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

<div>The result of 24-hour multistep prediction. Compared with single-step prediction, the predicted value curve has a relatively weak fitting effect because of accumulative errors within the rolling process, but it can also capture most of the peak points.</div>

Computational Intelligence and Neuroscience

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Figure 11

Figure 11: Operational Scheduling of Behind-the-Meter Storage Systems Based on Multiple Nonstationary Decomposition and Deep Convolutional Neural Network for Price Forecasting 

Figure 11 | Operational Scheduling of Behind-the-Meter Storage Systems Based on Multiple Nonstationary Decomposition and Deep Convolutional Neural Network for Price Forecasting 