Clustering houses by energetic profiles The issue Data pre-processing A primary approach to the answer Easier is best Conclusions

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. Because 120 kWh isn’t an excessive amount of or little, it’s what it’s, and it is important to grasp when you can improve it. To attain this functionality we’ve got faced the issue of classifying the homes of our customers in similar groups by way of the energy profile of the homes, to be able to compare consumption, and thus place them in a percentile of expenditure in comparison with other houses.

  • . From an energy perspective, it’s practical to categorise dwellings primarily by size.
  • . We’ve seen that regardless of how similar the homes are by way of characteristics, it doesn’t make sense for instance to group as similar a house with gas heating and one with electric heating (statistical studies indicate that electric heating is nearly 50% of household consumption), so we propose to estimate a volume of Kw “available” by multiplying the devices that we all know that a house has by its average nominal power.
  • . Spain is a rustic by which we will find very different climatic profiles even inside the same regional organizations. We’ve used for our grouping the Mediterranean, cold inland, warm inland, Atlantic, and Cantabrian profiles. We’ve assigned each complete province to considered one of these climates.
  • . We all know that that is an assumption that may have nuances, but we prefer to make use of it to characterize the extent of energy performance we take as distinct single-family homes and apartments.

In the primary approach, we proposed the potential of using Python mathematical libraries to construct a service that might perform the classification in three steps:

  • A This filter would modify the dataset dimensions to make it two-dimensional, from two columns to a single pair of values, to be able to give you the chance to run K-means against a two-dimensional vector.
  • . To attain an optimal result, we ran a clustering simulation for several values of the entire centroids, calculating the error of every run and saving the one with the smallest deviation.
  • Cluster task to every house using .

Once we finished the proof of concept of the classifier we realized that the classification process based on the VertexAI service, despite being very comfortable for us, introduces the error inherent to the model added to the error introduced by the K-means algorithm, so we decided to eliminate it from the equation by directly calculating the cluster of a latest house through the minimum distance to the centroids.

XKCD

In an unsupervised analytical process, as within the case of the applying of the K-means algorithm, . On this context, we’ve got put effort into preparing a dataset on the input of the algorithm containing processed data in each column to present us an excellent result.

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