IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v147y2015icp10-19.html
   My bibliography  Save this article

Short-term smart learning electrical load prediction algorithm for home energy management systems

Author

Listed:
  • El-Baz, Wessam
  • Tzscheutschler, Peter

Abstract

Energy management system (EMS) within buildings has always been one of the main approaches for an automated demand side management (DSM). These energy management systems are supposed to increase load flexibility to fit more the generation from renewable energies and micro co-generation devices. For EMS to operate efficiently, it must learn ahead about the available supply and demand so that it can work on supply–demand matching and minimizing the imports from the grid and running costs. This article presents a simple efficient day-ahead electrical load prediction approach for any EMS. In comparison to other approaches, the presented algorithm was designed to be apart of any generic EMS and it does not require to be associated with a prepared statistical or historical databases, or even to get connected to any kinds of sensors. The proposed algorithm was tested over the data of 25 households in Austria and the results have shown an error range that goes down to 8.2% as an initial prediction.

Suggested Citation

  • El-Baz, Wessam & Tzscheutschler, Peter, 2015. "Short-term smart learning electrical load prediction algorithm for home energy management systems," Applied Energy, Elsevier, vol. 147(C), pages 10-19.
    • Handle: RePEc:eee:appene:v:147:y:2015:i:c:p:10-19
    DOI: 10.1016/j.apenergy.2015.01.122
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261915001592
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2015.01.122?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Di Giorgio, Alessandro & Liberati, Francesco, 2014. "Near real time load shifting control for residential electricity prosumers under designed and market indexed pricing models," Applied Energy, Elsevier, vol. 128(C), pages 119-132.
    2. Tzafestas, S.G. & Dalianis, P.J. & Anthopoulos, G., 1996. "On the overtraining phenomenon of backpropagation neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 40(5), pages 507-521.
    3. Cassola, Federico & Burlando, Massimiliano, 2012. "Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output," Applied Energy, Elsevier, vol. 99(C), pages 154-166.
    4. Andersen, F.M. & Larsen, H.V. & Gaardestrup, R.B., 2013. "Long term forecasting of hourly electricity consumption in local areas in Denmark," Applied Energy, Elsevier, vol. 110(C), pages 147-162.
    5. Warren, Peter, 2014. "A review of demand-side management policy in the UK," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 941-951.
    6. McPherson, Madeleine & Karney, Bryan, 2014. "Long-term scenario alternatives and their implications: LEAP model application of Panama׳s electricity sector," Energy Policy, Elsevier, vol. 68(C), pages 146-157.
    7. Gottwalt, Sebastian & Ketter, Wolfgang & Block, Carsten & Collins, John & Weinhardt, Christof, 2011. "Demand side management—A simulation of household behavior under variable prices," Energy Policy, Elsevier, vol. 39(12), pages 8163-8174.
    8. 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.
    9. Di Giorgio, Alessandro & Pimpinella, Laura, 2012. "An event driven Smart Home Controller enabling consumer economic saving and automated Demand Side Management," Applied Energy, Elsevier, vol. 96(C), pages 92-103.
    10. Zúñiga, K.V. & Castilla, I. & Aguilar, R.M., 2014. "Using fuzzy logic to model the behavior of residential electrical utility customers," Applied Energy, Elsevier, vol. 115(C), pages 384-393.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ahmad, Tanveer & Chen, Huanxin, 2019. "Deep learning for multi-scale smart energy forecasting," Energy, Elsevier, vol. 175(C), pages 98-112.
    2. Haben, Stephen & Giasemidis, Georgios & Ziel, Florian & Arora, Siddharth, 2019. "Short term load forecasting and the effect of temperature at the low voltage level," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1469-1484.
    3. Baldi, Simone & Yuan, Shuai & Endel, Petr & Holub, Ondrej, 2016. "Dual estimation: Constructing building energy models from data sampled at low rate," Applied Energy, Elsevier, vol. 169(C), pages 81-92.
    4. Murray, D.M. & Liao, J. & Stankovic, L. & Stankovic, V., 2016. "Understanding usage patterns of electric kettle and energy saving potential," Applied Energy, Elsevier, vol. 171(C), pages 231-242.
    5. Eissa, M.M., 2018. "First time real time incentive demand response program in smart grid with “i-Energy” management system with different resources," Applied Energy, Elsevier, vol. 212(C), pages 607-621.
    6. Davut Solyali, 2020. "A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus," Sustainability, MDPI, vol. 12(9), pages 1-34, April.
    7. Nesamalar, J. Jeslin Drusila & Venkatesh, P. & Raja, S. Charles, 2016. "Energy management by generator rescheduling in congestive deregulated power system," Applied Energy, Elsevier, vol. 171(C), pages 357-371.
    8. Holger C. Hesse & Michael Schimpe & Daniel Kucevic & Andreas Jossen, 2017. "Lithium-Ion Battery Storage for the Grid—A Review of Stationary Battery Storage System Design Tailored for Applications in Modern Power Grids," Energies, MDPI, vol. 10(12), pages 1-42, December.
    9. Ogunjuyigbe, A.S.O. & Ayodele, T.R. & Akinola, O.A., 2017. "User satisfaction-induced demand side load management in residential buildings with user budget constraint," Applied Energy, Elsevier, vol. 187(C), pages 352-366.
    10. Awais Manzoor & Nadeem Javaid & Ibrar Ullah & Wadood Abdul & Ahmad Almogren & Atif Alamri, 2017. "An Intelligent Hybrid Heuristic Scheme for Smart Metering based Demand Side Management in Smart Homes," Energies, MDPI, vol. 10(9), pages 1-28, August.
    11. Sara Ayub & Shahrin Md Ayob & Chee Wei Tan & Saad M. Arif & Muhammad Taimoor & Lubna Aziz & Abba Lawan Bukar & Qasem Al-Tashi & Razman Ayop, 2023. "Multi-Criteria Energy Management with Preference Induced Load Scheduling Using Grey Wolf Optimizer," Sustainability, MDPI, vol. 15(2), pages 1-38, January.
    12. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
    13. Zhao, Xueyuan & Gao, Weijun & Qian, Fanyue & Ge, Jian, 2021. "Electricity cost comparison of dynamic pricing model based on load forecasting in home energy management system," Energy, Elsevier, vol. 229(C).
    14. Alobaidi, Mohammad H. & Chebana, Fateh & Meguid, Mohamed A., 2018. "Robust ensemble learning framework for day-ahead forecasting of household based energy consumption," Applied Energy, Elsevier, vol. 212(C), pages 997-1012.
    15. Eissa, M.M., 2019. "Developing incentive demand response with commercial energy management system (CEMS) based on diffusion model, smart meters and new communication protocol," Applied Energy, Elsevier, vol. 236(C), pages 273-292.
    16. Hribar, Rok & Potočnik, Primož & Šilc, Jurij & Papa, Gregor, 2019. "A comparison of models for forecasting the residential natural gas demand of an urban area," Energy, Elsevier, vol. 167(C), pages 511-522.

    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. Boßmann, Tobias & Eser, Eike Johannes, 2016. "Model-based assessment of demand-response measures—A comprehensive literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1637-1656.
    2. Yan, Xing & Ozturk, Yusuf & Hu, Zechun & Song, Yonghua, 2018. "A review on price-driven residential demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 411-419.
    3. Fridgen, Gilbert & Kahlen, Micha & Ketter, Wolfgang & Rieger, Alexander & Thimmel, Markus, 2018. "One rate does not fit all: An empirical analysis of electricity tariffs for residential microgrids," Applied Energy, Elsevier, vol. 210(C), pages 800-814.
    4. Rieger, Alexander & Thummert, Robert & Fridgen, Gilbert & Kahlen, Micha & Ketter, Wolfgang, 2016. "Estimating the benefits of cooperation in a residential microgrid: A data-driven approach," Applied Energy, Elsevier, vol. 180(C), pages 130-141.
    5. Blaschke, Maximilian J., 2022. "Dynamic pricing of electricity: Enabling demand response in domestic households," Energy Policy, Elsevier, vol. 164(C).
    6. Kobus, Charlotte B.A. & Klaassen, Elke A.M. & Mugge, Ruth & Schoormans, Jan P.L., 2015. "A real-life assessment on the effect of smart appliances for shifting households’ electricity demand," Applied Energy, Elsevier, vol. 147(C), pages 335-343.
    7. Mauser, Ingo & Müller, Jan & Allerding, Florian & Schmeck, Hartmut, 2016. "Adaptive building energy management with multiple commodities and flexible evolutionary optimization," Renewable Energy, Elsevier, vol. 87(P2), pages 911-921.
    8. Xu, Lei & Hou, Lei & Zhu, Zhenyu & Li, Yu & Liu, Jiaquan & Lei, Ting & Wu, Xingguang, 2021. "Mid-term prediction of electrical energy consumption for crude oil pipelines using a hybrid algorithm of support vector machine and genetic algorithm," Energy, Elsevier, vol. 222(C).
    9. Märkle-Huß, Joscha & Feuerriegel, Stefan & Neumann, Dirk, 2018. "Large-scale demand response and its implications for spot prices, load and policies: Insights from the German-Austrian electricity market," Applied Energy, Elsevier, vol. 210(C), pages 1290-1298.
    10. Vaclav Kaczmarczyk & Zdenek Bradac & Petr Fiedler, 2017. "A Heuristic Algorithm to Compute Multimodal Criterial Function Weights for Demand Management in Residential Areas," Energies, MDPI, vol. 10(7), pages 1-28, July.
    11. Eissa, M.M., 2019. "Developing incentive demand response with commercial energy management system (CEMS) based on diffusion model, smart meters and new communication protocol," Applied Energy, Elsevier, vol. 236(C), pages 273-292.
    12. Woo, C.K. & Sreedharan, P. & Hargreaves, J. & Kahrl, F. & Wang, J. & Horowitz, I., 2014. "A review of electricity product differentiation," Applied Energy, Elsevier, vol. 114(C), pages 262-272.
    13. Yilmaz, S. & Rinaldi, A. & Patel, M.K., 2020. "DSM interactions: What is the impact of appliance energy efficiency measures on the demand response (peak load management)?," Energy Policy, Elsevier, vol. 139(C).
    14. Haider, Haider Tarish & See, Ong Hang & Elmenreich, Wilfried, 2016. "A review of residential demand response of smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 166-178.
    15. Di Giorgio, Alessandro & Liberati, Francesco, 2014. "Near real time load shifting control for residential electricity prosumers under designed and market indexed pricing models," Applied Energy, Elsevier, vol. 128(C), pages 119-132.
    16. Francesco Liberati & Alessandro Di Giorgio, 2017. "Economic Model Predictive and Feedback Control of a Smart Grid Prosumer Node," Energies, MDPI, vol. 11(1), pages 1-23, December.
    17. Whittle, Colin & Jones, Christopher R. & While, Aidan, 2020. "Empowering householders: Identifying predictors of intentions to use a home energy management system in the United Kingdom," Energy Policy, Elsevier, vol. 139(C).
    18. Pin Li & Jinsuo Zhang, 2019. "Is China’s Energy Supply Sustainable? New Research Model Based on the Exponential Smoothing and GM(1,1) Methods," Energies, MDPI, vol. 12(2), pages 1-30, January.
    19. Heo, SungKu & Byun, Jaewon & Ifaei, Pouya & Ko, Jaerak & Ha, Byeongmin & Hwangbo, Soonho & Yoo, ChangKyoo, 2024. "Towards mega-scale decarbonized industrial park (Mega-DIP): Generative AI-driven techno-economic and environmental assessment of renewable and sustainable energy utilization in petrochemical industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    20. Meyabadi, A. Fattahi & Deihimi, M.H., 2017. "A review of demand-side management: Reconsidering theoretical framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 367-379.

    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:eee:appene:v:147:y:2015:i:c:p:10-19. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.