Skip to main content
SpringerLink
Log in

IndoorSense: context based indoor pollutant prediction using SARIMAX model

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Indoor air pollutants e.g., Carbon dioxide (CO2), Particulate Matter(PM)2.5, PM10, Total Volatile Organic Compounds (TVOC), etc. have a serious impact on human health. Out of these pollutants, CO2 is one of the most dominant one. Hence, proper monitoring and control of this pollutant is an important part of maintaining a healthy indoor. To make this happen, it is required to predict the next moment’s indoor pollutant level at an acceptable accuracy range that ensures necessary steps can be taken beforehand to avoid a rise in the indoor pollution level for maintaining a healthy indoor all the time. It also helps people plan ahead, decreases the adverse effects on health and the costs associated. For this experiment, we have collected three months of real-life time-series data along with proper context information and have gone through feature engineering and feature selection process to create model ready data. Now, since the indoor CO2 concentration is dependent on multiple external factors (context data) which in turn is dependent on time, makes it a time-dependent function. Hence, to predict the indoor pollutant CO2, here we have used the time series forecasting model based on our collected data nature. This is a powerful tool and used in a wide range of research domains for predicting the next moment’s target value. This model ready data is utilized in forecasting different time series models. According to our findings, among the selected popular time series models, the SARIMAX time series model is best suited for this forecasting problem which is utilizing indoor context information along with historical data (with 10 Fold Time-Series Split Cross-Validation score 0.907). We have achieved an average of RMSE 26.45 ppm (i.e., 97.36% accuracy) level based on a three day average for indoor pollutant prediction which is outperforming other relevant models in this domain.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Ahn J, Shin D, Kim K, Yang J (2017) Indoor air quality analysis using deep learning with sensor data. Sensors (Basel, Switzerland) 17(11):2476. https://doi.org/10.3390/s17112476

    Article  Google Scholar 

  2. Airveda (2020) Air Quality Monitors; web link: https://www.airveda.com/. Accessed 7 September 2020

  3. Arunraj NS, Ahrens D (2015) A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting. Int J Prod Econ 170(Part A):321–335. ISSN 0925–5273. https://doi.org/10.1016/j.ijpe.2015.09.039

    Article  Google Scholar 

  4. Azuma K, Kagi N, Yanagi U, Osawa H (2018) Effects of low-level inhalation exposure to carbon dioxide in indoor environments: a short review on human health and psychomotor performance. Environment International 121(Part 1):51–56. ISSN 0160–4120. https://doi.org/10.1016/j.envint.2018.08.059

    Article  Google Scholar 

  5. Bommert A, Sun X, Bischl B, Rahnenführer J, Lang M (2020) Benchmark for filter methods for feature selection in high-dimensional classification data. Computational Statistics & Data Analysis 143:106839. ISSN 0167–9473. https://doi.org/10.1016/j.csda.2019.106839

    Article  MathSciNet  Google Scholar 

  6. Candanedo LM, Feldheim V (2016) Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. Energy and Buildings 112:28–39. ISSN 0378–7788. https://doi.org/10.1016/j.enbuild.2015.11.071

    Article  Google Scholar 

  7. Chi HR, Tsang KF, Wu CK (2016) Indoor air quality management control scheme for smart community. In: IEEE 14th international conference on industrial informatics (INDIN), Poitiers. https://doi.org/10.1109/INDIN.2016.7819302, pp 972–975

  8. Dutta J, Chowdhury C, Roy S, Middya AI, Gazi F (2017) Towards Smart City: sensing air quality in City based on opportunistic crowd-sensing. In: Proceedings of the 18th international conference on distributed computing and networking (ICDCN). ACM, New York, NY, USA, Article 42. https://doi.org/10.1145/3007748.3018286

  9. Dutta J, Gazi F, Roy S, Chowdhury C (2016) Airsense: opportunistic crowd-sensing based air quality monitoring system for Smart City. IEEE SENSORS, Orlando, FL, USA, 2016, pp 1–3. https://doi.org/10.1109/ICSENS.2016.7808730

  10. Dutta J, Roy S (2017) Iot-fog-cloud based architecture for Smart City: prototype of a smart building. In: 7th international conference on cloud computing, data science & engineering - confluence, Noida, India. https://doi.org/10.1109/CONFLUENCE.2017.7943156, pp 237–242

  11. Dutta J, Roy S, Chowdhury C (2019) Unified framework for IoT and smartphone based different smart city related applications. Microsyst Technol 25:83–96. https://doi.org/10.1007/s00542-018-3936-9

    Article  Google Scholar 

  12. Elamin N, Fukushige M (2018) Modeling and forecasting hourly electricity demand by SARIMAX with interactions. Energy 65(Part B):257–268. ISSN 0360–5442. https://doi.org/10.1016/j.energy.2018.09.157

    Article  Google Scholar 

  13. Jacob M, Neves C (2020) Short term load forecasting. In: Forecasting and assessing risk of individual electricity peaks. Mathematics of Planet Earth. Springer, Cham

  14. Jović A, Brkić K, Bogunović N (2015) A review of feature selection methods with applications. In: 38th international convention on information and communication technology, electronics and microelectronics (MIPRO), Opatija. https://doi.org/10.1109/MIPRO.2015.7160458, pp 1200–1205

  15. Khazaei B, Shiehbeigi A, Haji MAKAR (2019) Modeling indoor air carbon dioxide concentration using artificial neural network. Int J Environ Sci Technol 16:729–736. https://doi.org/10.1007/s13762-018-1642-x

    Article  Google Scholar 

  16. Lohani D, Acharya D (2016) Smartvent: a context aware IoT system to measure indoor air quality and ventilation rate. In: 17th IEEE international conference on mobile data management (MDM), Porto, 2016. https://doi.org/10.1109/MDM.2016.91, pp 64–69

  17. Middya AI, Roy S, Dutta J, Das R (2020) JUSense: a unified framework for participatory-based urban sensing system. Mobile Netw Appl 25:1249–1274. https://doi.org/10.1007/s11036-020-01539-x

    Article  Google Scholar 

  18. pmdarima statistical python library, weblink: https://pypi.org/project/pmdarima/. Accessed 9 June 2020

  19. Priyamvada WR (2017) Review on various models for time series forecasting. In: International conference on inventive computing and informatics (ICICI), Coimbatore, pp 405–410

  20. Raman RK, Mohanty SK, Bhatta KS, et al. (2018) Time series forecasting model for fisheries in Chilika lagoon (a Ramsar site, 1981), Odisha, India: a case study. Wetlands Ecol Manage 26:677–687. https://doi.org/10.1007/s11273-018-9600-4

    Article  Google Scholar 

  21. RESET™: International Standard for Healthy Buildings, Weblink: https://www.reset.build/

  22. Rodero A, Krawczyk DA (2019) Carbon dioxide human Gains—A new approach of the estimation. Sustainability 11(24):7128. https://doi.org/10.3390/su11247128

    Article  Google Scholar 

  23. Teleszewski T, Gładyszewska FK (2019) The concentration of carbon dioxide in conference rooms: a simplified model and experimental verification. Int J Environ Sci Technol 16:8031–8040. https://doi.org/10.1007/s13762-019-02412-5

    Article  Google Scholar 

  24. Vagropoulos SI, Chouliaras GI, Kardakos EG, Simoglou CK, Bakirtzis AG (2016) Comparison of SARIMAX, SARIMA, modified SARIMA and ANN-based models for short-term PV generation forecasting. IEEE International Energy Conference (ENERGYCON), Leuven, pp 1–6. https://doi.org/10.1109/ENERGYCON.2016.7514029

  25. Wang H, Xie L, Liu S, Xu J (2016) A model-based control of CO2 concentration in multi-zone ACB air-conditioning systems. In: 12th IEEE international conference on control and automation (ICCA), Kathmandu, pp 467–472

  26. Zhang H, Wang X, Cao J, et al. (2018) A multivariate short-term traffic flow forecasting method based on wavelet analysis and seasonal time series. Appl Intell 48:3827–3838. https://doi.org/10.1007/s10489-018-1181-7

    Article  Google Scholar 

Download references

Acknowledgements

The research work of Joy Dutta is funded by “Visvesvaraya PhD Scheme, Ministry of Electronics & IT, Government of India”. This research work is also supported by the project entitled “Participatory and Realtime Pollution Monitoring System For Smart Cit”, funded by Higher Education, Science & Technology and Biotechnology, Department of Science & Technology, Government of West Bengal, India.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Joy Dutta & Sarbani Roy

Authors
  1. Joy Dutta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sarbani Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sarbani Roy.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dutta, J., Roy, S. IndoorSense: context based indoor pollutant prediction using SARIMAX model. Multimed Tools Appl 80, 19989–20018 (2021). https://doi.org/10.1007/s11042-021-10666-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-10666-w

Keywords

Search

Navigation