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

<div>Results of the EEMD decomposition. IMF1-IMF4 shows great nonstationary components. Res denotes the trend of the original signal.</div>

Computational Intelligence and Neuroscience

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Figure 2

Figure 2: Operational Scheduling of Behind-the-Meter Storage Systems Based on Multiple Nonstationary Decomposition and Deep Convolutional Neural Network for Price Forecasting 

Figure 2 | Operational Scheduling of Behind-the-Meter Storage Systems Based on Multiple Nonstationary Decomposition and Deep Convolutional Neural Network for Price Forecasting 