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View Code? Open in Web Editor NEWThis contains the energy disaggregation code based on Graph Signal Processing approach
Home Page: https://ieeexplore.ieee.org/document/7457610/
This contains the energy disaggregation code based on Graph Signal Processing approach
Home Page: https://ieeexplore.ieee.org/document/7457610/
Hi
When I execute the gsp_disagreggator.py
file, I've got the following error on the 64 line:
LinAlgError: SVD did not converge
What can it be?
Hi,
I have a problem with compiling using Python 3.7 on PyCharm 2018.3.4 x64.
Compiling stop at step 3 of 6> generates full power series of appliances and display following error:
File "C:/Users/hacke/PycharmProjects/GSP_Disagregator/gsp_disaggregator.py", line 72, in <module> power_series, appliance_signatures = gsp.generate_appliance_powerseries(appliance_pairs, delta_p) File "C:\Users\hacke\PycharmProjects\GSP_Disagregator\gsp_support.py", line 194, in generate_appliance_powerseries powerval = interpolate_values(final) if sum(np.isnan(final)) else final TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
Thanks for response
Hi,
I am new in this area and thank you so much to upload the python code of GSP based NILM! And I really want to have a successful run of your code!
However I have some problem when running the code with the function DWT and fastDWT
" dist = (s1[i] - s2[j]) ** 2
TypeError: unsupported operand type(s) for -: 'function' and 'int'". Could you please help me with that?
I keep getting this issue when I run the gsp_disaggregator.py.
A[pd.isnull(A)] = np.interp(x, xp, fp), This is where the error is pointed it is line 314 in gsp_support.py in interpolate function.
Please guide I'm unable to resolve the error
Hello,
I am trying to get the disaggregation results with REFIT and REDD files, as the authors did on the article. You have output_disaggr.csv
and signature_database_labelled.csv
, which are two labelled disaggregated files, and REDD and REFIT data have aggregated files and dissaggregated files which are equivalent to output_disaggr.csv
(ground truth). How can I generate/obtain the equivalen signature_database_labelled.csv
to REDD and REFIT data?
File "gsp_disaggregator.py", line 71, in
power_series, appliance_signatures = gsp.generate_appliance_powerseries(appliance_pairs, delta_p)
File "/home/qzx/nilm/gsp_support.py", line 210, in generate_appliance_powerseries
power_series[i] = pd.DataFrame({'timestamp':timeseq,'power':powerseq})
File "/home/qzx/.local/lib/python3.8/site-packages/pandas/core/frame.py", line 709, in init
mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)
File "/home/qzx/.local/lib/python3.8/site-packages/pandas/core/internals/construction.py", line 481, in dict_to_mgr
return arrays_to_mgr(arrays, columns, index, dtype=dtype, typ=typ, consolidate=copy)
File "/home/qzx/.local/lib/python3.8/site-packages/pandas/core/internals/construction.py", line 115, in arrays_to_mgr
index = _extract_index(arrays)
File "/home/qzx/.local/lib/python3.8/site-packages/pandas/core/internals/construction.py", line 655, in _extract_index
raise ValueError("All arrays must be of the same length")
ValueError: All arrays must be of the same length
Could you please provide the source of the demo data?
Is it real measured data?
I got the following error while i would like to run the code as it is.
also help me in implementing the same algorithm to the public available datasets like REDD,UKDALE
1 of 6> reading data
Traceback (most recent call last):
File "gsp_disaggregator.py", line 62, in
event = [i for i in range(0, len(delta_p)) if (delta_p[i] > T_Positive or delta_p[i] < T_Negative) ]
File "gsp_disaggregator.py", line 62, in
event = [i for i in range(0, len(delta_p)) if (delta_p[i] > T_Positive or delta_p[i] < T_Negative) ]
TypeError: '>' not supported between instances of 'tuple' and 'int'
I have plotted the sum of the dis-aggregated 15 loads obtained in gsp_result data frame. The actual aggregated data is also plotted to compare the results with the actual data. There appears to be a big difference between the actual aggregate power and the result obtained using your code. Can you verify?
Hi @loneharoon,
I'd like to give your implementation a try, but there is not demo data file.
csvfile = "./demo_data.csv"
Would you include this file pls?
Thank you.
Best,
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