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The proposed load forecasting approach outperformed the currently available state-of-the-art techniques; namely root mean square error (RMSE), normalized root mean square error (NRMSE), and mean absolute error (MAE).The innovations at different power system infrastructures’ levels facilitate the integration of new smart grid ideas.

These proposed algorithms include two benchmark disaggregation algorithms; Factorial Hidden Markov Model (FHMM), Combinatorial Optimization in addition to three adopted Deep Neural Network; long short- term memory (LSTM), Denoising Autoencoder, and a network which regress start time, end time, and average power.An adaptive neural fuzzy inference system (ANFIS) was combined with RBF neural network to adjust the forecasting by taking into consideration real-time electricity prices [6].A neural network based predictor for STLF was presented in [7].The second category is using the aggregated smart metering data to cancel out the uncertainty.Therefore, the aggregated load exhibits typically regular patterns and more accessible to predict.

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