Fermat-curve based fuzzy inference system for the fuzzy logic controller performance optimization in load frequency control application

Ghadim, Hadi Vatankhah; Hagh, Mehrdad Tarafdar; Zadeh, Saeid Ghassem

doi:10.1007/s10700-022-09402-2

Cited by 4 publications

(1 citation statement)

References 24 publications

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“…The other two models are Mamdani and Sugeno reported in literature (36)(37)(38). FIS serves as a mathematical framework or model employed for implementing fuzzy logic in decision-making or system control (39,40). FIS encompasses several key components that collaborate to generate outputs based on fuzzy inputs.…”

Section: Fuzzy Tsukamotomentioning

confidence: 99%

Energy Efficiency with Internet of Things Based Fuzzy Inference System for Room Temperature and Humidity Regulation

Furizal,

Sunardi,

Yudhana

et al. 2024

IJE

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Energy consumption is a crucial aspect in the effort to optimize the utilization of resources and reduce energy wastage. Focusing on energy efficiency can result in operational cost savings, a reduction in greenhouse gas emissions, and support for environmental sustainability for future generations. Therefore, it is important to consider energy efficiency in daily life, especially in the use of electricity for electronic devices. This research aims to compare the energy efficiency of two different approaches to Air Conditioner (AC) usage: the manual method and the fuzzy logic method. The manual method involves eight tests with direct power measurements over a 30-minute period at various AC temperature settings, namely 18°C, 20°C, 23°C, 24°C, 25°C, 26°C, 27°C, and 30°C. On the other hand, the fuzzy logic method involves six tests allowing for dynamic temperature adjustments based on room conditions. The research findings indicate that the fuzzy logic method achieves lower average power consumption, except at 30°C, where the manual method is slightly more efficient (a difference of 140,745 watts). This difference is primarily attributed to the "cooling and fan" mode used at lower temperatures in the manual method, resulting in higher power consumption. Furthermore, this research reveals the potential of the fuzzy logic in optimizing AC power usage based on real-time conditions, achieving approximately a 41.96% energy savings. The primary contribution of this study is to provide practical insights into how the fuzzy logic method can significantly reduce AC energy consumption, support energy efficiency efforts, and contribute to environmental sustainability.

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