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Predictive Load Management Using IoT and Data Analytics

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Artificial Intelligence of Things (ICAIoT 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1930))

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Abstract

The objective of this paper is to design and implement an Internet of Things (IoT) based energy data acquisition system that incorporates a Long Short Term Memory (LSTM) model for predicting household energy demand and optimizing energy consumption with load scheduling and shifting. To achieve this goal, the system feeds 7 days’ worth of energy consumption data, along with relevant features such as temperature, humidity, precipitation, and holiday information, into the LSTM model to predict the energy demand curve. The system categorizes loads into deferrable and non-deferrable loads. Based on these categories, the system applies load scheduling techniques to flatten out the demand curve and optimize energy consumption. In addition, the system shifts the deferrable loads from the grid to renewable sources during peak hours if available. This helps to reduce the burden on the main grid and promotes sustainability. Furthermore, the system takes into account the day-ahead hourly price-based tariff rate, ensuring that energy consumption is cost-effective. This is achieved with a prototype Predictive Load Management Device (PLMD). To relay information to the users, the system includes a web-based application made in Blynk that presents the information in a simple, easy-to-understand format. This reduces the complexity associated with different techniques of demand-side management (DSM) and makes it accessible to users with varying technical backgrounds. The web-based application allows users to monitor energy consumption patterns, view predictions generated by the LSTM model, and make informed decisions on energy consumption intuitively and straightforwardly.

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References

  1. Rodrigues, S., Faria, F., Ivaki, A., Cafôfo, N., Chen, X., Dias, M.: The Tesla powerwall: does it bring something new? A market analysis. In: Proceedings of the Engineering & Technology, Computer, Basic & Applied Sciences (ECBA-2015), Bangkok, Thailand, 9–10 December 2015 (2015)

    Google Scholar 

  2. Chatfield, C.: Time-Series Forecasting (2000). https://doi.org/10.1201/9781420036206

  3. Gellings, C.W., Chamberlin, J.H.: Demand-Side Management: Concepts and Methods (1987)

    Google Scholar 

  4. Philippou, N., Hadjipanayi, M., Makrides, G., Efthymiou, V., Georghiou, G.E.: Effective dynamic tariffs for price-based Demand Side Management with grid-connected PV systems. In: 2015 IEEE Eindhoven PowerTech (2015). https://doi.org/10.1109/ptc.2015.7232387

  5. He, W.: Load forecasting via deep neural networks. Procedia Comput. Sci. 122, 308–314 (2017). https://doi.org/10.1016/j.procs.2017.11.374

    Article  Google Scholar 

  6. Khan, Z.A., Jayaweera, D.: Smart meter data based load forecasting and demand side management in distribution networks with embedded PV systems. IEEE Access 8, 2631–2644 (2020). https://doi.org/10.1109/access.2019.2962150

    Article  Google Scholar 

  7. Fan, L., Li, J., Zhang, X.P.: Load prediction methods using machine learning for home energy management systems based on human behavior patterns recognition. CSEE J. Power Energy Syst. 6(3), 563–571 (2020)

    Google Scholar 

  8. Saglam, M., Spataru, C., Karaman, O.A.: Electricity demand forecasting with use of artificial intelligence: the case of Gokceada island. Energies 15(16), 5950 (2022). https://doi.org/10.3390/en15165950

    Article  Google Scholar 

  9. Rahman, Md.A., Rahman, I., Mohammad, N.: Demand side residential load management system for minimizing energy consumption cost and reducing peak demand in smart grid. In: 2020 2nd International Conference on Advanced Information and Communication Technology (ICAICT) (2020). https://doi.org/10.1109/icaict51780.2020.9333451

  10. Zhu, Q., Li, Y., Song, J.: DSM and optimization of multihop smart grid based on genetic algorithm. Comput. Intell. Neurosci. 2022, 5354326 (2022). https://doi.org/10.1155/2022/5354326

    Article  Google Scholar 

  11. Adejumobi, I.A., Adesina Adeoti, J.: Efficient utilization of industrial power: demand side management approach. In: 2019 IEEE PES/IAS PowerAfrica (2019). https://doi.org/10.1109/powerafrica.2019.8928817

  12. Javor, D., Raicevic, N.: Two-steps procedure in demand side management for reducing energy costs. In: 2020 19th International Symposium INFOTEH-JAHORINA (INFOTEH) (2020). https://doi.org/10.1109/infoteh48170.2020.9066311

  13. Stute, J., Kühnbach, M.: Dynamic pricing and the flexible consumer – investigating grid and financial implications: a case study for Germany. Energ. Strat. Rev. 45, 100987 (2023). https://doi.org/10.1016/j.esr.2022.100987

    Article  Google Scholar 

  14. Maharaja, K., Balaji, P.P., Sangeetha, S., Elakkiya, M.: Development of bidirectional net meter in grid connected solar PV system for domestic consumers. In: 2016 International Conference on Energy Efficient Technologies for Sustainability (ICEETS) (2016). https://doi.org/10.1109/iceets.2016.7582897

  15. Anjana, S.P., Angel, T.S.: Intelligent demand side management for residential users in a smart micro-grid. In: 2017 International Conference on Technological Advancements in Power and Energy (TAP Energy) (2017). https://doi.org/10.1109/tapenergy.2017.8397265

  16. Tabassum, Z., Shastry, B.S.C.: Peak power management of residential building using demand side management strategies. Int. J. Health Sci., 8978–8997 (2022). https://doi.org/10.53730/ijhs.v6ns2.7333

  17. Allegromicro: ACS712: fully integrated, hall-effect-based linear current sensor IC. https://www.allegromicro.com/en/products/sense/current-sensor-ics/zero-to-fifty-amp-integrated-conductor-sensor-ics/acs712

  18. Olah, C.: Understanding LSTM Networks. http://colah.github.io/posts/2015-08-Understanding-LSTMs/

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Institute of Engineering, Tribhuwan University, Pulchowk Campus, Lalitpur, Nepal

    Sushil Phuyal, Shashwot Shrestha, Swodesh Sharma, Rachana Subedi & Shahabuddin Khan

Authors
  1. Sushil Phuyal
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  2. Shashwot Shrestha
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  3. Swodesh Sharma
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  4. Rachana Subedi
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  5. Shahabuddin Khan
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Corresponding author

Correspondence to Sushil Phuyal .

Editor information

Editors and Affiliations

  1. National Institute of Technical Teachers Training and Research, Chandigarh, India

    Rama Krishna Challa

  2. Durham University, Durham, UK

    Gagangeet Singh Aujla

  3. National Institute of Technical Teachers Training and Research, Chandigarh, India

    Lini Mathew

  4. National Institute of Technical Teachers Training and Research, Chandigarh, India

    Amod Kumar

  5. National Institute of Technical Teachers Training and Research, Chandigarh, India

    Mala Kalra

  6. National Institute of Technical Teachers Training and Research, Chandigarh, India

    S. L. Shimi

  7. National Institute of Technical Teachers Training and Research, Chandigarh, India

    Garima Saini

  8. National Institute of Technical Teachers Training and Research, Chandigarh, India

    Kanika Sharma

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Phuyal, S., Shrestha, S., Sharma, S., Subedi, R., Khan, S. (2024). Predictive Load Management Using IoT and Data Analytics. In: Challa, R.K., et al. Artificial Intelligence of Things. ICAIoT 2023. Communications in Computer and Information Science, vol 1930. Springer, Cham. https://doi.org/10.1007/978-3-031-48781-1_13

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  • DOI: https://doi.org/10.1007/978-3-031-48781-1_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48780-4

  • Online ISBN: 978-3-031-48781-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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