Summary of the methodology used for the forecasting and clustering analysis.
|Step||ANN||SVR and LS-SVR||Clustering and forecast|
|1||Import and clean raw data from missing points and outliers|
|2||Choose a resolution and horizon||Resolution: 60 min, horizon:24 h|
|3||Organize input matrix X and output vector Y|
|4||Normalize X and Y||Organize Y as 24 hourly daily and re-arrange X accordingly|
|5||Randomly partition data into 10 folds||Use daily variance and peak index values to be used for clustering|
|6||Allocate 9 folds for training and validation of the models||Run K-means with optimum number of clusters|
|7||Tune the ANN ensemble parameters using the training set||Tune the SVR and LS-SVR parameters using the training set||Assign daily profiles to the relevant cluster|
|8||Choose optimum parameters according to the smallest 10 fold cross-validation error||Repeat steps 4–8 of ANN,SVR and LS-SVR methods on the clusters|
|9||Test the models on the independent test set and store the results in the “Predictions” vector||Store results separately for each cluster and also for the entire clustered data in Predictions_wCluster|
|10||Repeat the first 9 steps for each of the 10 folds.||Calculate error metrics and compare the results obtained by Predictions_wCluster and Predictions|
|11||Calculate error metrics by using the “Predictions” vector and the corresponding real load values|
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