IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i12p2840-d1411663.html
   My bibliography  Save this article

Weather-Based Prediction of Power Consumption in District Heating Network: Case Study in Finland

Author

Listed:
  • Aleksei Vakhnin

    (Department of Environmental and Biological Sciences, University of Eastern Finland, Yliopistonranta 1E, 70210 Kuopio, Finland)

  • Ivan Ryzhikov

    (Department of Environmental and Biological Sciences, University of Eastern Finland, Yliopistonranta 1E, 70210 Kuopio, Finland)

  • Christina Brester

    (Department of Environmental and Biological Sciences, University of Eastern Finland, Yliopistonranta 1E, 70210 Kuopio, Finland)

  • Harri Niska

    (Department of Environmental and Biological Sciences, University of Eastern Finland, Yliopistonranta 1E, 70210 Kuopio, Finland)

  • Mikko Kolehmainen

    (Department of Environmental and Biological Sciences, University of Eastern Finland, Yliopistonranta 1E, 70210 Kuopio, Finland)

Abstract

Accurate prediction of energy consumption in district heating systems plays an important role in supporting effective and clean energy production and distribution in dense urban areas. Predictive models are needed for flexible and cost-effective operation of energy production and usage, e.g., using peak shaving or load shifting to compensate for heat losses in the pipeline. This helps to avoid exceedance of power plant capacity. The purpose of this study is to automate the process of building machine learning (ML) models to solve a short-term power demand prediction problem. The dataset contains a district heating network’s measured hourly power consumption and ambient temperature for 415 days. In this paper, we propose a hybrid evolutionary-based algorithm, named GA-SHADE, for the simultaneous optimization of ML models and feature selection. The GA-SHADE algorithm is a hybrid algorithm consisting of a Genetic Algorithm (GA) and success-history-based parameter adaptation for differential evolution (SHADE). The results of the numerical experiments show that the proposed GA-SHADE algorithm allows the identification of simplified ML models with good prediction performance in terms of the optimized feature subset and model hyperparameters. The main contributions of the study are (1) using the proposed GA-SHADE, ML models with varying numbers of features and performance are obtained. (2) The proposed GA-SHADE algorithm self-adapts during operation and has only one control parameter. There is no fine-tuning required before execution. (3) Due to the evolutionary nature of the algorithm, it is not sensitive to the number of features and hyperparameters to be optimized in ML models. In conclusion, this study confirms that each optimized ML model uses a unique set and number of features. Out of the six ML models considered, SVR and NN are better candidates and have demonstrated the best performance across several metrics. All numerical experiments were compared against the measurements and proven by the standard statistical tests.

Suggested Citation

  • Aleksei Vakhnin & Ivan Ryzhikov & Christina Brester & Harri Niska & Mikko Kolehmainen, 2024. "Weather-Based Prediction of Power Consumption in District Heating Network: Case Study in Finland," Energies, MDPI, vol. 17(12), pages 1-32, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:12:p:2840-:d:1411663
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/12/2840/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/12/2840/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Crompton, Paul & Wu, Yanrui, 2005. "Energy consumption in China: past trends and future directions," Energy Economics, Elsevier, vol. 27(1), pages 195-208, January.
    2. Kumaran Kadirgama & Omar I. Awad & M. N. Mohammed & Hai Tao & Ali A. H. Karah Bash, 2023. "Sustainable Green Energy Management: Optimizing Scheduling of Multi-Energy Systems Considered Energy Cost and Emission Using Attractive Repulsive Shuffled Frog-Leaping," Sustainability, MDPI, vol. 15(14), pages 1-19, July.
    3. Mohamed, Zaid & Bodger, Pat, 2005. "Forecasting electricity consumption in New Zealand using economic and demographic variables," Energy, Elsevier, vol. 30(10), pages 1833-1843.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sun-Youn Shin & Han-Gyun Woo, 2022. "Energy Consumption Forecasting in Korea Using Machine Learning Algorithms," Energies, MDPI, vol. 15(13), pages 1-20, July.
    2. Huang, Liqiao & Liao, Qi & Qiu, Rui & Liang, Yongtu & Long, Yin, 2021. "Prediction-based analysis on power consumption gap under long-term emergency: A case in China under COVID-19," Applied Energy, Elsevier, vol. 283(C).
    3. Shao, Zhen & Gao, Fei & Zhang, Qiang & Yang, Shan-Lin, 2015. "Multivariate statistical and similarity measure based semiparametric modeling of the probability distribution: A novel approach to the case study of mid-long term electricity consumption forecasting i," Applied Energy, Elsevier, vol. 156(C), pages 502-518.
    4. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    5. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    6. Bloch, Harry & Rafiq, Shuddhasattwa & Salim, Ruhul, 2015. "Economic growth with coal, oil and renewable energy consumption in China: Prospects for fuel substitution," Economic Modelling, Elsevier, vol. 44(C), pages 104-115.
    7. Weiqiang Zhu & Yun Zhang, 2024. "Household Energy Clean Transition Mechanisms under Market Failures: A Government Financing Perspective," Sustainability, MDPI, vol. 16(13), pages 1-29, July.
    8. Xu, Bin & Lin, Boqiang, 2018. "Do we really understand the development of China's new energy industry?," Energy Economics, Elsevier, vol. 74(C), pages 733-745.
    9. A. Azadeh & M. Saberi & A. Gitiforouz, 2013. "An integrated fuzzy mathematical model and principal component analysis algorithm for forecasting uncertain trends of electricity consumption," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(4), pages 2163-2176, June.
    10. Pappas, S.Sp. & Ekonomou, L. & Karamousantas, D.Ch. & Chatzarakis, G.E. & Katsikas, S.K. & Liatsis, P., 2008. "Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models," Energy, Elsevier, vol. 33(9), pages 1353-1360.
    11. Andreas Lenk & Marcus Vogt & Christoph Herrmann, 2024. "An Approach to Predicting Energy Demand Within Automobile Production Using the Temporal Fusion Transformer Model," Energies, MDPI, vol. 18(1), pages 1-34, December.
    12. Hong, Junjie & Shi, Fangyuan & Zheng, Yuhan, 2023. "Does network infrastructure construction reduce energy intensity? Based on the “Broadband China” strategy," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    13. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    14. Marvão Pereira, Alfredo & Marvão Pereira, Rui Manuel, 2010. "Is fuel-switching a no-regrets environmental policy? VAR evidence on carbon dioxide emissions, energy consumption and economic performance in Portugal," Energy Economics, Elsevier, vol. 32(1), pages 227-242, January.
    15. Psiloglou, B.E. & Giannakopoulos, C. & Majithia, S. & Petrakis, M., 2009. "Factors affecting electricity demand in Athens, Greece and London, UK: A comparative assessment," Energy, Elsevier, vol. 34(11), pages 1855-1863.
    16. Xu, Yang-Jie & Li, Guo-Xiu & Sun, Zuo-Yu, 2016. "Development of biodiesel industry in China: Upon the terms of production and consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 318-330.
    17. Kim, Hyun Seok & Baek, Jungho, 2013. "Assessing dynamics of crude oil import demand in Korea," Economic Modelling, Elsevier, vol. 35(C), pages 260-263.
    18. Andersen, Thomas Barnebeck & Barslund, Mikkel & Hansen, Casper Worm & Harr, Thomas & Jensen, Peter Sandholt, 2014. "How much did China's WTO accession increase economic growth in resource-rich countries?," China Economic Review, Elsevier, vol. 30(C), pages 16-26.
    19. Chang, Tzu-Pu & Hu, Jin-Li, 2010. "Total-factor energy productivity growth, technical progress, and efficiency change: An empirical study of China," Applied Energy, Elsevier, vol. 87(10), pages 3262-3270, October.
    20. Li, Sisi & Khan, Sufyan Ullah & Yao, Yao & Chen, George S. & Zhang, Lin & Salim, Ruhul & Huo, Jiaying, 2022. "Estimating the long-run crude oil demand function of China: Some new evidence and policy options," Energy Policy, Elsevier, vol. 170(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:12:p:2840-:d:1411663. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.