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Detect outliers in the standby consumption #148
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Can I use the scikit-learn library for it? |
Yes! I was thinking about a RANSAC-algorithm. I have implemented it before, so I might have some code snippets that could be used to start. |
This should get you started! import numpy as np
from sklearn import linear_model
x = [] # list with x-values
y = [] # list with y-values
# make sure that x and y have the same size!
model_ransac = linear_model.RANSACRegressor(linear_model.LinearRegression())
model_ransac.fit(x, y)
# use inlier_mask_ to get a boolean series of the inliers
inliers = model_ransac.inlier_mask_
outliers = np.logical_not(inliers) You can also use My expectation is that if you would apply this method to the image posted above, those two outlying red dot's would be detected pretty easily. |
@saroele JrtPec: Maybe also kick out FL03001550 from the analysis? This seriously skews the analysis/mean. I'm convinced this is not a residential building (an office?) as it has a night/weekend consumption of 5kW and week-day consumption of 15-20kW. |
yes, this is an office :-)
With a basic categorisation approach we should be able to select only
dwellings or only offices for a specific analysis. That´s the idea of the
SAREF or Haystack implementations (see also #116).
…On Fri, Dec 9, 2016 at 9:06 AM, J. Ver. ***@***.***> wrote:
@saroele <https://github.com/saroele> JrtPec: Maybe also kick out
FL03001550 from the analysis? This seriously skews the analysis/mean. I'm
convinced this is not a residential building (an office?) as it has a
night/weekend consumption of 5kW and week-day consumption of 15-20kW.
https://opengrid.be/sensor/565de0a7dc64d8370aa321491217b85f
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It wouldn't be too hard to add a "building type" argument to the sites in the houseprint, both in the GDocs file as in the code + parser. I would like to see what naming conventions SAREF uses, but other than that it's pretty straightforward. |
We can easily develop an algorithm to detect these kind of outliers (I'm thinking a ransac regressor)
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