K5JA56e2sj6KoiiKoigSaumkKIqiKIrSOB8H0AvgSuH4pQAG4VzwWs0PAVTqHF8I4D4ANyGJA9UF4N3W2k9YazWIuKIoiqIoE4LRfoeiKIqiKIqiKIqiKIrSatTSSVEURVEURVEURVEURWk5KjopiqIoiqIoiqIoiqIoLUdFJ0VRFEVRFEVRFEVRFKXlqOikKIqiKIqiKIqiKIqitBwVnRRFURRFURRFURRFUZSW0zbZFZhInvKUp9gjjjhisquhKIqiKIqiKIqiKIoyY1iyZMlea+1Ta/cfVKLTEUccgcWLF092NRRFURRFURRFURRFUWYMxphN3H51r1MURVEURVEURVEURVFajopOiqIoiqIoiqIoiqIoSstR0UlRFEVRFEVRFEVRFEVpOSo6KYqiKIqiKIqiKIqiKC1HRSdFURRFURRFURRFURSl5ajopCiKoiiKoiiKoiiKorQcFZ0URVEURVEURVEURVGUlqOik6IoiqIoiqIoiqIoitJyVHRSFEVRFEVRFEVRFEVRWo6KToqiKIqiKIqiKIqiKErLUdFJURRFURRFURRFURRFaTkqOimKoiiKoiiKoiiKoigtZ9JFJ2PMrcYYa4w5oUDa2caYXxpjdhhjBo0x9xlj3jIR9VQURVEURVEURVEURVGKM6mikzHmfwC8vIFTzgXwvwB+COC9AHYAuM0Y84rW105RFEVRFEVRFEVRFEVplkkTnYwxhwM4BcA3CqZ/OYAjAXzdWnuOtfZOAB8CsBnA8eNVT0VRFEVRFEVRFEVRFKVxJtPS6SQAK621lxZM/z4AowAuT3dYaysALgPwLmNMR+urqCiKoiiKoiiKoiiKojRD22QUaox5E4CPoTHXupcA2GitHajZvxLALADPq24riqIoiqIoiqIoiqIok8yEWzoZY9oBnAXgZGvt6gZOfRKAA8z+/eS4cpBzxDE34Wc3PzbZ1VAURVEURVEURVGUg57JcK/7DoDHAfhpg+cZAFbYL59kzGeNMYuNMYv37NnTYJHKdOTsBRsmuwqKoiiKoiiKoihKC7DWYnAkmuxqKE0yoaKTMeZZAL4P4AcAOowxh1cDioP8uyycvh+8NdNfkOM5rLVnW2tfba199VOf+tQx1F5RFEVRFEVRFEVRlInkd3PX4UU/vBX7+0cmuypKE0y0pdNzAcwGcDESV7n0PwD4VnX7pcK5KwE8xxjz+Jr9LwYwAmBdy2urKIqiKIqiKIqiKMqkcd3D2wEAe/uG2eODIxGe//2bcfOKHRNZLaUgEy06PQzg7cx/QCJEvR2yeHQ9gHYA/53uMMa0AfgwgDnWWv4JVBRFURRFURRFURRlWiPF1dnWNYjRyOLk2xoJGa1MFBO6ep21tgvAvNr9xhgA2GStnVf997MBrAdwvLX2+Oq5DxtjLgfwm2ow8o0AvgDgOQCOmoDqK5PMqp09+OR5i3DTV9+MJx0ya7KroyiKoiiKoiiKoihKHSYjkHgRDIAy8vX7JIDzAJwA4CYAfwPg3dbapRNbPWUy+P289djRPYT5a3Z7+5dt6cIj27pz6Xf3Dk1U1RRFURRFURRFUZRxwFpuPTFg9c5e7OgeRLbeWN0lxpTJYkItnSSstabm351gHhlr7SCAb1T/Uw4ySolFHGrbnP/43T0AgI0nvifbt2TTfvzX7+/Dbz78Crz/lc+YsDoqiqIoiqIoiqIorcfUKATv+s0CAMAd33hLcnyiK6QUYqpaOilKjrQRiXmh22PVzl4AwAMb2UUNFUVRFEVRFEVRlClK9+Ao/vvMe7Fl/wBCwz/BEMpj/po9uGvVLgDAr29fg0WdOk6cKFR0UhqmZ2gUw5Vowss1maUT36rQ3QYu7Uglxrrdfbn0Ww8MoGtAl91UFEVRFEVRFEWZSty8YgcWdR7A7+bSdcZ4W6Z0GGhqTaEIH//jg/jU+YsBAKfeuRb/feZ9LaqpEkJFJ6VhXnbcHHzorPtbklf34Cjmrt4dTghnTikJ2XR/qZo2thY/un4l3vnr+bkYT2/6xVy86RdzG6uwoiiKoiiKoiiKMnEUsGQC1L1uqqKik9IUy7Z0tSSfL/1pKT553iLs6R0Ops0aEaHRoRZQparqFFvgwY37AAA9g6O5c/qGKw3VV1EURVEURVEURRlfOOcWyZCpiHudMnmo6KSMK6t39mLDnrxrW0p6rIi7niHWSxzXL9uebadBx2NryXahKiuKoiiKoiiKoig1rNjajW5mIj9l7a5e3L8hmfAfrkSoRPGYyzTGtznoH65gX59vsGCrKep41ymTiIpOSiE27evHo9t7vH2DIxH+43f3YMXWbvG8d/1mAf7pV/PF40ZYkY5juJI0WlLSb1yxLNtO3eus9QWoRujc249rH9ra0DmKoiiKoiiKoigzkX8/fSE+du4D4vF/PmUB/t/ZSRiWvzv2Vnz47OZDslhm1GcA/MspC/APJ9zBnmPUwW5KoqKTUoi3/nIe3nPq3d6+ZVu7sGxLF35y46MN5xdSvYdGfWV8W9cg/vxwYskUW4tKFKO/jmvcos4DWdrMQooU+bObHwvW8b2nLcTXL18WTKcoiqIoiqIoijKTSUOZLKtjcFDLkk0HWlCy8cKobOsabEGeykSiopPSNKmOzKnQ9Vi+tQvP+/4tWLBmD0rVJ7DWCOmFP/CV8e01jcuXL3kIL/nRbWI
"text/plain": [
"<Figure size 1440x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_compare('AT_ND')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1440x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_compare('BE_WIND_OFFSHORE')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1440x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_compare('AT_WIND_ONSHORE')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}