Operational Scheduling of Behind-the-Meter Storage Systems Based on Multiple Nonstationary Decomposition and Deep Convolutional Neural Network for Price Forecasting

<div>Framework of our proposed electricity price forecasting with an end-to-end structure. The original price series is first decomposed using EEMD. According to fuzzy information entropy, two nonstationary components are selected to be decomposed by VMD furthermore. A different group of significant components is sent to the TCMS-CNN model for price forecasting respectively. Both results of branches are added as the final price prediction.</div>

Computational Intelligence and Neuroscience

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Figure 6: Operational Scheduling of Behind-the-Meter Storage Systems Based on Multiple Nonstationary Decomposition and Deep Convolutional Neural Network for Price Forecasting 

Figure 6 | Operational Scheduling of Behind-the-Meter Storage Systems Based on Multiple Nonstationary Decomposition and Deep Convolutional Neural Network for Price Forecasting 