Energy Savings for Air Conditioning System Using Fuzzy Logic Controller Design for Northeastern Nigeria

Francis, Maxwell; Luqman, Raji; Charles, Dadiyo; Audu, Anjili

doi:10.35313/ijatr.v3i2.95

Cited by 4 publications

(2 citation statements)

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“…When compared to several previous studies as outlined, this research demonstrates a commendable contribution, trailing behind only the study conducted by Francis et al (27) with a difference of 20.04%. Nevertheless, these findings already indicate that the application of fuzzy logic in AC control can optimize electrical power usage intelligently by adapting operations to the actual needs of the room and environmental conditions.…”

Section: 1 Testing With Fuzzy Logic Methodssupporting

confidence: 44%

“…By the use of fuzzy logic controllers in air conditioning systems Francis et al (27) have successfully reduced electricity consumption by adjusting the compressor speed and operating mode according to environmental conditions and user needs. This demonstrates significant potential for energy savings in air conditioning systems, particularly in urban areas, while considering various input parameters such as temperature, occupancy, time, and weather conditions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: 1 Testing With Fuzzy Logic Methodssupporting

confidence: 44%

Section: Introductionmentioning

confidence: 99%