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

Table 7

Optimal chiller loading comparison by LRELD and NNGA methods.

Cooling load (RT)	Chiller	LR + ELD			NN + GA			Saving A − B (%)
		PLR	Load (RT)	Total (kW) (A)	PLR	Load (RT)	Total (kW) (B)
6023 (95%)	1	0.95	1216	5301.3	0.99658	1275.62	4405.2
	2	0.95	1216		1	1280.00
	4	0.95	1216		1	1280.00		16.90%
	5	0.95	1187.5		1	1250.00
	6	0.95	1187.5		0.75024	937.80
5706 (90%)	1	0.9	1152	4986.5	0.99316	1271.24	4141.4
	2	0.9	1152		1	1280.00
	4	0.9	1152		1	1280.00		16.95%
	5	0.9	1125		1	1250.00
	6	0.9	1125		0.5	625.00
5389 (85%)	1	0.85	1088	4679.1	0.74536	954.06	3937.3
	2	0.85	1088		1	1280.00
	4	0.85	1088		1	1280.00		15.85%
	5	0.85	1062.5		1	1250.00
	6	0.85	1062.5		0.5	625.00
5072 (80%)	1	0.8	1024	4379.2	0.5	640.00	3696
	2	0.8	1024		1	1280.00
	4	0.8	1024		1	1280.00		15.60%
	5	0.8	1000		0.99804	1247.55
	6	0.8	1000		0.5	625.00
4755 (75%)	1	0.75	960	4086.7	0.50489	646.26	3547.7
	2	0.75	960		1	1280.00
	4	0.75	960		1	1280.00		13.19%
	5	0.75	937.5		0.67791	847.39
	6	0.75	937.5		0.56109	701.36
4438 (70%)	1	0.7	896	3801.6	0.5	640.00	3344
	2	0.7	896		1	1280.00
	4	0.7	896		0.99071	1268.11		12.04%
	5	0.7	875		0.5	625.00
	6	0.7	875		0.5	625.00
4121 (65%)	1	0.65	832	3523.9	0.5	640.00	3177.9
	2	0.65	832		1	1280.00
	4	0.65	832		0.61193	783.27		9.82%
	5	0.65	812.5		0.5826	728.25
	6	0.65	812.5		0.55279	690.99
3804 (60%)	1	0.6	768	3253.7	0.5	640.00	3048
	2	0.6	768		0.9956	1274.37
	4	0.6	768		0.5	640.00		6.32%
	5	0.6	750		0.5	625.00
	6	0.6	750		0.5	625.00
3487 (55%)	1	0.55	704	2991	0.5	640.00	2912.3
	2	0.55	704		0.69208	885.86
	4	0.55	704		0.55572	711.32		2.63%
	5	0.55	687.5		0.5	625.00
	6	0.55	687.5		0.5	625.00