Thi Hoai Thu Nguyen
ABSTRACT
Load forecasting has always played a particularly important role in the power industry. In this article, we proposed a hybrid model based on Ensemble Empirical Mode Decomposition (EEMD) and Bidirectional Long Short-Term memory (Bi-LSTM). The original time series of load demand was decomposed into several sub-series called Intrinsic Mode Functions (IMFs) using the EEMD method. Then each IMF was divided into training, validation and testing datasets, and predicted using Bi-LSTM. Finally, the forecasting of the load demand would be obtained by composing the predictions of all IMFs. This model was applied to forecast the electrical demand in Hanoi using the data in 2018. The data was first decomposed based on Seasonal-Trend decomposition using the LOESS (STL) method to find down if there was a seasonal characteristic or not. Then 2 cases were analyzed, forecasting the load with and without considering the seasons. The performance of the proposed model in both cases were compared with other traditional models. The results showed that the proposed approach outperformed the other methods with minimal mean absolute percentage error (MAPE) of just over 2% when considering the whole year data as one time series. Meanwhile, the results of the seasonal division are slightly better in 3 seasons and more biased in the summer due to more variability.
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