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• W4313306942 abstract "Improving the accuracy of solar power forecasting is becoming important as the development of this type of energy tends to increase sharply in the coming years. Forecasting solar power, especially rooftop solar power still faces many difficulties due to its dependence on solar radiation and other weather parameters. To deal with this problem and increase the forecasting performance, in this paper, a hybrid model was developed for short-term rooftop solar power forecasting. The developed hybrid model firstly omitted the white noise time series from the original dataset using the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) method and the remainder of the dataset was the input of the forecasting model using Bidirectional Long short-term memory (BiLSTM). Then, particle swarm optimization (PSO) was employed to optimize kernel parameters and weighted coefficients in the forecasting model. To demonstrate the forecasting performance, the proposed method was applied to forecast the rooftop solar power in Ha Bao, Lam Dong city, Vietnam and compared with 3 other models. Four evaluation metrics namely RMSE, n-RMSE, sMAPE and MAE showed that the proposed model outperformed the compared models, thus, the proposed PSO-CEEMDAN-BiLSTM was an effective method for short-term rooftop solar power forecasting." @default.
• W4313306942 created "2023-01-06" @default.
• W4313306942 creator A5032829480 @default.
• W4313306942 creator A5068357013 @default.
• W4313306942 creator A5077868189 @default.
• W4313306942 date "2022-11-21" @default.
• W4313306942 modified "2023-09-30" @default.
• W4313306942 title "Multiple Step Ahead Forecasting of Rooftop Solar Power Based on a Novel Hybrid Model of CEEMDAN - Bidirectional LSTM Network with Structure Optimized by PSO Method" @default.
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• W4313306942 doi "https://doi.org/10.1109/iccais56082.2022.9990323" @default.
• W4313306942 hasPublicationYear "2022" @default.
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