| (i) | INPUTS: , the wind speed dataset. |
| (ii) | PARAMETERS: |
| | Nstd: noise standard deviation in CEEMDAN |
| | NR: number of realizations in CEEMDAN |
| | MaxIter: maximum number of sifting iterations that are allowed in CEEMDAN |
| q: variable that is selected in GSFS |
| MaxIterRF: maximum number of iterations in random forest |
| NTress: number of trees in random forest |
| N: number of whales in WOA |
| MaxIterWOA: maximum number of iterations in WOA |
| (iii) | OUTPUTS : the forecasting error |
| (1) | The original dataset is divided into training dataset and test dataset . |
| (2) | Decomposition using CEEMDAN, which results in I IMFs: . |
| (3) | Establish a quadratic model of and , which results in and |
| (4) | INITIALIZATION: set , . |
| (5) | IF DO |
| (6) | Calculate the pseudoresponse |
| (7) | Perform regression on the data with a pseudoresponse and select the most strongly correlated variable |
| (8) | Update the response and . |
| (9) | END IF |
| (10) | FOR to DO |
| (11) | Orthogonalize via the Gram–Schmidt method. |
| (12) | Perform regression on the data with a pseudoresponse and select the most strongly correlated variable |
| (13) | Update the response and . |
| (14) | END FOR |
| (15) | Build the solution path and use HDIC to select the optimal solution, including q important variables. |
| (16) | Grow a random forest with NTress = 3000 trees using the selected variables with MaxIterRF iterations. |
| (17) | Initialize the whale population for weight matrix in which the columns are weights for established RF models. |
| (18) | Calculate the fitness function using and RF models |
| (19) | Let be the current best weights |
| (20) | WHILE DO |
| (21) | FOR each weight DO |
| (22) | Update and |
| (23) | IF THEN |
| (24) | IF THEN |
| (25) | Update the positions of the current weight via (12) and (13) |
| (26) | ELSE IF THEN |
| (27) | Randomly select a search agent |
| (28) | Update the position of the current search agent via (17) |
| (29) | END IF |
| (30) | ELSE IF THEN |
| (31) | Update the position of the current weight via (16) |
| (32) | END FOR |
| (33) | Check if any search agent goes beyond the search space |
| (34) | Calculate the fitness function of each weight |
| (35) | Update if there is a better solution |
| (36) | |
| (37) | END WHILE |
| (38) | Obtain the optimal weights |
| (39) | Calculate the forecasting error using the optimal weights and the RF model that is generated by and . |
