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

Table 8

Optimal chiller loading comparison by LRELD and ANFIS + GA methods.

Cooling load (RT)	Chiller	LR + ELD			ANFIS + GA			Saving A − B (%)
		PLR	Load (RT)	Total (kW) (A)	PLR	Load (RT)	Total (kW) (B)
6023 (95%)	1	0.95	1216	5301.3	0.87537	1120.47	4296.3
	2	0.95	1216		0.87732	1122.97
	4	0.95	1216		1	1280		18.96%
	5	0.95	1187.5		1	1250
	6	0.95	1187.5		1	1250
5706 (90%)	1	0.9	1152	4986.5	0.56549	723.8272	4052.9
	2	0.9	1152		0.93939	1202.42
	4	0.9	1152		1	1280		18.72%
	5	0.9	1125		1	1250
	6	0.9	1125		1	1250
5389 (85%)	1	0.85	1088	4679.1	0.5	640	3831.3
	2	0.85	1088		0.75806	9703.32
	4	0.85	1088		1	1280		18.12%
	5	0.85	1062.5		0.99902	1248.78
	6	0.85	1062.5		1	1250
5072 (80%)	1	0.8	1024	4379.2	0.51075	653.76	3625.8
	2	0.8	1024		0.5	640
	4	0.8	1024		1	1280		17.20%
	5	0.8	1000		0.99902	1248.78
	6	0.8	1000		1	1250
4755 (75%)	1	0.75	960	4086.7	0.5	640	3478.3
	2	0.75	960		0.5	640
	4	0.75	960		1	1280		14.89%
	5	0.75	937.5		0.88416	1105.2
	6	0.75	937.5		0.87195	1089.94
4438 (70%)	1	0.7	896	3801.6	0.5	640	3284.3
	2	0.7	896		0.5	640
	4	0.7	896		1	1280		11.66%
	5	0.7	875		0.93744	1171.8
	6	0.7	875		0.565	706.25
4121 (65%)	1	0.65	832	3523.9	0.55034	704.44	3195.8
	2	0.65	832		0.5	640
	4	0.65	832		0.63099	807.67		9.31%
	5	0.65	812.5		1	1250
	6	0.65	812.5		0.57527	719.09
3804 (60%)	1	0.6	768	3253.7	0.5	640	3048.2
	2	0.6	768		0.5	640
	4	0.6	768		0.59677	763.87		6.32%
	5	0.6	750		0.88172	1093.6
	6	0.6	750		0.52639	666.55
3487 (55%)	1	0.55	704	2991	0.5	640	2980.2
	2	0.55	704		0.5	640
	4	0.55	704		0.61975	793.28		0.36%
	5	0.55	687.5		0.56207	702.59
	6	0.55	687.5		0.56891	711.14