一种利用改进麻雀搜索算法的中央空调系统节能控制方法

doi:10.16183/j.cnki.jsjtu.2022.018

摘要/Abstract

摘要：

中央空调系统在面临末端数量多、末端负荷需求变化频繁的情况下,虽然采用常规的比例积分微分控制或固定参数控制能够满足冷量需求,但存在冷量过剩带来的能耗问题.针对中央空调系统中的空气调节子系统,提出一种基于改进麻雀搜索算法(ISSA)的中央空调系统节能控制方法,利用t分布强化麻雀群体的搜索能力,基于轮盘赌规则使得个体向最优群体学习,增强算法跳出局部最优的能力,有效改进控制参数的寻优精度和稳定性.在12个测试函数中,寻优精度和稳定性大多提升2个数量级以上.针对空气调节子系统能耗优化问题,ISSA表现出很好的节能潜力,相比于固定参数的控制方法节约能耗25.13%.ISSA解决实际工程问题的可行性也得到验证.

关键词: 中央空调系统, 优化控制, 麻雀搜索算法, 轮盘赌

Abstract:

There are many terminals in the central air-conditioning system of which load demands vary frequently. Although conventional proportional integral derivative control or fixed parameter control can meet the load demand, there is a problem of energy waste caused by excess cooling. This paper proposes a central air-conditioning system energy-saving control method based on an improved sparrow search algorithm (ISSA) for the air-water system in the central air-conditioning system. The ISSA applies t-distribution to strengthening the search ability and enables the individual to learn from the best group based on the roulette wheel selection, which enhances the ability of the algorithm to jump out of the local optimum and improves the accuracy and stability of control parameters effectively. For the 12 test functions, most of the optimization accuracy and stability have been improved by more than 2 orders of magnitude. Compared with the original control strategy, the ISSA has shown a good energy-saving potential for energy optimization of air conditioning subsystems, reducing energy consumption by 25.13%. The feasibility of the ISSA in actual engineering problems has also been verified.

Key words: central air-conditioning system, optimal control, sparrow search algorithm (SSA), roulette wheel

中图分类号:

熊磊, 苗雨润, 范新舟, 姚晔. 一种利用改进麻雀搜索算法的中央空调系统节能控制方法[J]. 上海交通大学学报, 2023, 57(4): 495-504.

XIONG Lei, MIAO Yurun, FAN Xinzhou, YAO Ye. Energy-Saving Control of Central Air-Conditioning System Based on an Improved-SSA[J]. Journal of Shanghai Jiao Tong University, 2023, 57(4): 495-504.

图/表 7

图1

表1

ISSA测试函数

测试函数	范围	最小值
$F 1 (s) = ∑ α = 1 δ s α 2$	[-100, 100]	0
$F 2 (s) = ∑ α = 1 δ \| s α \| + ∏ α = 1 δ \| s α \|$	[-10, 10]	0
$F 3 (s) = ∑ α = 1 δ ∑ ψ = 1 α s ψ 2$	[-100, 100]	0
$F 4 (s) = m a x α {\| s α \|, 1 ≤ α ≤ δ}$	[-100, 100]	0
$F 5 (s) = ∑ α = 1 δ (s α + 0.5) 2$	[-100, 100]	0
$F 6 (s) = ∑ α = 1 δ - 1 [100 (s α + 1 - s α 2) 2 + (s α - 1) 2]$	[-30, 30]	0
$F 7 (s) = ∑ α = 1 δ \| s α \| α + 1$	[-1, 1]	0
$F 8 (s) = ∑ α = 1 δ s α 2 + ∑ α = 1 δ (s α / 2) 2 + ∑ α = 1 δ (s α / 2) 4$	[-5, 5]	0
$F 9 (s) = ∑ α = 1 δ (s α 2 - 10 c o s 2 π s α + 10)$	[-5.12, 5.12]	0
$F 10 (s) = 14000 ∑ α = 1 δ s α 2 - ∏ α = 1 δ c o s s α α + 1$	[-600, 600]	0
$F 11 (s) = - 20 e x p - 0.2 1 δ ∑ α = 1 δ s α 2 - e x p 1 n ∑ α = 1 δ c o s 2 π s α + 20 + e$	[-32, 32]	0
$F 12 (s) = π δ [10 s i n π y 1 + ∑ α = 1 δ - 1 (y α - 1) 2 (1 + 10 s i n 2 π y α + 1)] + ∑ i = 1 δ u α$	[-50, 50]	0
$y α = 1 + s α + 1 4, u α = 100 (s α - 10) 4, s α > 10 0, - 10 ≤ s α ≤ 10 100 (- s α - 10) 4, s α < - 10$	[-50, 50]	0

表1

表2

表3

图2

图3

图4

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测试函数	ISSA1		ISSA2		ISSA
测试函数	平均值	标准差	平均值	标准差	平均值	标准差
F₁	4.81×10^-14	1.52×10^-13	1.09×10^-16	2.93×10^-16	1.72×10^-16	8.11×10^-16
F₂	5.78×10^-6	1.34×10^-5	5.97×10^-8	1.60×10^-7	2.86×10^-7	9.22×10^-7
F₃	1.25×10^-11	4.12×10^-11	3.19×10^-12	8.58×10^-12	9.38×10^-16	5.1×10^-15
F₄	6.55×10^-8	1.64×10^-7	1.96×10^-8	6.41×10^-8	5.76×10^-9	1.85×10^-8
F₅	1.06×10^-7	1.79×10^-7	1.04×10^-9	1.10×10^-9	6.47×10^-10	5.67×10^-10
F₆	4.20×10^-6	6.38×10^-6	4.83×10^-7	4.32×10^-7	7.47×10^-7	8.02×10^-7
F₇	4.24×10^-17	2.09×10^-16	6.63×10^-19	2.68×10^-18	6.85×10^-18	3.19×10^-17
F₈	3.52×10^-10	1.31×10^-9	8.19×10^-13	2.41×10^-12	5.55×10^-13	1.68×10^-12
F₉	3.01×10^-14	1.05×10^-13	4.91×10^-14	1.25×10^-13	2.75×10^-11	9.29×10^-11
F₁₀	3.74×10^-16	1.09×10^-15	1.47×10^-14	5.49×10^-14	3.70×10^-18	1.34×10^-18
F₁₁	1.68×10^-6	3.16×10^-6	4.72×10^-10	1.91×10^-9	4.53×10^-19	2.38×10^-9
F₁₂	2.06×10^-8	3.77×10^-8	7.35×10^-11	8.93×10^-11	2.71×10^-11	9.78×10^-11

测试函数	DE		GA		SBO		SSA		ISSA
测试函数	平均值	标准差	平均值	标准差	平均值	标准差	平均值	标准差	平均值	标准差
F₁	3.88×10^-5	1.08×10^-5	7.09×10	1.34×10²	1.44×10^-3	3.46×10^-4	1.86×10^-12	8.75×10^-12	1.72×10^-16	8.11×10^-16
F₂	7.58×10^-4	1.49×10^-4	1.79×10	9.34×10^-1	5.78×10^-2	2.16×10^-2	4.76×10^-5	1.77×10^-4	2.86×10^-7	9.22×10^-7
F₃	2.55×10^-3	5.98×10^-4	8.05×10³	9.54×10³	1.97×10	5.77×10	5.94×10^-9	3.04×10^-8	9.38×10^-16	5.10×10^-15
F₄	2.57	1.13	4.68×10	4.64	5.68	1.32	7.50×10^-7	2.07×10^-6	5.76×10^-9	1.85×10^-8
F₅	3.92×10^-5	1.21×10^-5	3.07×10	5.09×10	1.69×10^-3	6.45×10^-4	2.31×10^-6	5.52×10^-6	6.47×10^-10	5.67×10^-10
F₆	7.71×10	4.46×10	2.22×10³	3.94×10³	1.66×10²	1.57×10²	1.51×10^-5	2.69×10^-5	7.47×10^-7	8.02×10^-7
F₇	4.57×10^-23	2.07×10^-22	9.41×10^-7	4.91×10^-6	4.52×10^-11	3.29×10^-11	4.95×10^-16	2.13×10^-15	6.85×10^-18	3.19×10^-17
F₈	1.72×10²	2.32×10²	1.77×10²	2.32×10	3.14	2.66	9.80×10^-10	3.84×10^-9	5.55×10^-13	1.68×10^-12
F₉	6.73×10	1.14×10	6.59×10	5.93	2.59×10	8.05	8.22×10^-12	3.02×10^-11	2.75×10^-11	9.29×10^-11
F₁₀	3.89×10^-4	2.63×10^-4	1.53	8.73×10^-1	4.57×10^-2	1.81×10^-2	9.88×10^-14	4.47×10^-12	3.70×10^-18	1.34×10^-18
F₁₁	1.58×10^-3	2.16×10^-3	2.26	1.07	1.70	1.59	1.97×10^-6	8.11×10^-6	4.53×10^-19	2.38×10^-9
F₁₂	3.20×10^-6	5.53×10^-6	2.45	2.42	3.46	4.26	2.00×10^-6	2.41×10^-6	2.71×10^-11	9.78×10^-11