Machine Learning as a Downscaling Approach for Prediction of Wind Characteristics under Future Climate Change Scenarios

Table 3

Predicted monthly mean (PMM) of wind speed changes compared to the year 2005.

	Monthly prediction based on Scenarios and Years
	Historical	RCP2.6			RCP4.5			RCP8.5
Month	2005	2030	2035	2040	2030	2035	2040	2030	2035	2040
Jan	13.2741	11.93	11.61	11.16	11.84	11.77	11.57	11.76	11.61	11.80
Feb	11.8188	12.63	12.45	12.89	12.75	12.72	12.35	12.32	12.46	13.14
Mar	10.1221	11.22	11.47	11.59	11.10	10.77	10.83	11.04	11.03	11.62
Apr	10.0947	9.590	9.483	9.593	9.763	9.500	9.598	9.416	9.816	9.419
May	9.0151	7.441	7.463	7.694	7.726	7.840	7.480	7.539	7.411	7.422
Jun	7.7712	6.922	7.636	7.530	7.156	7.178	7.268	6.899	7.385	7.797
Jul	7.0615	6.315	6.836	6.414	6.682	6.572	6.464	6.401	6.309	6.447
Aug	8.5289	6.329	7.085	6.555	6.648	6.873	6.550	6.721	6.218	7.160
Sep	9.6920	8.455	8.222	8.503	8.498	8.390	8.884	8.220	8.208	9.083
Oct	11.5291	10.10	10.35	9.839	10.02	10.00	9.741	9.650	10.15	9.788
Nov	11.8781	10.83	10.85	10.51	11.11	11.18	11.50	10.93	11.16	10.81
Dec	11.1440	11.14	11.57	11.15	11.06	10.99	11.24	11.49	11.26	11.05
Mean	10.1608	9.409	9.586	9.452	9.529	9.481	9.457	9.365	9.418	9.628