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. 2023 Feb 2;23(3):1639. doi: 10.3390/s23031639

Table 4.

Qualitative comparison of smart grid related works.

Use Case Ref Contribution AI Role
(At the Edge)
AI Algorithm Dataset AI Placement Employed
Technology
Platform Metrics Benefits
AI-Edge
Drawbacks
Smart grid LDF [33] Short-term energy consumption forecasting Prediction LSTM Pecan Street Inc’s Dataport site Edge, cloud Federated learning Python, TensorFlow Federated 0.4.0 Tensorflow 1.13.1 backend RMSE, MAPE High accuracy Heterogeneous data unsolved
[34] Short-term energy consumption forecasting Prediction, classification LSTM, K-means Energy company UK Power Networks Edge device, cloud Federated learning Python, TensorFlow RMSE, training time High accuracy, heterogeneous data solved Privacy still low
[35] Day-ahead prediction of building energy demands Prediction, Feature selection Ant-bee, cuckoo, elephant, flower, genetic harmony, PSO, rhino, wolf, DT, HT Ornl-research-house-3 Edge server (Raspberry Pi) Low-cost model Keras, Python Accuracy, time, speed, MAE High accuracy, low training time Low interpretability
[36] Short-term electricity demand Prediction, classification XGBoost, K-means Tianchi under license Edge server (PC) Low-cost model Not mentioned Training time, accuracy, cross-entropy loss High accuracy Data distribution unsolved
[37] Short-term electricity demand Outlier detection, Feature selection, prediction NB, wrapper FS, Filter FS EUNITE dataset Fog nodes Matlab Accuracy, error, precision, sensitivity/recall High accuracy, reliability, resilience, stability High complexity of model
[38] Online short-term energy prediction data preprocessing, prediction DNN Real-world dataset Edge server, edge devices, cloud Collaborative learning Not mentioned Flexibility, accuracy Flexibility, high accuracy, dynamic data, IoT addressed, real-time prediction Less scalability
[39] Load forecasting for optimal energy management Prediction CNN IHEPC dataset Edge devices / TensorFlow, Keras MAPE, RMSE Low complexity Heterogeneous data, uncertainties, privacy is not addressed
[40] Online short-term residential load forecasting Prediction STN Ohta-AMPds datasets Edge device Low-cost model-reservoir computing Not mentioned RMSE, MAE Low complexity, high accuracy Heterogeneity not addressed
D.S.M [41] Demand-side management Resource management RL Real-world dataset Edge server (Raspberry Pi) Real implementation Not mentioned / Less scalability
[42] Demand-side management Classification LDA REFIT project Edge server Low-cost model Not mentioned MAPE, RMSE
[43] Managing prosumers over wireless networks Data preprocessing, prediction LSTM Pecan Street Inc.’s Dataport site Edge server Federated learning TensorFlow RMSE, data transmitted Heterogeneous data addressed, high accuracy low-communication cost Single-point failure not addressed
LAD [44] Detection of anomalous power consumption at household prediction GBR, RFR, LR, SVR IHEPC dataset Edge server, fog / Not mentioned MAPE, RMSE Load reduction Communication cost still high
[45] Anomaly detection in smart-meter data resource allocation, classification SDA, GA, kNN IHEPC dataset Edge server / Not mentioned Accuracy, execution time, energy consumption
[46] Electric energy fraud detection Dimensionality reduction, prediction DTR, LR D1C database Edge server Raspberry Pi model Not mentioned MAPE
[47] Anomaly detection consumption smart grid Classification DNN, HDBSC K-means, KNN Midwest region Edge server, Raspberry Pi / Not mentioned Testing time, frequency, model size Low complexity, high accuracy
[48] Energy theft detection Feature-extraction classification VAE-GAN, K-means GEF Com 2012 public dataset Edge server / Not mentioned ROC curve, running efficiency Adaptive model, high accuracy -
[49] Energy theft detection Classification (SGCC) dataset Edge devices Federated learning Flower RMSE, log loss accuracy, precision F-measure Privacy Low accuracy compared with the centralized model