zhangraymond / neural-nilm Goto Github PK

Hi Zhang,
Nice job. Is there any article or website to followup and check the results of your approach?
Is this approach reliable to perform an online/real time appliance level energy disaggregation?

I'm building a three phase smart meter that sends electrical parameters via MQTT to a Raspberry pi and would like to use it.

Thanks.

Hi @ZhangRaymond,
I really tried to recreate this dict, but I could not handle nilmtk/REDD adequately I think :( I'm sorry.
Please help me. I'd really like to try your code out.

Thank you.
import pickle
from nilmtk import DataSet, TimeFrame
import pandas as pd

datafolder = '/Users/alessandro/Documents/data/'

redd = DataSet(datafolder + 'redd.h5')
metadata = dict(redd.metadata)

deviceName = set()
appName = set()
mains={}
appliances={}
for i in range(1,7):
mains[i] = {}

for house in range(1,7):
print('house_',house)
elec = redd.buildings[house].elec
appliancelist = elec.appliances
mainsvalues = elec.mains().power_series_all_data()
mains[house]=mainsvalues
for app in appliancelist:
label = app.label('unknown')
print(' '*3,label)
appName.add(label)
appliances[house][label] = elec[label].power_series(ac_type='active')
deviceName = list(appName)
with open('house_{}.pickle'.format(house), 'wb') as file:
pickle.dump([deviceName], file)
print('End')

Sorry to bother you, I encountered this problem while running your code. Do you know how to solve the problem?

hi Raymond,
Could help me to solve this problem please:

" get_modelPath() missing 1 required positional argument: 'dir_path' "