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

<div>Two subnetworks MS-CNN and fully connected networks constitute the backbone of TCMS-CNN. At the end of two branches, a fully connected layer fuses features extracted from a multiscale dilated neural network and periodic coding model. This design ensures an excellent ability to learn nonlinear relationships in electricity price fluctuations.</div>

Computational Intelligence and Neuroscience

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Figure 5

Figure 5: Operational Scheduling of Behind-the-Meter Storage Systems Based on Multiple Nonstationary Decomposition and Deep Convolutional Neural Network for Price Forecasting 

Figure 5 | Operational Scheduling of Behind-the-Meter Storage Systems Based on Multiple Nonstationary Decomposition and Deep Convolutional Neural Network for Price Forecasting 