Load Energy Forecasting based on a Hybrid PSO LSTM-AE Model

A. Saoud, A. Recioui


In the smart grid, the data collected from the smart meters can be used to develop an accurate energy consumption forecasting. A close prediction is beneficial in providing a good energy scheduling, making balance between demand and generation of power which results in reducing the production costs of the energy. Several models are used to give an accurate energy consumption forecast. One of these models is long short-term memory (LSTM). LSTM model may be combined with other models to give better results.  On the other hand, LSTM has a drawback of selecting the hyperparameters values. In this paper, we optimize a long short-term memory autoencoder (LSTM-AE) model with the metahuristic algorithm Particle Swarm Optimization (PSO) in order to obtain the optimal parameters for the model to give better results in forecasting and then compared to other forecasting models. The evaluation metrics used for the comparison are mean squareerror (MSE) and root mean square error (RMSE).

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