The Optimization of Chiller Loading by Adaptive Neuro-Fuzzy Inference System and Genetic Algorithms

Table 6

Optimal chiller loading comparison by LRELD and LRGA methods.

Cooling load (RT)	Chiller	LR + ELD			LR + GA			Saving A − B (%)
		PLR	Load (RT)	Total (kW) (A)	PLR	Load (RT)	Total (kW) (B)
6023 (95%)	1	0.95	1216	5301.3	0.83431	1067.92	5240.8
	2	0.95	1216		0.99902	1278.75
	4	0.95	1216		0.99951	1279.37		1.14%
	5	0.95	1187.5		0.92864	1160.80
	6	0.95	1187.5		0.98925	1236.56
5706 (90%)	1	0.9	1152	4986.5	0.76197	975.32	4894.3
	2	0.9	1152		1	1280.00
	4	0.9	1152		0.99951	1279.37		1.85%
	5	0.9	1125		0.85924	1074.05
	6	0.9	1125		0.87781	1097.26
5389 (85%)	1	0.85	1088	4679.1	0.67253	860.84	4574.4
	2	0.85	1088		1	1280.00
	4	0.85	1088		1	1280.00		2.24%
	5	0.85	1062.5		0.80303	1003.79
	6	0.85	1062.5		0.77175	964.69
5072 (80%)	1	0.8	1024	4379.2	0.58798	752.61	4280.6
	2	0.8	1024		0.99902	1278.75
	4	0.8	1024		0.99902	1278.75		2.25%
	5	0.8	1000		0.74145	926.81
	6	0.8	1000		0.66813	835.16
4755 (75%)	1	0.75	960	4086.7	0.53666	686.92	4012.1
	2	0.75	960		0.99902	1278.75
	4	0.75	960		1	1280.00		1.83%
	5	0.75	937.5		0.66569	832.11
	6	0.75	937.5		0.54203	677.54
4438 (70%)	1	0.7	896	3801.6	0.60068	768.87	3737.6
	2	0.7	896		0.50098	641.25
	4	0.7	896		0.99951	1279.37		1.68%
	5	0.7	875		0.74633	932.91
	6	0.7	875		0.65249	815.61
4121 (65%)	1	0.65	832	3523.9	0.5176	662.53	3468.9
	2	0.65	832		0.5	640.00
	4	0.65	832		0.99951	1279.37		1.56%
	5	0.65	812.5		0.66618	832.73
	6	0.65	812.5		0.56549	706.86
3804 (60%)	1	0.6	768	3253.7	0.52981	678.16	3223.2
	2	0.6	768		0.50098	641.25
	4	0.6	768		0.74438	952.81		0.94%
	5	0.6	750		0.67302	841.28
	6	0.6	750		0.55279	690.99
3487 (55%)	1	0.55	704	2991	0.52884	676.92	2967.9
	2	0.55	704		0.5	640.00
	4	0.55	704		0.51222	655.64		0.77%
	5	0.55	687.5		0.67058	838.23
	6	0.55	687.5		0.54106	676.33