A Hybrid Deep Neural Network for Electricity Theft Detection Using Intelligent Antenna-Based Smart Meters
Algorithm 2.
Pseudocode of the proposed HDNN model.
1: Initialization
2: Load the dataset Ds from the smart meter Sm
3: Perform pre-processing steps
4: Split Ds into Dst (train dataset), Dste (test dataset)
5: Adjust the parameter of the classifier such that
and e. Where b is the number of the batches, h is the number of hidden layers, n is the number of neurons in the hidden layer and e is the number of epoch set for training and testing phases 6: Pass the h, b and lr parameters to the PSO 7: Steps in training phase:
8: Build gruPSO ∈ PSO − GRU with the network parameters b, h and lr for the Ds
9: Repeat
10: At the nth epoch do
11: Train the gruPSO to fetch b from Ds
12: Proposed model’s performance is measured in terms of AUC, accuracy, precision, recall and F1-score
13: Update the performance of the queue ∈ Q. where Q ∈
14: End
15: The loss function is calculated until loss function ls ≤ Cn. Where Cn is the convergence threshold
16: If early stopping is performed, then
17: Proceed
18: Else, go to step 8
19: Steps in testing phase:
20: Fetch Dste from the Ds
21: Test is performed by gruPSO on the Dste to generate the final results through plots
22: Compare the proposed and benchmark models on the basis of Dste in terms of performance measures for final prediction
