Abstract
Background: Accurate short-term load forecasting is an important guarantee for the safety, stability, economic and efficient operation of power systems.
Objective: In order to effectively improve the forecasting accuracy, this paper proposes a hybrid forecasting model of convolutional neural network (CNN) and bi-directional long short-term memory (BiLSTM) neural network considering load time-varying trend mapping model (M).
Methods: Firstly, the time-varying features of the load curve are analyzed, and a mapping model is established to characterize the load time-varying trend. The features of the load time-varying trend are extracted, and they are quantified into a mathematical model. Secondly, the feature set is reconstructed through data migration. Then, the reconstructed feature set is input into the CNN-BiLSTM hybrid model. In the hybrid model, CNN is used to extract the features from data again to form a new feature vector, and then the feature vector is input into BiLSTM for forecasting.
Result: The power load data set from the New England in United States is used to simulate and verify the correctness and validity of the proposed method.
Conclusion: With the comparison of the forecasting results between different load forecasting models, the results show that the forecasting accuracy of the proposed method is higher and it is verified that the load time-varying trend mapping method proposed can improve the forecasting accuracy of different models in varying degrees.
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