Temperature, pressure, relative humidity and rainfall sensors early error detection system for automatic weather station (AWS) with artificial neural network (ANN) backpropagation

Wellyantama, P.; Soekirno, Santoso

doi:10.1088/1742-6596/1816/1/012056

Cited by 6 publications

(9 citation statements)

References 2 publications

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“…The data segmentation scenario is split into two conditions: the normal dataset and the synthetic dataset [18]. The normal dataset contains the original parameter values, signifying sensors operating under standard conditions.…”

Section: Data Segmentationmentioning

confidence: 99%

Predictive Maintenance Automatic Weather Station Sensor Error Detection using Long Short-Term Memory

Santoso,

Ryan,

Wicaksana

et al. 2023

ULTIMA Computing

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Weather information plays a crucial role in various sectors due to Indonesia's wide range of weather and extreme climate phenomena. Automatic Weather Stations (AWS) are automated equipment designed to measure and collect meteorological parameters such as atmospheric pressure, rainfall, relative humidity, atmospheric temperature, wind speed, and wind direction. Occasionally, AWS sensors may produce erroneous values without the technicians' awareness. This study aims to develop sensors error detection system for predictive maintenance on AWS using the Long Short-Term Memory (LSTM) model. The AWS dataset from Jatiwangi, West Java, covering the period from 2017 to 2021, will be utilized in the study. The study revolves around developing and testing four distinct LSTM models dedicated to each sensor: RR, TT, RH, and PP. The research methodology involves a phased approach, encompassing model training on 70% of the available dataset, subsequent validation using 25% of the data, and finally, testing on 5% of the dataset alongside the calibration dataset. Research outcomes demonstrate notably high accuracy, exceeding 90% for the RR, TT, and PP models, while the RH model achieves above 85%. Moreover, the research highlights that Probability of Detection (POD) values generally trend high, surpassing 0.8, with a low False Alarm Rate (FAR), typically below 0.1, except for the RH model. Sensor condition requirements will adhere to the rules set by World Meteorological Organization (WMO) and adhere to the permitted tolerance limits for each weather parameter.

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Section: Data Segmentationmentioning

confidence: 99%

Predictive Maintenance Automatic Weather Station Sensor Error Detection using Long Short-Term Memory

Santoso,

Ryan,

Wicaksana

et al. 2023

ULTIMA Computing

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“…In this paper, the NB-IoT wireless data transmission technology is adopted to optimize the weather station and upload the acquired data to the cloud platform for users to monitor the meteorological data in real time [12]. The research results solve the disadvantages of traditional weather stations to a certain extent, and have a certain research significance for the development of weather stations and NB-IoT.…”

Section: Related Workmentioning

confidence: 99%

Based on Internet of Things Platform Using NB-IoT Communication Low-Power Weather Station System

Deng

Zhang

et al. 2022

Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications

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In recent years, meteorological environment has become a topic of concern to people. Various meteorological disasters threaten human life and production. Accurate and timely acquisition of meteorological data has become a prerequisite for dealing with various aspects of production and life, and also laid a foundation for weather prediction. For a long time, meteorological data acquisition system combined with modern information technology has gradually become a hot spot in the field of meteorological monitoring and computer research. The continuous development of NB-IoT technology has brought new elements to the research of meteorological monitoring system. This paper designs a weather station system based on NB-IoT, including data acquisition module, main controller module, NB-IoT wireless communication module, energy capture module, low power consumption scheme, etc.

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“…Range kelembapan dan suhu diperoleh dari hasil analisis kemungkinan suhu di wilayah Kota Yogyakarta sehingga kisarannya tidak terlalu lebar dan umum [15]. Data uji yang dianalisis untuk membuat fungsi keanggotaan fuzzy diperoleh dari data yang bersumber dari Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) [16].…”

Section: Pendahuluanunclassified

Optimasi Pengendalian Suhu dan Kelembapan Ruangan di Kota Yogyakarta Menggunakan Metode Fuzzy

2022

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The dry season is a season where most regions in Indonesia experience an increase in temperature. This unstable temperature can have a negative effect on the human body, so a control device is needed according to the needs of the body automatically. This study focuses on optimizing room temperature and humidity control in Yogyakarta City using a fan duty cycle unit with the Fuzzy Tsukamoto method. The ideal temperature and humidity range is obtained from measurements by the Indonesian Board of Meteorology, Climatology, and Geophysics (BMKG). The purpose of this study is to reduce the hot temperature in the room to normal temperature conditions. The calculation results with a temperature of 28.29°C and humidity of 79.06% resulted in a duty cycle of 40.92%. Based on 50 sample data taken each fan rotated for five minutes showed that the average change in temperature was -0.01°C and humidity -0.032%, meaning it could lower 0.01°C and humidity 0.032% every five minutes. This result is considered inefficient considering the very small changes, so in subsequent studies it is recommended to use technology such as air conditioning as a control tool

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Temperature, pressure, relative humidity and rainfall sensors early error detection system for automatic weather station (AWS) with artificial neural network (ANN) backpropagation

Cited by 6 publications

References 2 publications

Predictive Maintenance Automatic Weather Station Sensor Error Detection using Long Short-Term Memory

Predictive Maintenance Automatic Weather Station Sensor Error Detection using Long Short-Term Memory

Based on Internet of Things Platform Using NB-IoT Communication Low-Power Weather Station System

Optimasi Pengendalian Suhu dan Kelembapan Ruangan di Kota Yogyakarta Menggunakan Metode Fuzzy

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