Short-Term Electricity Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Improved Sparrow Search Algorithm–Convolutional Neural Network–Bidirectional Long Short-Term Memory Model

Short-Term Electricity Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Improved Sparrow Search Algorithm–Convolutional Neural Network–Bidirectional Long Short-Term Memory Model

Accurate power load forecasting plays an important role in smart grid analysis. To improve the accuracy of forecasting through the three-level “decomposition–optimization–prediction” innovation, this study proposes a prediction model that integrates complete ensemble empirical mode decomposition wit...

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Bibliographic Details
Main Authors: Han Qiu, Rong Hu, Jiaqing Chen, Zihao Yuan
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Mathematics
Subjects:
short-term load forecasting
CEEMDAN
convolutional neural network
bidirectional long short-term memory
improved sparrow search algorithm
Online Access:https://www.mdpi.com/2227-7390/13/5/813
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