Combining IoT and Time Series Model for Minute-Level Outlier Detection in Wind Speed Forecasting

Nur'ainul Miftahul Huda, Nurfitri Imro'ah, Rahmi Hidayati, Kartika Sari

Abstract


Renewable energy optimisation and early warning systems require accurate short-term wind speed forecast. Anomalies in environmental data impair forecasting model reliability. This paper presents an integrated approach using IoT-based remote sensing and time series modelling to address the issue. IoT-based anemometer sensors collected wind speed data at one-minute intervals from December 24, 2024, to January 10, 2025. Aggregating the raw data into 5-minute intervals prepared it for the ARIMA model. This model determined temporal patterns and predicted short-term wind speeds. Analyzing residuals between observed and predicted results helped identify wind outliers. This approach is novel because it uses IoT-based continuous sensing and time series modeling for real-time environmental monitoring. Studies showed that a 65-minute frame with 5-minute intervals was best for replicating wind speed dynamics. Six cycles of outlier detection found 87 outliers. The ARIMA model improved predictions by include these outliers as exogenous variables. This emphasizes the importance of fixing time series model anomalies to improve prediction. The augmented ARIMA model with outlier corrections provides minute-level forecasts and reliable anomaly identification for renewable energy optimization and early warning systems. This study shows that new statistical methods and the Internet of Things (IoT) can improve real-time environmental and energy decisions.

Keywords


extreme; accuracy; autoregressive; sensor

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References


[1] J. K. B. Rogers, T. C. R. Mercado, and F. A. J. Galleto, “Comparison of ARIMA boost,
Prophet boost, and TSLM models in forecasting Davao City weather data,” Indonesian
Journal of Electrical Engineering and Computer Science, vol. 34, no. 2, pp. 1092–1101, 2024.
doi: 10.11591/ijeecs.v34.i2.pp1092-1101.

[2] H. Liu, J. Meng, W. Li, and S. Liu, “Extreme weather risk assessment and insurance
decision making based on ARIMA model and visual analysis,” Highlights in Science,
Engineering and Technology, vol. 101, pp. 305–313, 2024. doi: 10.54097/93qwm898.

[3] I. Tyass, A. Bellat, A. Raihani, K. Mansouri, and T. Khalili, “Wind speed prediction
based on seasonal ARIMA model,” E3S Web of Conferences, vol. 336, p. 00034, 2022.
doi: 10.1051/e3sconf/202233600034.

[4] Z. Zhou, Y. Dai, J. Xiao, M. Liu, J. Zhang, and M. Zhang, “Research on short-time
wind speed prediction in mountainous areas based on improved ARIMA model,”
Sustainability, vol. 14, no. 22, p. 15301, 2022. doi: 10.3390/su142215301.

[5] A. Demirtop and O. Sevli, “Wind speed prediction using LSTM and ARIMA time series
analysis models: A case study of Gelibolu,” Turkish Journal of Engineering, vol. 8, no. 3,
pp. 524–536, 2024. doi: 10.31127/tuje.1431629.

[6] L. A.-j. Dawod, “Forecasting short-term wind speed using ARIMA and convolutional
neural network models at international airports in Saudi Arabia,” Communications on
Applied Nonlinear Analysis, vol. 31, no. 6s, pp. 81–96, 2024. doi: 10.52783/cana.v31.1162.

[7] S. Sheoran and S. Pasari, “Efficacy and application of the window-sliding ARIMA for
daily and weekly wind speed forecasting,” Journal of Renewable and Sustainable Energy,
vol. 14, no. 5, 2022. doi: 10.1063/5.0108847.

[8] M. Roem, M. Musa, Rudianto, and Y. Risjani, “The surface wind regimes on the northeast
coastal of Kalimantan during 2016–2018,” IOP Conference Series: Earth and Environmental
Science, vol. 763, p. 012013, 2021. doi: 10.1088/1755-1315/763/1/012013.

[9] K. Furtak and A. Wolińska, “The impact of extreme weather events as a consequence of
climate change on the soil moisture and on the quality of the soil environment and
agriculture – a review,” CATENA, vol. 231, p. 107378, 2023.
doi: 10.1016/j.catena.2023.107378.

[10] R. Chikte, A. Mathew, B. Ramachandran, K. Almaeeni, N. Aljasmi, and J. Vandavasi,
“Short-term time series energy forecasting for smart buildings using machine learning
models,” in Volume 6: Energy, Nov. 2024. doi: 10.1115/IMECE2024-145349.

[11] H. Darmawan, M. Yuliana, and M. Z. Samsono Hadi, “Realtime weather prediction system
using GRU with daily surface observation data from IoT sensors,” in 2022 International
Electronics Symposium (IES), 2022, pp. 221–226. doi: 10.1109/IES55876.2022.9888468.

[12] U. Mukhaiyar, N. M. Huda, R. K. Novita Sari, and U. S. Pasaribu, “Modeling dengue
fever cases by using GSTAR(1;1) model with outlier factor,” Journal of Physics:
Conference Series, vol. 1366, p. 012122, 2019. doi: 10.1088/1742-6596/1366/1/012122.

[13] U. Mukhaiyar, N. M. Huda, K. N. Sari, and U. S. Pasaribu, “Analysis of generalized
space time autoregressive with exogenous variable (GSTARX) model with outlier factor,”
Journal of Physics: Conference Series, vol. 1496, p. 012004, 2020.
doi: 10.1088/1742-6596/1496/1/012004.

[14] T. Umairah, N. Imro’ah, and N. M. Huda, “ARIMA model verification with outlier factors
using control chart,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 18, no. 1,
pp. 0579–0588, 2024. doi: 10.30598/barekengvol18iss1pp0579-0588.

[15] S. A. Panchal, L. Ferdouse, and A. Sultana, “Comparative analysis of ARIMA and LSTM
models for stock price prediction,” in 2024 IEEE/ACIS 27th International Conference on
Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing
(SNPD), 2024, pp. 240–244. doi: 10.1109/SNPD61259.2024.10673919.

[16] S. O. Areh and N. H. Miswan, “ARIMA models for Kijang Emas price forecasting:
Pre- and post-COVID analysis,” Malaysian Journal of Mathematical Sciences, vol. 18,
no. 1, pp. 127–140, 2024. doi: 10.47836/mjms.18.1.08.

[17] K. Holakouie-Naieni, M. Sepandi, B. Eshrati, S. Nematollahi, and Y. Alimohamadi,
“Comparative performance of hybrid model based on discrete wavelet transform and
ARIMA models in prediction incidence of COVID-19,” Heliyon, vol. 10, no. 13, e33848,
2024. doi: 10.1016/j.heliyon.2024.e33848.

[18] R. Ospina, J. A. M. Gondim, V. Leiva, and C. Castro, “An overview of forecast analysis
with ARIMA models during the COVID-19 pandemic: Methodology and case study in
Brazil,” Mathematics, vol. 11, no. 14, p. 3069, 2023. doi: 10.3390/math11143069.

[19] J. D. Cryer and K.-S. Chan, Time Series Analysis. New York: Springer, 2008.
doi: 10.1007/978-0-387-75959-3.

[20] M. Gupta, J. Gao, C. C. Aggarwal, and J. Han, “Outlier detection for temporal data:
A survey,” IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 9,
pp. 2250–2267, 2014. doi: 10.1109/TKDE.2013.184.

[21] N. M. Huda, U. Mukhaiyar, and N. Imro’ah, “An iterative procedure for outlier detection
in GSTAR(1;1) model,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 16, no. 3,
pp. 975–984, 2022. doi: 10.30598/barekengvol16iss3pp975-984.

[22] X. Du, E. Zuo, Z. Chu, Z. He, and J. Yu, “Fluctuation-based outlier detection,”
Scientific Reports, vol. 13, p. 2408, 2023. doi: 10.1038/s41598-023-29549-1.

[23] L. F. Maldaner, J. P. Molin, and M. Spekken, “Methodology to filter out outliers in high
spatial density data to improve maps reliability,” Scientia Agricola, vol. 79, no. 1, 2022.
doi: 10.1590/1678-992X-2020-0178.

[24] C. Wang, Z. Liu, H. Gao, and Y. Fu, “VOS: A new outlier detection model using virtual
graph,” Knowledge-Based Systems, vol. 185, p. 104907, 2019.
doi: 10.1016/j.knosys.2019.104907.

[25] H. Wang, M. J. Bah, and M. Hammad, “Progress in outlier detection techniques:
A survey,” IEEE Access, vol. 7, pp. 107964–108000, 2019.
doi: 10.1109/ACCESS.2019.2932769.

[26] W. W. S. Wei, Time Series Analysis: Univariate and Multivariate Methods, 2nd ed.
Addison Wesley, 2006. (Available online.)

[27] N. M. Huda, U. Mukhaiyar, and U. S. Pasaribu, “Forecasting dengue fever cases using
autoregressive distributed lag model with outlier factor,” AIP Conference Proceedings,
2020. doi: 10.1063/5.0018450.

[28] S. Armiento, F. Meder, and B. Mazzolai, “Device for simultaneous wind and raindrop
energy harvesting operating on the surface of plant leaves,” IEEE Robotics and Automation
Letters, vol. 8, no. 4, pp. 2269–2276, 2023. doi: 10.1109/LRA.2023.3250006.

[29] J. S. Sarjerao, G. Sudhagar, and R. N. Jadhav, “Sustainable smart irrigation in precision
farming using IoT,” in 2025 International Conference on Emerging Smart Computing and
Informatics (ESCI), 2025, pp. 1–7. doi: 10.1109/ESCI63694.2025.10987956.

[30] D. Kim and T.-Y. Heo, “Anomaly detection with feature extraction based on machine
learning using hydraulic system IoT sensor data,” Sensors, vol. 22, no. 7, p. 2479, 2022.
doi: 10.3390/s22072479.




DOI: https://doi.org/10.18860/cauchy.v10i2.35768

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