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一种基于粒子群算法和模糊逻辑的智能家居控制器的用户舒适度感知的改进优化函数。

An Improved Optimization Function to Integrate the User's Comfort Perception into a Smart Home Controller Based on Particle Swarm Optimization and Fuzzy Logic.

机构信息

Department of Electrical Engineering, Federal University of Ceará, Fortaleza 60455-760, CE, Brazil.

Department of Computing, Federal Institute of Ceará-IFCE Campus Maracanaú, Maracanaú 61925-315, CE, Brazil.

出版信息

Sensors (Basel). 2023 Mar 10;23(6):3021. doi: 10.3390/s23063021.

DOI:10.3390/s23063021
PMID:36991733
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10059585/
Abstract

Scheduling residential loads for financial savings and user comfort may be performed by smart home controllers (SHCs). For this purpose, the electricity utility's tariff variation costs, the lowest tariff cost schedules, the user's preferences, and the level of comfort that each load may add to the household user are examined. However, the user's comfort modeling, found in the literature, does not take into account the user's comfort perceptions, and only uses the user-defined preferences for load on-time when it is registered in the SHC. The user's comfort perceptions are dynamic and fluctuating, while the comfort preferences are fixed. Therefore, this paper proposes the modeling of a comfort function that takes into account the user's perceptions using fuzzy logic. The proposed function is integrated into an SHC that uses PSO for scheduling residential loads, and aims at economy and user comfort as multiple objectives. The analysis and validation of the proposed function includes different scenarios related to economy-comfort, load shifting, consideration of energy tariffs, user preferences, and user perceptions. The results show that it is more beneficial to use the proposed comfort function method only when the user requires SHC to prioritize comfort at the expense of financial savings. Otherwise, it is more beneficial to use a comfort function that only considers the user's comfort preferences and not their perceptions.

摘要

为了实现经济节省和用户舒适度，智能家居控制器 (SHC) 可以对居民用电负荷进行调度。为此，需要考虑电力公司的电价波动成本、最低电价成本计划、用户的偏好以及每个负荷为家庭用户带来的舒适度水平。然而，文献中的用户舒适度建模并未考虑用户的舒适度感知，仅在 SHC 中记录用户定义的负荷按时开启偏好时使用。用户的舒适度感知是动态变化的，而舒适度偏好是固定的。因此，本文提出了一种使用模糊逻辑考虑用户感知的舒适度函数建模方法。该函数被集成到一个使用粒子群优化算法 (PSO) 调度居民用电负荷的 SHC 中，旨在实现经济和用户舒适度的多目标优化。对所提出函数的分析和验证包括与经济舒适度、负荷转移、能源价格考虑、用户偏好和用户感知相关的不同场景。结果表明，仅当用户要求 SHC 优先考虑舒适度而牺牲经济节省时，使用所提出的舒适度函数方法更有利。否则，使用仅考虑用户舒适度偏好而不考虑其感知的舒适度函数更有利。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e27e/10059585/10d462683b3a/sensors-23-03021-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e27e/10059585/db7ceb53d291/sensors-23-03021-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e27e/10059585/07434704afd7/sensors-23-03021-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e27e/10059585/33343852a957/sensors-23-03021-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e27e/10059585/7f79ab2c4510/sensors-23-03021-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e27e/10059585/d26520992cdc/sensors-23-03021-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e27e/10059585/b2c477b4ef1c/sensors-23-03021-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e27e/10059585/9f1f2a232ac8/sensors-23-03021-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e27e/10059585/e70ab2236ba0/sensors-23-03021-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e27e/10059585/43840a33067e/sensors-23-03021-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e27e/10059585/10d462683b3a/sensors-23-03021-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e27e/10059585/db7ceb53d291/sensors-23-03021-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e27e/10059585/07434704afd7/sensors-23-03021-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e27e/10059585/33343852a957/sensors-23-03021-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e27e/10059585/7f79ab2c4510/sensors-23-03021-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e27e/10059585/d26520992cdc/sensors-23-03021-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e27e/10059585/b2c477b4ef1c/sensors-23-03021-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e27e/10059585/9f1f2a232ac8/sensors-23-03021-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e27e/10059585/e70ab2236ba0/sensors-23-03021-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e27e/10059585/43840a33067e/sensors-23-03021-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e27e/10059585/10d462683b3a/sensors-23-03021-g010.jpg

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引用本文的文献

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本文引用的文献

1
Relationships between Weather and Mood.天气与情绪之间的关系。
J Gen Psychol. 1982 Jul;107(1):155-156. doi: 10.1080/00221309.1982.9709917.
2
A multidimensional approach to the relationship between mood and weather.一种针对情绪与天气之间关系的多维研究方法。
Br J Psychol. 1984 Feb;75 ( Pt 1):15-23. doi: 10.1111/j.2044-8295.1984.tb02785.x.