Predictive Model of Humidity in Greenhouses through Fuzzy Inference Systems Applying Optimization Methods

Table 1

Exploratory analysis of the variables in the greenhouse.

Variable	Mean	Standard deviation	Skewness coefficient	Kurtosis	0%	25%	50%	75%	100%
act_fan	0.15253936	0.35954581	1.9328377	1.73591780	0	0.00000000	0.00000000	0.0000000	1
act_heating	0.11383125	0.31760876	2.4318046	3.91380017	0	0.00000000	0.00000000	0.0000000	1
act_solenoid_valve	0.03355672	0.18008662	5.1803775	24.83711288	0	0.00000000	0.00000000	0.0000000	1
co2_ppm	0.09974095	0.08708806	3.5546924	19.58887848	0	0.05239229	0.08400087	0.1121455	1
ground_humidity_per	0.62709550	0.21353123	−0.6608821	−0.33038421	0	0.50000000	0.70370370	0.8148148	1
hum	0.87382599	0.15438635	−1.3320890	1.50554501	0	0.78802395	0.94011976	1.0000000	1
light_Intensity	0.24009555	0.18741405	1.1521273	0.02054868	0	0.12153734	0.12153734	0.4061441	1
Luminosity	0.18616786	0.22691410	1.3011977	0.62474161	0	0.02197373	0.05108137	0.3270054	1
temp	0.33717349	0.16374756	0.1102891	−0.76813738	0	0.18143460	0.37552743	0.4556962	1