INTEGRATED DATA ACQUISITION PLATFORM FOR EXPLAINABLE CONTROL IN DISTRICT HEATING SYSTEMS

Stevica Cvetković, Dušan Stojiljković, Ivan Ćirić, Rajko Turudija, Matija Špeletić

DOI Number
https://doi.org/10.22190/FUACR241129010C
First page
133
Last page
143

Abstract


This paper presents a comprehensive data acquisition platform designed for the intelligent management of District Heating Systems (DHS), aiming to optimize energy efficiency, reduce environmental impact, and minimize heat loss. A DHS is a centralized network that transfers thermal energy to multiple buildings via an insulated pipeline infrastructure. Traditional DHS configurations rely on PLCs and SCADA for data collection and heating control, but integrating real-time monitoring and advanced decision-making capabilities can significantly enhance system efficiency. Our Data Acquisition Platform aggregates data from diverse sources, including IoT sensors, weather stations, and smart meters, into a unified database for time-series analysis. The platform supports automated data retrieval through cron job scheduling and integrates with SCADA systems for remote data collection and monitoring. It is integral part of an intelligent DHS control approach named XAI4HEAT. This approach leverages explainable artificial intelligence algorithms and model-based predictive control to dynamically adjust heat supply based on demand and weather forecasts. Key benefits of this approach include improved load balancing, optimized energy distribution, and the potential integration of alternative energy sources.

Keywords

District heating system, data acquisition, predictive control, energy efficiency

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References


M. Zdravković, S. Cvetković, M. Ignjatović, I. Ćirić, D. Mitrović, M. Stojiljković, V. Nejković, D. Stojiljković, and R. Turudija. "XAI4HEAT: Towards Demand-Driven, AI Facilitated Management of District Heating Systems." In Conference on Information Technology and its Applications, pp. 23-34. Cham: Springer Nature Switzerland, 2024.

M. Zdravković, M., I. Ćirić, and M. Ignjatović, “Explainable heat demand forecasting for the novel control strategies of district heating systems,” Annual Reviews in Control, 53, pp.405-413, 2022.

S. Moustakidis, I. Meintanis, G. Halikias, and N. Karcanias, "An innovative control framework for district heating systems: Conceptualisation and preliminary results," Resources, vol. 8, no. 1, p. 27, 2019. doi: 10.3390/resources8010027.

J. van Dreven, V. Boeva, S. Abghari, H. Grahn, J. A. Koussa, and E. Motoasca, "Intelligent approaches to fault detection and diagnosis in district heating: Current trends, challenges, and opportunities," Electronics, vol. 12, no. 6, p. 1448, 2023. doi: 10.3390/electronics12061448.

H. B. Gunay, A. Ashouri, and W. Shen, "Load forecasting and equipment sequencing in a central heating and cooling plant: A case study," ASHRAE Trans., vol. 125, pp. 513–523, 2019.

J. Runge and E. Saloux, "A comparison of prediction and forecasting artificial intelligence models to estimate the future energy demand in a district heating system," Energy, vol. 269, p. 126661, 2023. doi: 10.1016/j.energy.2023.126661.

E. Saloux, J. Runge, and K. Zhang, "Field implementation of a predictive control strategy in district heating systems: A tale of two demonstration sites," in Energy Informatics, B. N. Jørgensen, L. C. P. da Silva, and Z. Ma, Eds. Cham: Springer, 2024, vol. 14468, doi: 10.1007/978-3-031-48652-4_21.

T. Van Oevelen, D. Vanhoudt, C. Johansson, and E. Smulders, "Testing and performance evaluation of the STORM controller in two demonstration sites," Energy, vol. 197, p. 117177, 2020. doi: 10.1016/j.energy.2020.117177.

N. Cotrufo, E. Saloux, J. M. Hardy, and J. A. Candanedo, "A practical artificial intelligence-based approach for predictive control in commercial and institutional buildings," Energy Build., vol. 206, p. 109563, 2019. doi: 10.1016/j.enbuild.2019.109563.

E. Saloux and J. A. Candanedo, "Model-based predictive control to minimize primary energy use in a solar district heating system with seasonal thermal energy storage," Appl. Energy, vol. 291, p. 116840, 2021. doi: 10.1016/j.apenergy.2021.116840.

E. Saloux, K. Zhang, and J. A. Candanedo, "Data-driven model-based control strategies to improve the cooling performance of commercial and institutional buildings," Buildings, vol. 13, no. 2, p. 474, 2023. doi: 10.3390/buildings13020474.

J. A. Candanedo, E. Saloux, J. M. Hardy, R. Platon, and V. Raissi-Dehkordi, "Preliminary assessment of a weather forecast tool for building operation," presented at the 5th Int. High Perform. Buildings Conf., Purdue, 2018.

T. Van Oevelen, T. Neven, A. Brès, R.-R. Schmidt, and D. Vanhoudt, "Testing and evaluation of a smart controller for reducing peak loads and return temperatures in district heating networks," Smart Energy, vol. 10, p. 100105, 2023. doi: 10.1016/j.segy.2023.100105.

E. Saloux, N. Cotrufo, and J. A. Candanedo, "A practical data-driven multi-model approach to model predictive control: Results from implementation in an institutional building," presented at the 6th Int. High Perform. Buildings Conf., Purdue, 2021.

H. Qi, Q. Ouyang, and L. Ma, "Application of artificial intelligence control in the control system of cooling and heating energy stations," Thermal Science, vol. 28, pp. 1321–1328, 2024. doi: 10.2298/TSCI2402321Q.

Y. Ding, T. Timoudas, Q. Wang, S. Chen, H. Brattebø, and N. Nord, "A study on data-driven hybrid heating load prediction methods in low-temperature district heating: An example for nursing homes in Nordic countries," Energy Convers. Manage., vol. 269, p. 116163, 2022. doi: 10.1016/j.enconman.2022.116163.

V. Vansovitš, A. Tepljakov, K. Vassiljeva, and E. Petlenkov, "Towards an intelligent control system for district heating plants: Design and implementation of a fuzzy logic based control loop," in Proc. IEEE 14th Int. Conf. Ind. Informatics (INDIN), 2016, pp. 405–410. doi: 10.1109/INDIN.2016.7819193.

M. Gong, Y. Liu, J. Sun, W. Xu, W. Li, C. Yan, and W. Fu, "Intelligent control of district heating system based on RDPG," Eng. Appl. Artif. Intell., vol. 129, p. 107672, 2024. doi: 10.1016/j.engappai.2023.107672.

M. A. Stošović, N. Radivojević, I. Jovanović, A. Petrušić, "Artificial neural networks application to prediction of electricity consumption," Facta Universitatis Series: Automatic Control and Robotics Vol. 20, No 1, pp. 33 – 42, 2021. https://doi.org/10.22190/FUACR201231003A

M. Simonović, V. Nikolić, E. Petrović, "Input vector impact on short-term heat loadnprediction of small district heating system,“ ," Facta Universitatis Series: Automatic Control and Robotics Vol. 15, No 2, pp. 95 – 103, 2016.

D. Stojiljković, I. Ćirić, S. Cvetković, R. Turudija, D. Srećković, “Data-Driven Approaches for Intelligent Control in District Heating Systems”, Proc. of XVII Int. Conf. SAUM 2024, Niš, Serbia, November 14-15, 2024.

M. Khorasani, M. Abdou, and J. Hernández Fernández, “Web Application Development with Streamlit,” Software Development, pp.498-507, 2022.




DOI: https://doi.org/10.22190/FUACR241129010C

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