Predicting Short-Term Electricity Demand by Combining the Advantages of ARMA and XGBoost in Fog Computing Environment

Algorithm 1

Prototype-based K-means clustering algorithm.

Input: Historical electricity consumption records R

Output: the group division of enterprise users ClusterSet(Power_i)

  (1) / the historical total electricity consumption of each /

    INPUT:

    FOR To length(user_i)

     DO

      SumPower_i = 0

      FOR To length(Record_date_j)

       DO

        SumPower_i = SumPower_i + ;

       END

     END

    OUTPUT:

  (2) / the clustering algorithm (K-means) to cluster all the objects and then choose k =

    1 to cluster all the samples into a cluster and find the centroid of the /

    INPUT:

    DO

      Select

      ClusterCenter = Kmeans (SumPower_i)

    END

    OUTPUT: ClusterCenter

  (3) / the relative /

    INPUT: ClusterCenter,

    FOR To length(user_i)

     DO

      MedianDistance_i = median(AbsoluteDistance_i)

      RelativeDistance_i = AbsoluteDistance_i/MedianDistance_i

     END

    OUTPUT:

  (4) Make discrete point relative distance error figure and roughly determine the range of k

    accord to the relative distance

  (5) /-means algorithm to do clustering for enterprises, and get the groups of enterprises

    according to the result of /

    INPUT: , k

    DO

      Select

      ClusterSet(user_i) = Kmeans(user_i)

    END

    OUTPUT: ClusterSet(user_i)