pypsa-eur/data/heat_load_profile.csv
Tom Brown d3a0f7e67d Diff intraday heat profiles for (res, services) x (water, space)
These are specified in data/heat_load_profile.csv.

The resulting heat_demand df has MultiIndex columns, where the first
level is ["sector use"], and the second level level is nodes.
2019-08-01 15:43:04 +02:00

1.7 KiB

1hourresidential space weekdayresidential space weekendresidential water weekdayresidential water weekendservices space weekdayservices space weekendservices water weekdayservices water weekend
200.91814386890.9421512708110.91814386890.942151270811
310.91723590710.9400891069110.91723590710.940089106911
420.92694644810.9461062015110.92694644810.946106201511
530.94150479320.9535084941110.94150479320.953508494111
640.96562995070.9651094993110.96562995070.965109499311
751.02211664430.9834676747111.02211664430.983467674711
861.15530904931.0124171051111.15530904931.012417105111
971.20934110311.0446615927111.20934110311.044661592711
1081.14702959421.088203419111.14702959421.08820341911
1191.08771913411.1110334576111.08771913411.111033457611
12101.04183273721.0926752822111.04183273721.092675282211
13111.00629771331.055488209111.00629771331.05548820911
14120.98370303591.0251266112110.98370303591.025126611211
15130.96675702780.9990015154110.96675702780.999001515411
16140.95483209320.9782897278110.95483209320.978289727811
17150.95092320610.9698167237110.95092320610.969816723711
18160.96369733190.974288587110.96369733190.97428858711
19170.97993725630.9886456216110.97993725630.988645621611
20181.00465018481.0084159643111.00465018481.008415964311
21191.00794524191.0171243296111.00794524191.017124329611
22200.98605664810.9994722379110.98605664810.999472237911
23210.97052280740.982761591110.97052280740.98276159111
24220.95864858190.9698167237110.95864858190.969816723711
25230.93350237780.9515079292110.93350237780.951507929211