Wind Speed Forecast Based on the LSTM Neural Network Optimized by the Firework Algorithm

Algorithm 1

FWA-LSTM wind speed prediction algorithm.

(1)	Wind speed data preparation and preprocessing;
(2)	Normalize the raw data;
(3)	Divide training set and test set;
(4)	Construct LSTM wind speed prediction model. Set partial parameters and fix the number n of optimized parameter;
(5)	FWA parameter initialization (fireworks population size P, solving space dimension d, maximum number of iterations iter_max, explosion amplitude range control parameter , the maximum number of sparks m, the number of variation sparks , the parameters a and b that limit the number of sparks produced by the explosion, the minimum normal value ε that tends to zero, the solution space boundaries Bu and Bi);
(6)	Initialize the values of n-dimensional parameter combinations of P groups randomly in the solution space;
(7)	Initialize the global optimal parameter combination gbest_parameters, the partial optimal parameter combination pbest_parameters, and the best fitness function value Pg;
(8)	While the end condition is false:
(9)	Apply the n-dimensional parameter combinations of P groups, respectively, to the LSTM network flow prediction model for training, and calculate the current fitness function value;
(10)	Get the current best fitness value Pi and the corresponding parameter combination pbest_parameters;
(11)	if Fi <
(12)	= Fi;
(13)	gbest_parameters = pbest_parameters;
(14)	end if
(15)	for each parameter combination
(16)	Calculate the search direction and position of the new parameter combination according to formulas (8) and (9);
(17)	Fix the updated parameter in the selected values;
(18)	end for
(19)	The number of iterations+1;
(20)	end while
(21)	Return the gbest_parameters;
(22)	gbest_parameters is introduced into LSTM wind speed prediction model to predict test data and calculate prediction error.