Application of ARIMA Kalman Filter with Multi-Sensor Data Fusion Fuzzy Logic to Improve Indoor Air Quality Index Estimation

Erfianto, Bayu; Rahmatsyah, Andrian

doi:10.30630/joiv.6.4.889

Cited by 3 publications

(3 citation statements)

References 17 publications

(19 reference statements)

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“…Moreover, SVM is regarded as among the finest machine learning techniques for both regression and classification, according to some statistical learning theories (Gao et al, 2003;Yuan et al, 2010). When the results of SVM were compared to those of other strong data-driven empirical techniques like ARIMA, RBF, MLP, and IIR-LRNN, the SVR results were observed to exceed or be equivalent to those of other learning machines (Erfianto and Rahmatsyah, 2022;Moura et al, 2011;Said et al, 2023). Additionally, SVR is thought to function well for time series analysis because of better generalizability and the capability of ensuring a global minimum for certain training data (Fuadi et al, 2021;Wu et al, 2004).…”

Section: Support Vector Machine (Svm) In Energy Regulationmentioning

confidence: 99%

Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

Le,

Priya,

et al. 2024

Int. J. Renew. Energy Dev.

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The escalating trends in energy consumption and the associated emissions of pollutants in the past century have led to energy depletion and environmental pollution. Achieving comprehensive sustainability requires the optimization of energy efficiency and the implementation of efficient energy management strategies. Artificial intelligence (AI), a prominent machine learning paradigm, has gained significant traction in control applications and found extensive utility in various energy-related domains. The utilization of AI techniques for addressing energy-related challenges is favored due to their aptitude for handling complex and nonlinear data structures. Based on the preliminary inquiries, it has been observed that predictive analytics, prominently driven by artificial neural network (ANN) algorithms, assumes a crucial position in energy management across various sectors. This paper presents a comprehensive bibliometric analysis to gain deeper insights into the progression of AI in energy research from 2003 to 2023. AI models can be used to accurately predict energy consumption, load profiles, and resource planning, ensuring consistent performance and efficient resource utilization. This review article summarizes the existing literature on the implementation of AI in the development of energy management systems. Additionally, it explores the challenges and potential areas of research in applying ANN to energy system management. The study demonstrates that ANN can effectively address integration issues between energy and power systems, such as solar and wind forecasting, power system frequency analysis and control, and transient stability assessment. Based on the comprehensive state-of-the-art study, it can be inferred that the implementation of AI has consistently led to energy reductions exceeding 25%. Furthermore, this article discusses future research directions in this field.

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Section: Support Vector Machine (Svm) In Energy Regulationmentioning

confidence: 99%

Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

Le,

Priya,

et al. 2024

Int. J. Renew. Energy Dev.

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“…The actions taken from fuzzy logic control are the result of conversion at this stage [109]. This stage can be completed using several methods, such as centroid method, mean-max method, weighted average method, and center-of-gravity (COG) method [110], [111]. While the defuzzification method used for this study is weighted average.…”

Section: Fuzzy Logicmentioning

confidence: 99%

Temperature and Humidity Control System with Air Conditioner Based on Fuzzy Logic and Internet of Things

Furizal,

Sunardi,

Yudhana

2023

Journal of Robotics and Control

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Work is an activity that takes most of the day to earn a living and improve the standard of living. During work, many people have to work indoors, which can be a less comfortable and unhealthy place if the temperature and humidity are not well controlled. Unsuitable temperature and humidity conditions can negatively affect the health and comfort of workers, as well as interfere with productivity and work quality. However, the problem that often arises is the difficulty of controlling room temperature and humidity effectively, especially in rooms that are closed and do not get air circulation from outside. Therefore, an effective solution is needed to control the temperature and humidity of the room automatically and remotely via the internet. The contribution of this research is to develop an effective and efficient AC control system in controlling room temperature and humidity using Tsukamoto's Fuzzy Inference System (FIS) method and the Internet of Things (IoT). Tsukamoto's FIS is used to produce AC temperature values in room temperature and humidity control as measured by the DHT22 sensor directly integrated with the ESP32 microcontroller. This control system is monitored remotely using IoT concepts through a mobile application interface. The results of this study show that room temperature can be controlled under normal conditions, with an average change of -1.67°C and an overall average temperature of 25.95°C. While the average humidity is at a value of 80.16% which is included in the Wet set. This suggests that humidity cannot be controlled under normal conditions, so it still requires further development. In addition, it is also necessary to further investigate the effectiveness of the tool in various sizes and more complex layouts of rooms.

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“…Dang et al [10] propose a fuzzy adaptive controller design for a class of networked control systems (NCS) with the presence of network induced delay, data packet dropout and unknown time-delay controlled plant. The mathematical model of Smith predictor, which is combined with fuzzy adaptive controller and fuzzy compensator time-delay, is designed to adjust online its parameters according to the changing of the system's output [12]. The results based on TrueTime Beta2.0 simulation platform demonstrate that our design significantly improves the response of system over unknown time-delay.…”

Section: Introductionmentioning

confidence: 98%

An Artificial Neural Networks (ANN) Approach for 3 Degrees of Freedom Motion Controlling

Khanh

Thao

2023

JOIV : Int. J. Inform. Visualization

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Maritime simulation systems provide opportunities to acquire technical, procedural, and operational skills without the risks and expenses associated with on-the-job training. Maritime simulation systems are tools used to simulate real-world scenarios for training and research purposes, in which they are used to train seafarers in a safe and controlled environment. These systems are used to simulate different scenarios, such as navigation, maneuvering, and ship handling. The simulation systems allow users to learn and practice different scenarios without exposing themselves to real-life risks. However, at the moment, Vietnam's maritime simulators are dependent on other nations, which results in a lack of technological autonomy, a lengthy transfer of technology, high expenses, and a reduction in national security. Therefore, there is a lot of interest in developing a domestic maritime simulation system. With a rotation angle of α = [α1 α2 α3]T from the PLC controlling the DC/Servo system, the motion platform of the marine simulation system is built on the Stewart platform design principle. Due to the use of conventional control methods, this system suffers from a time delay of up to 1200ms, which prevents it from reacting to real-time control. In this paper, we investigate a novel technique for controlling the dynamic model with three degrees of freedom (3 DOF) of a cockpit cabin deck using artificial neural networks. The findings demonstrate that the reaction to real-time control, rotation error, and drive/servo system movement are all greatly improved.

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Application of ARIMA Kalman Filter with Multi-Sensor Data Fusion Fuzzy Logic to Improve Indoor Air Quality Index Estimation

Cited by 3 publications

References 17 publications

Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

Temperature and Humidity Control System with Air Conditioner Based on Fuzzy Logic and Internet of Things

An Artificial Neural Networks (ANN) Approach for 3 Degrees of Freedom Motion Controlling

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