Figure 1 Power cycle feature matrix extraction and splicing
As illustrated in Figure 1, the process begins with resampling to extract the average value, maximum value, minimum value, standard deviation, and variance of power generation over periods ranging from 1 hour to 24 hours, resulting in 24 feature matrices. These 24 feature matrices are then aligned with the input data’s time granularity (15 minutes). Finally, the 24 matrices are concatenated to construct the periodic scale feature input matrix for power generation.
In summary, by concatenating the raw data features, temporal scale features, and power cycle features, we construct the model’s input feature matrix. This fusion method integrates diverse characteristics to enhance the model’s predictive capability. The detailed feature information is presented in Table 3.
Table 3 Model Input Features