| region | \nGrand Est | \nNouvelle-Aquitaine | \nAuvergne-Rhône-Alpes | \nBourgogne-Franche-Comté | \nBretagne | \nCentre-Val de Loire | \nIle-de-France | \nOccitanie | \nHauts-de-France | \nNormandie | \nPays-de-la-Loire | \nPACA | \n
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| time | \n\n | \n | \n | \n | \n | \n | \n | \n | \n | \n | \n | \n |
| 2014-01-31 | \n4.471033 | \n4.348247 | \n3.385047 | \n4.331848 | \n6.541332 | \n5.216271 | \n5.445381 | \n3.802262 | \n6.250730 | \n6.249713 | \n5.351345 | \n3.473030 | \n
| 2014-02-28 | \n5.228498 | \n5.488293 | \n3.969629 | \n5.294989 | \n8.104619 | \n6.537807 | \n6.491930 | \n4.266872 | \n7.130648 | \n7.480851 | \n6.919350 | \n3.753783 | \n
| 2014-03-31 | \n3.247862 | \n3.737462 | \n3.169869 | \n3.308689 | \n4.774360 | \n3.765674 | \n3.785264 | \n4.118966 | \n4.192514 | \n4.400593 | \n3.966221 | \n3.566239 | \n
| 2014-04-30 | \n2.989822 | \n3.612039 | \n3.154355 | \n3.235733 | \n4.781591 | \n3.760678 | \n3.418167 | \n3.707929 | \n3.758075 | \n3.925559 | \n4.133669 | \n3.584715 | \n
| 2014-05-31 | \n3.916245 | \n4.029611 | \n3.958974 | \n4.327494 | \n4.873780 | \n4.599434 | \n4.381525 | \n4.121242 | \n4.590732 | \n4.870740 | \n4.622843 | \n4.156127 | \n
| ... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n
| 2019-08-31 | \n2.935242 | \n2.852736 | \n2.620896 | \n2.927428 | \n4.422352 | \n3.554445 | \n3.471418 | \n2.702522 | \n3.772703 | \n3.950350 | \n3.826699 | \n2.967331 | \n
| 2019-09-30 | \n3.735931 | \n3.661603 | \n3.091064 | \n3.888781 | \n5.657197 | \n4.545930 | \n4.511178 | \n3.177065 | \n4.779937 | \n5.092583 | \n4.978077 | \n3.624991 | \n
| 2019-10-31 | \n4.280705 | \n3.892113 | \n3.436332 | \n4.104208 | \n5.638809 | \n4.868557 | \n5.029251 | \n3.638608 | \n5.176669 | \n5.388301 | \n4.849962 | \n3.820495 | \n
| 2019-11-30 | \n4.154172 | \n4.431273 | \n3.319221 | \n4.301270 | \n6.275538 | \n4.976124 | \n4.973913 | \n3.828136 | \n5.029555 | \n5.258453 | \n5.158895 | \n3.827157 | \n
| 2019-12-31 | \n4.999985 | \n4.845899 | \n3.954124 | \n5.183798 | \n6.605115 | \n5.683018 | \n5.680073 | \n4.216725 | \n6.004361 | \n6.232695 | \n5.720450 | \n4.275733 | \n
72 rows × 12 columns
\n| \n | region | \nwind_speed | \n
|---|---|---|
| 0 | \nGrand Est | \n4.471033 | \n
| 1 | \nNormandie | \n6.249713 | \n
| 2 | \nHauts-de-France | \n6.250730 | \n
| 3 | \nOccitanie | \n3.802262 | \n
| 4 | \nIle-de-France | \n5.445381 | \n
| ... | \n... | \n... | \n
| 859 | \nAuvergne-Rhône-Alpes | \n3.954124 | \n
| 860 | \nNouvelle-Aquitaine | \n4.845899 | \n
| 861 | \nGrand Est | \n4.999985 | \n
| 862 | \nPays-de-la-Loire | \n5.720450 | \n
| 863 | \nPACA | \n4.275733 | \n
864 rows × 2 columns
\n| \n | region | \nwind_speed | \n
|---|---|---|
| 0 | \n4 | \n4.471033 | \n
| 1 | \n7 | \n6.249713 | \n
| 2 | \n5 | \n6.250730 | \n
| 3 | \n9 | \n3.802262 | \n
| 4 | \n6 | \n5.445381 | \n
| ... | \n... | \n... | \n
| 859 | \n0 | \n3.954124 | \n
| 860 | \n8 | \n4.845899 | \n
| 861 | \n4 | \n4.999985 | \n
| 862 | \n11 | \n5.720450 | \n
| 863 | \n10 | \n4.275733 | \n
864 rows × 2 columns
\n| \n | time | \nregion | \ncapacity factor | \n
|---|---|---|---|
| 0 | \n2014-01-01 | \nAuvergne-Rhône-Alpes | \n0.272 | \n
| 1 | \n2014-01-01 | \nOccitanie | \n0.290 | \n
| 2 | \n2014-01-01 | \nNouvelle-Aquitaine | \n0.345 | \n
| 3 | \n2014-01-01 | \nNormandie | \n0.446 | \n
| 4 | \n2014-01-01 | \nÃŽle-de-France | \n0.397 | \n
| ... | \n... | \n... | \n... | \n
| 859 | \n2019-12-01 | \nBretagne | \n0.352 | \n
| 860 | \n2019-12-01 | \nBourgogne-Franche-Comté | \n0.435 | \n
| 861 | \n2019-12-01 | \nAuvergne-Rhône-Alpes | \n0.334 | \n
| 862 | \n2019-12-01 | \nPays de la Loire | \n0.366 | \n
| 863 | \n2019-12-01 | \nProvence-Alpes-Côte d'Azur | \n0.258 | \n
864 rows × 3 columns
\n| \n | region | \nwind_speed | \ndensity | \n
|---|---|---|---|
| 0 | \n4 | \n4.471033 | \n1.224567 | \n
| 1 | \n7 | \n6.249713 | \n1.230758 | \n
| 2 | \n5 | \n6.250730 | \n1.242508 | \n
| 3 | \n9 | \n3.802262 | \n1.184342 | \n
| 4 | \n6 | \n5.445381 | \n1.239542 | \n
| ... | \n... | \n... | \n... | \n
| 859 | \n0 | \n3.954124 | \n1.166310 | \n
| 860 | \n8 | \n4.845899 | \n1.223121 | \n
| 861 | \n4 | \n4.999985 | \n1.228415 | \n
| 862 | \n11 | \n5.720450 | \n1.245126 | \n
| 863 | \n10 | \n4.275733 | \n1.152507 | \n
864 rows × 3 columns
\n| \n | region | \nwind_speed | \ndensity | \ntemperature | \nheight_500 | \nwind_var | \nwind_std | \nwind_max | \nwind_min | \nwind_cube | \n
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n4 | \n4.471033 | \n1.224567 | \n276.738556 | \n5457.524902 | \n3.747965 | \n1.913444 | \n10.805568 | \n0.342017 | \n152.853821 | \n
| 1 | \n7 | \n6.249713 | \n1.230758 | \n278.975800 | \n5426.675781 | \n8.094595 | \n2.837001 | \n14.423610 | \n0.617211 | \n407.056580 | \n
| 2 | \n5 | \n6.250730 | \n1.242508 | \n278.103119 | \n5418.401855 | \n6.213293 | \n2.482862 | \n13.240123 | \n0.720422 | \n371.695801 | \n
| 3 | \n9 | \n3.802262 | \n1.184342 | \n279.471191 | \n5526.241699 | \n3.707209 | \n1.891394 | \n9.724280 | \n0.187934 | \n108.221443 | \n
| 4 | \n6 | \n5.445381 | \n1.239542 | \n278.326813 | \n5441.877441 | \n5.375361 | \n2.317015 | \n12.843593 | \n0.713326 | \n254.245667 | \n
| ... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n
| 859 | \n0 | \n3.954124 | \n1.166310 | \n277.858948 | \n5600.200195 | \n6.046566 | \n2.375380 | \n12.635825 | \n0.151835 | \n168.917801 | \n
| 860 | \n8 | \n4.845899 | \n1.223121 | \n281.079712 | \n5615.462891 | \n7.439332 | \n2.693640 | \n14.047593 | \n0.234808 | \n258.852295 | \n
| 861 | \n4 | \n4.999985 | \n1.228415 | \n277.263885 | \n5552.700195 | \n6.393699 | \n2.509099 | \n13.066129 | \n0.591719 | \n243.603943 | \n
| 862 | \n11 | \n5.720450 | \n1.245126 | \n280.306000 | \n5577.145020 | \n8.350408 | \n2.884501 | \n14.306827 | \n0.633577 | \n346.943176 | \n
| 863 | \n10 | \n4.275733 | \n1.152507 | \n279.716278 | \n5615.496582 | \n6.831639 | \n2.565415 | \n12.461452 | \n0.136556 | \n204.244476 | \n
864 rows × 10 columns
\n| \n | wind_speed | \ndensity | \ntemperature | \nheight_500 | \nwind_var | \nwind_std | \nwind_max | \nwind_min | \nwind_cube | \n
|---|---|---|---|---|---|---|---|---|---|
| 0 | \n4.471033 | \n1.224567 | \n276.738556 | \n5457.524902 | \n3.747965 | \n1.913444 | \n10.805568 | \n0.342017 | \n152.853821 | \n
| 1 | \n6.249713 | \n1.230758 | \n278.975800 | \n5426.675781 | \n8.094595 | \n2.837001 | \n14.423610 | \n0.617211 | \n407.056580 | \n
| 2 | \n6.250730 | \n1.242508 | \n278.103119 | \n5418.401855 | \n6.213293 | \n2.482862 | \n13.240123 | \n0.720422 | \n371.695801 | \n
| 3 | \n3.802262 | \n1.184342 | \n279.471191 | \n5526.241699 | \n3.707209 | \n1.891394 | \n9.724280 | \n0.187934 | \n108.221443 | \n
| 4 | \n5.445381 | \n1.239542 | \n278.326813 | \n5441.877441 | \n5.375361 | \n2.317015 | \n12.843593 | \n0.713326 | \n254.245667 | \n
| ... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n
| 859 | \n3.954124 | \n1.166310 | \n277.858948 | \n5600.200195 | \n6.046566 | \n2.375380 | \n12.635825 | \n0.151835 | \n168.917801 | \n
| 860 | \n4.845899 | \n1.223121 | \n281.079712 | \n5615.462891 | \n7.439332 | \n2.693640 | \n14.047593 | \n0.234808 | \n258.852295 | \n
| 861 | \n4.999985 | \n1.228415 | \n277.263885 | \n5552.700195 | \n6.393699 | \n2.509099 | \n13.066129 | \n0.591719 | \n243.603943 | \n
| 862 | \n5.720450 | \n1.245126 | \n280.306000 | \n5577.145020 | \n8.350408 | \n2.884501 | \n14.306827 | \n0.633577 | \n346.943176 | \n
| 863 | \n4.275733 | \n1.152507 | \n279.716278 | \n5615.496582 | \n6.831639 | \n2.565415 | \n12.461452 | \n0.136556 | \n204.244476 | \n
864 rows × 9 columns
\n| \n | wind_speed | \ndensity | \ntemperature | \nheight_500 | \nwind_var | \nwind_std | \nwind_max | \nwind_min | \nwind_cube | \nregion | \n
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n0.429739 | \n0.627725 | \n-1.358417 | \n-1.743196 | \n-0.259707 | \n-0.169293 | \n-0.069636 | \n0.279980 | \n0.043185 | \n4 | \n
| 1 | \n2.412611 | \n0.778494 | \n-0.975160 | \n-2.021829 | \n1.932967 | \n1.846133 | \n1.428249 | \n1.708413 | \n2.570744 | \n7 | \n
| 2 | \n2.413744 | \n1.064627 | \n-1.124657 | \n-2.096560 | \n0.983937 | \n1.073316 | \n0.938281 | \n2.244146 | \n2.219149 | \n5 | \n
| 3 | \n-0.315805 | \n-0.351805 | \n-0.890296 | \n-1.122536 | \n-0.280266 | \n-0.217412 | \n-0.517294 | \n-0.519816 | \n-0.400599 | \n9 | \n
| 4 | \n1.515942 | \n0.992391 | \n-1.086336 | \n-1.884526 | \n0.561239 | \n0.711396 | \n0.774115 | \n2.207311 | \n1.051332 | \n6 | \n
| ... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n
| 859 | \n-0.146509 | \n-0.790908 | \n-1.166485 | \n-0.454533 | \n0.899831 | \n0.838763 | \n0.688099 | \n-0.707191 | \n0.202910 | \n0 | \n
| 860 | \n0.847640 | \n0.592521 | \n-0.614744 | \n-0.316678 | \n1.602417 | \n1.533285 | \n1.272577 | \n-0.276506 | \n1.097137 | \n8 | \n
| 861 | \n1.019415 | \n0.721443 | \n-1.268424 | \n-0.883559 | \n1.074944 | \n1.130572 | \n0.866246 | \n1.576094 | \n0.945521 | \n4 | \n
| 862 | \n1.822588 | \n1.128384 | \n-0.747287 | \n-0.662770 | \n2.062013 | \n1.949790 | \n1.379901 | \n1.793366 | \n1.973032 | \n11 | \n
| 863 | \n0.212019 | \n-1.127043 | \n-0.848310 | \n-0.316374 | \n1.295864 | \n1.253467 | \n0.615908 | \n-0.786497 | \n0.554166 | \n10 | \n
864 rows × 10 columns
\n| \n | wind_speed | \ndensity | \ntemperature | \nheight_500 | \nwind_var | \nwind_std | \nwind_max | \nwind_min | \nwind_cube | \nregion | \n
|---|---|---|---|---|---|---|---|---|---|---|
| 651 | \n-1.581818 | \n-1.267182 | \n1.729689 | \n1.290919 | \n-1.499519 | \n-1.878235 | \n-1.938274 | \n-0.804362 | \n-1.173466 | \n1 | \n
| 352 | \n0.088491 | \n0.239687 | \n0.675657 | \n0.467063 | \n-0.560433 | \n-0.494220 | \n-0.389259 | \n-0.098570 | \n-0.321708 | \n2 | \n
| 606 | \n0.913040 | \n0.829751 | \n-1.025390 | \n-2.256506 | \n-0.006482 | \n0.153396 | \n0.452401 | \n0.326311 | \n0.377709 | \n6 | \n
| 7 | \n2.737707 | \n0.744219 | \n-0.612203 | \n-1.983588 | \n2.680415 | \n2.395537 | \n2.155956 | \n0.990991 | \n3.468702 | \n2 | \n
| 445 | \n0.382906 | \n0.655776 | \n-0.638693 | \n-0.657160 | \n1.651468 | \n1.549974 | \n1.686950 | \n-0.116935 | \n0.772351 | \n8 | \n
| ... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n... | \n
| 106 | \n-0.981122 | \n0.028810 | \n1.124738 | \n1.053945 | \n-1.230957 | \n-1.409201 | \n-1.828326 | \n-0.452998 | \n-0.967671 | \n11 | \n
| 270 | \n-0.506813 | \n-1.266701 | \n-0.336045 | \n0.584353 | \n0.834096 | \n0.775741 | \n0.564724 | \n-0.645867 | \n0.035972 | \n10 | \n
| 860 | \n0.847640 | \n0.592521 | \n-0.614744 | \n-0.316678 | \n1.602417 | \n1.533285 | \n1.272577 | \n-0.276506 | \n1.097137 | \n8 | \n
| 435 | \n0.169093 | \n2.105278 | \n-1.854199 | \n-0.919877 | \n0.231150 | \n0.385007 | \n2.027615 | \n-0.341511 | \n0.012441 | \n5 | \n
| 102 | \n-1.285968 | \n-0.589636 | \n1.316084 | \n1.112746 | \n-1.287834 | \n-1.569761 | \n-1.651358 | \n-0.347837 | \n-1.022131 | \n8 | \n
691 rows × 10 columns
\nLasso(alpha=0.0001, warm_start=True)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
Lasso(alpha=0.0001, warm_start=True)
GridSearchCV(cv=5, estimator=Ridge(),\n param_grid={'alpha': array([1.00000000e-04, 2.05061224e-02, 4.09122449e-02, 6.13183673e-02,\n 8.17244898e-02, 1.02130612e-01, 1.22536735e-01, 1.42942857e-01,\n 1.63348980e-01, 1.83755102e-01, 2.04161224e-01, 2.24567347e-01,\n 2.44973469e-01, 2.65379592e-01, 2.85785714e-01, 3.06191837e-01,\n 3.26597959e-01, 3.47004082e-01, 3.67410204e-01, 3....\n 4.89846939e-01, 5.10253061e-01, 5.30659184e-01, 5.51065306e-01,\n 5.71471429e-01, 5.91877551e-01, 6.12283673e-01, 6.32689796e-01,\n 6.53095918e-01, 6.73502041e-01, 6.93908163e-01, 7.14314286e-01,\n 7.34720408e-01, 7.55126531e-01, 7.75532653e-01, 7.95938776e-01,\n 8.16344898e-01, 8.36751020e-01, 8.57157143e-01, 8.77563265e-01,\n 8.97969388e-01, 9.18375510e-01, 9.38781633e-01, 9.59187755e-01,\n 9.79593878e-01, 1.00000000e+00])})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. GridSearchCV(cv=5, estimator=Ridge(),\n param_grid={'alpha': array([1.00000000e-04, 2.05061224e-02, 4.09122449e-02, 6.13183673e-02,\n 8.17244898e-02, 1.02130612e-01, 1.22536735e-01, 1.42942857e-01,\n 1.63348980e-01, 1.83755102e-01, 2.04161224e-01, 2.24567347e-01,\n 2.44973469e-01, 2.65379592e-01, 2.85785714e-01, 3.06191837e-01,\n 3.26597959e-01, 3.47004082e-01, 3.67410204e-01, 3....\n 4.89846939e-01, 5.10253061e-01, 5.30659184e-01, 5.51065306e-01,\n 5.71471429e-01, 5.91877551e-01, 6.12283673e-01, 6.32689796e-01,\n 6.53095918e-01, 6.73502041e-01, 6.93908163e-01, 7.14314286e-01,\n 7.34720408e-01, 7.55126531e-01, 7.75532653e-01, 7.95938776e-01,\n 8.16344898e-01, 8.36751020e-01, 8.57157143e-01, 8.77563265e-01,\n 8.97969388e-01, 9.18375510e-01, 9.38781633e-01, 9.59187755e-01,\n 9.79593878e-01, 1.00000000e+00])})Ridge()
Ridge()
GridSearchCV(cv=5, estimator=RandomForestRegressor(),\n param_grid={'criterion': ['squared_error', 'absolute_error',\n 'friedman_mse', 'poisson'],\n 'max_depth': [10, 20, 30, 40, 50],\n 'n_estimators': [10, 50, 100, 200]})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. GridSearchCV(cv=5, estimator=RandomForestRegressor(),\n param_grid={'criterion': ['squared_error', 'absolute_error',\n 'friedman_mse', 'poisson'],\n 'max_depth': [10, 20, 30, 40, 50],\n 'n_estimators': [10, 50, 100, 200]})RandomForestRegressor()
RandomForestRegressor()