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A platform for research: civil engineering, architecture and urbanism
Time series analysis model for forecasting unsteady electric load in buildings
Accurate and reliable load forecasting is crucial for ensuring the security and stability of the power grid. This paper proposes a combined prediction method based on Empirical Wavelet Transform (EWT) and Autoformer time series prediction model for the non-stationary and non-linear time series of electric load. The original sequence is first decomposed by EWT to obtain a set of stable subsequences, and then the Autoformer time series prediction model is used to predict each subsequence. Finally, the prediction results of each subsequence are combined to obtain the final prediction results. The proposed EWT-Autoformer prediction model is applied to an electric load example, and the experimental results are compared with the Recurrent Neural Network (RNN) method, Long Short-Term Memory (LSTM) method, and Informer method under the same conditions. The experimental results indicate that compared to LSTM, the method proposed in the paper has an R2 improvement of 9–20 percentage points, an improvement of 6–8 percentage points compared to RNN, an improvement of 3–7 percentage points compared to Informer, and an improvement of 2–3 percentage points compared to Autoformer. In addition, the RMSE and MAE are also significantly lower than other models.
Time series analysis model for forecasting unsteady electric load in buildings
Accurate and reliable load forecasting is crucial for ensuring the security and stability of the power grid. This paper proposes a combined prediction method based on Empirical Wavelet Transform (EWT) and Autoformer time series prediction model for the non-stationary and non-linear time series of electric load. The original sequence is first decomposed by EWT to obtain a set of stable subsequences, and then the Autoformer time series prediction model is used to predict each subsequence. Finally, the prediction results of each subsequence are combined to obtain the final prediction results. The proposed EWT-Autoformer prediction model is applied to an electric load example, and the experimental results are compared with the Recurrent Neural Network (RNN) method, Long Short-Term Memory (LSTM) method, and Informer method under the same conditions. The experimental results indicate that compared to LSTM, the method proposed in the paper has an R2 improvement of 9–20 percentage points, an improvement of 6–8 percentage points compared to RNN, an improvement of 3–7 percentage points compared to Informer, and an improvement of 2–3 percentage points compared to Autoformer. In addition, the RMSE and MAE are also significantly lower than other models.
Time series analysis model for forecasting unsteady electric load in buildings
Dandan Liu (author) / Hanlin Wang (author)
2024
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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