AMI	Advanced Metering Infrastructure
APD-HT	Anomaly Pattern Detection Hypothesis Testing
Bi-GRU	Bi-directional Gated Recurrent Unit
AUC	Area Under the Curve
Bi-LSTM	Bi-directional Long Short-Term Memory
CatBoost	Categorical Boosting
CNN	Convolutional Neural Network
DTKSVM	Decision Tree Combined K-Nearest Neighbor and Support Vector Machine
EBT	Ensemble Bagged Tree
ETD	Electricity Theft Detection
DT	Decision Tree
DR	Detection Rate
DG	Distributed Generation
XGBoost	Extreme Gradient Boosting
Fits	Feed-in Tariffs
FN	False Negative
FP	False Positive
FPR	FP Rate
GBCs	Gradient Boosting Classifiers
LGBoost	Light Gradient Boosting
MIC	Maximum Information Coefficient
ML	Machine Learning
NaN	Not a Number
NAN	Neighborhood Area Network
NTLs	Non-Technical Losses
PV	Photo Voltaic
PRC	Precision Recall Curve
RUSBOOST	Random Under Sampling Boosting
RF	Random Forest
SSEA	Semi-Supervised Auto-Encoder
SGCC	State Grid Corporation of China
SMs	Smart Meters
SSDAE	Stacked Sparse Denoising Auto-Encoder
SCADA	Supervisory Control and Data Acquisition
SVM	Support Vector Machine
TLs	Technical Losses
TN	True Negative
TP	True Positive
UP	Utility Provider
WFI	Weighted Feature Importance
C	Sample’s Unique Class
O	Observations
p	Population of the Samples
S	Number of Samples
$S_t$	Time-Series Data
T	Theft Case
$\sigma$	Standard Deviation
$\mu$	Mean