IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i10p2672-d362866.html
   My bibliography  Save this article

HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning

Author

Listed:
  • Ivana Kiprijanovska

    (Department of Intelligent Systems, Jožef Stefan Institute, 1000 Ljubljana, Slovenia
    Jožef Stefan Postgraduate School, Information and Communication Technologies, 1000 Ljubljana, Slovenia
    The first two authors have contributed equally to this work.)

  • Simon Stankoski

    (Department of Intelligent Systems, Jožef Stefan Institute, 1000 Ljubljana, Slovenia
    Jožef Stefan Postgraduate School, Information and Communication Technologies, 1000 Ljubljana, Slovenia
    The first two authors have contributed equally to this work.)

  • Igor Ilievski

    (ITS Iskratel, ITS Softver Centar, 1000 Skopje, North Macedonia)

  • Slobodan Jovanovski

    (ITS Iskratel, ITS Softver Centar, 1000 Skopje, North Macedonia)

  • Matjaž Gams

    (Department of Intelligent Systems, Jožef Stefan Institute, 1000 Ljubljana, Slovenia
    Jožef Stefan Postgraduate School, Information and Communication Technologies, 1000 Ljubljana, Slovenia)

  • Hristijan Gjoreski

    (Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University, 1000 Skopje, North Macedonia)

Abstract

Short-term load forecasting is integral to the energy planning sector. Various techniques have been employed to achieve effective operation of power systems and efficient market management. We present a scalable system for day-ahead household electrical energy consumption forecasting, named HousEEC. The proposed forecasting method is based on a deep residual neural network, and integrates multiple sources of information by extracting features from (i) contextual data (weather, calendar), and (ii) the historical load of the particular household and all households present in the dataset. Additionally, we compute novel domain-specific time-series features that allow the system to better model the pattern of energy consumption of the household. The experimental analysis and evaluation were performed on one of the most extensive datasets for household electrical energy consumption, Pecan Street, containing almost four years of data. Multiple test cases show that the proposed model provides accurate load forecasting results, achieving a root-mean-square error score of 0.44 kWh and mean absolute error score of 0.23 kWh, for short-term load forecasting for 300 households. The analysis showed that, for hourly forecasting, our model had 8% error (22 kWh), which is 4 percentage points better than the benchmark model. The daily analysis showed that our model had 2% error (131 kWh), which is significantly less compared to the benchmark model, with 6% error (360 kWh).

Suggested Citation

  • Ivana Kiprijanovska & Simon Stankoski & Igor Ilievski & Slobodan Jovanovski & Matjaž Gams & Hristijan Gjoreski, 2020. "HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning," Energies, MDPI, vol. 13(10), pages 1-29, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:10:p:2672-:d:362866
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/10/2672/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/10/2672/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ricardo Vazquez & Hortensia Amaris & Monica Alonso & Gregorio Lopez & Jose Ignacio Moreno & Daniel Olmeda & Javier Coca, 2017. "Assessment of an Adaptive Load Forecasting Methodology in a Smart Grid Demonstration Project," Energies, MDPI, vol. 10(2), pages 1-23, February.
    2. Ben Taieb, Souhaib & Hyndman, Rob J., 2014. "A gradient boosting approach to the Kaggle load forecasting competition," International Journal of Forecasting, Elsevier, vol. 30(2), pages 382-394.
    3. Rainer Göb & Kristina Lurz & Antonio Pievatolo, 2013. "Electrical load forecasting by exponential smoothing with covariates," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 29(6), pages 629-645, November.
    4. Rahman, Aowabin & Srikumar, Vivek & Smith, Amanda D., 2018. "Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 212(C), pages 372-385.
    5. Miguel López & Carlos Sans & Sergio Valero & Carolina Senabre, 2019. "Classification of Special Days in Short-Term Load Forecasting: The Spanish Case Study," Energies, MDPI, vol. 12(7), pages 1-31, April.
    6. Sholeh Hadi Pramono & Mahdin Rohmatillah & Eka Maulana & Rini Nur Hasanah & Fakhriy Hario, 2019. "Deep Learning-Based Short-Term Load Forecasting for Supporting Demand Response Program in Hybrid Energy System," Energies, MDPI, vol. 12(17), pages 1-16, August.
    7. Berk, K. & Hoffmann, A. & Müller, A., 2018. "Probabilistic forecasting of industrial electricity load with regime switching behavior," International Journal of Forecasting, Elsevier, vol. 34(2), pages 147-162.
    8. Liang, Yi & Niu, Dongxiao & Hong, Wei-Chiang, 2019. "Short term load forecasting based on feature extraction and improved general regression neural network model," Energy, Elsevier, vol. 166(C), pages 653-663.
    9. Hong, Tao & Pinson, Pierre & Fan, Shu, 2014. "Global Energy Forecasting Competition 2012," International Journal of Forecasting, Elsevier, vol. 30(2), pages 357-363.
    10. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    11. 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.
    12. Shree Krishna Acharya & Young-Min Wi & Jaehee Lee, 2019. "Short-Term Load Forecasting for a Single Household Based on Convolution Neural Networks Using Data Augmentation," Energies, MDPI, vol. 12(18), pages 1-19, September.
    13. Ruijin Zhu & Weilin Guo & Xuejiao Gong, 2019. "Short-Term Load Forecasting for CCHP Systems Considering the Correlation between Heating, Gas and Electrical Loads Based on Deep Learning," Energies, MDPI, vol. 12(17), pages 1-18, August.
    14. Chujie Tian & Jian Ma & Chunhong Zhang & Panpan Zhan, 2018. "A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network," Energies, MDPI, vol. 11(12), pages 1-13, December.
    15. Hong, Tao & Wang, Pu & White, Laura, 2015. "Weather station selection for electric load forecasting," International Journal of Forecasting, Elsevier, vol. 31(2), pages 286-295.
    16. Nedellec, Raphael & Cugliari, Jairo & Goude, Yannig, 2014. "GEFCom2012: Electric load forecasting and backcasting with semi-parametric models," International Journal of Forecasting, Elsevier, vol. 30(2), pages 375-381.
    17. Magnus Dahl & Adam Brun & Oliver S. Kirsebom & Gorm B. Andresen, 2018. "Improving Short-Term Heat Load Forecasts with Calendar and Holiday Data," Energies, MDPI, vol. 11(7), pages 1-16, June.
    18. Katarzyna Maciejowska & Weronika Nitka & Tomasz Weron, 2019. "Day-Ahead vs. Intraday—Forecasting the Price Spread to Maximize Economic Benefits," Energies, MDPI, vol. 12(4), pages 1-15, February.
    19. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    20. Takeda, Hisashi & Tamura, Yoshiyasu & Sato, Seisho, 2016. "Using the ensemble Kalman filter for electricity load forecasting and analysis," Energy, Elsevier, vol. 104(C), pages 184-198.
    21. Huiting Zheng & Jiabin Yuan & Long Chen, 2017. "Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation," Energies, MDPI, vol. 10(8), pages 1-20, August.
    22. Lusis, Peter & Khalilpour, Kaveh Rajab & Andrew, Lachlan & Liebman, Ariel, 2017. "Short-term residential load forecasting: Impact of calendar effects and forecast granularity," Applied Energy, Elsevier, vol. 205(C), pages 654-669.
    23. BARTEN, Anton P., 1987. "The coefficient of determination for regression without a constant term," LIDAM Reprints CORE 766, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    24. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2019. "Single and Multi-Sequence Deep Learning Models for Short and Medium Term Electric Load Forecasting," Energies, MDPI, vol. 12(1), pages 1-21, January.
    25. Seyedeh Narjes Fallah & Mehdi Ganjkhani & Shahaboddin Shamshirband & Kwok-wing Chau, 2019. "Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview," Energies, MDPI, vol. 12(3), pages 1-21, January.
    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. Fang, Lei & He, Bin, 2023. "A deep learning framework using multi-feature fusion recurrent neural networks for energy consumption forecasting," Applied Energy, Elsevier, vol. 348(C).
    2. Antonio Gabaldón & María Carmen Ruiz-Abellón & Luis Alfredo Fernández-Jiménez, 2022. "Guest Editorial: Special Issue on Short-Term Load Forecasting 2019, Results and Future Perspectives," Energies, MDPI, vol. 15(24), pages 1-5, December.
    3. Nafeesa Javed & Muhammad Javaid Iqbal & Sohail Masood & Laiba Rehman & Saba Ramzan, 2024. "The Effect of Climate Change on Energy Consumption Using Smart Meter Dataset," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 13(1), pages 777-783.
    4. Gwanggil Jeon, 2022. "Artificial Intelligence Approaches for Energies," Energies, MDPI, vol. 15(18), pages 1-3, September.
    5. Deyslen Mariano-Hernández & Luis Hernández-Callejo & Martín Solís & Angel Zorita-Lamadrid & Oscar Duque-Pérez & Luis Gonzalez-Morales & Felix Santos García & Alvaro Jaramillo-Duque & Adalberto Ospino-, 2022. "Analysis of the Integration of Drift Detection Methods in Learning Algorithms for Electrical Consumption Forecasting in Smart Buildings," Sustainability, MDPI, vol. 14(10), pages 1-14, May.
    6. Umar Javed & Khalid Ijaz & Muhammad Jawad & Ejaz A. Ansari & Noman Shabbir & Lauri Kütt & Oleksandr Husev, 2021. "Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis," Energies, MDPI, vol. 14(17), pages 1-22, September.
    7. 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.
    8. Akylas Stratigakos & Athanasios Bachoumis & Vasiliki Vita & Elias Zafiropoulos, 2021. "Short-Term Net Load Forecasting with Singular Spectrum Analysis and LSTM Neural Networks," Energies, MDPI, vol. 14(14), pages 1-13, July.

    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. Seyedeh Narjes Fallah & Mehdi Ganjkhani & Shahaboddin Shamshirband & Kwok-wing Chau, 2019. "Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview," Energies, MDPI, vol. 12(3), pages 1-21, January.
    2. 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.
    3. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    4. Zhang, Ning & Li, Zhiying & Zou, Xun & Quiring, Steven M., 2019. "Comparison of three short-term load forecast models in Southern California," Energy, Elsevier, vol. 189(C).
    5. Wang, Pu & Liu, Bidong & Hong, Tao, 2016. "Electric load forecasting with recency effect: A big data approach," International Journal of Forecasting, Elsevier, vol. 32(3), pages 585-597.
    6. Luo, Jian & Hong, Tao & Fang, Shu-Cherng, 2018. "Benchmarking robustness of load forecasting models under data integrity attacks," International Journal of Forecasting, Elsevier, vol. 34(1), pages 89-104.
    7. Antonio Gabaldón & María Carmen Ruiz-Abellón & Luis Alfredo Fernández-Jiménez, 2022. "Guest Editorial: Special Issue on Short-Term Load Forecasting 2019, Results and Future Perspectives," Energies, MDPI, vol. 15(24), pages 1-5, December.
    8. Moreno-Carbonell, Santiago & Sánchez-Úbeda, Eugenio F. & Muñoz, Antonio, 2020. "Rethinking weather station selection for electric load forecasting using genetic algorithms," International Journal of Forecasting, Elsevier, vol. 36(2), pages 695-712.
    9. Akylas Stratigakos & Athanasios Bachoumis & Vasiliki Vita & Elias Zafiropoulos, 2021. "Short-Term Net Load Forecasting with Singular Spectrum Analysis and LSTM Neural Networks," Energies, MDPI, vol. 14(14), pages 1-13, July.
    10. Kamal Chapagain & Somsak Kittipiyakul, 2018. "Performance Analysis of Short-Term Electricity Demand with Atmospheric Variables," Energies, MDPI, vol. 11(4), pages 1-34, April.
    11. Tartakovsky, Alexandre M. & Ma, Tong & Barajas-Solano, David A. & Tipireddy, Ramakrishna, 2023. "Physics-informed Gaussian process regression for states estimation and forecasting in power grids," International Journal of Forecasting, Elsevier, vol. 39(2), pages 967-980.
    12. Pinheiro, Marco G. & Madeira, Sara C. & Francisco, Alexandre P., 2023. "Short-term electricity load forecasting—A systematic approach from system level to secondary substations," Applied Energy, Elsevier, vol. 332(C).
    13. Makridakis, Spyros & Hyndman, Rob J. & Petropoulos, Fotios, 2020. "Forecasting in social settings: The state of the art," International Journal of Forecasting, Elsevier, vol. 36(1), pages 15-28.
    14. Thomas Steens & Jan-Simon Telle & Benedikt Hanke & Karsten von Maydell & Carsten Agert & Gian-Luca Di Modica & Bernd Engel & Matthias Grottke, 2021. "A Forecast-Based Load Management Approach for Commercial Buildings Demonstrated on an Integration of BEV," Energies, MDPI, vol. 14(12), pages 1-25, June.
    15. Moting Su & Zongyi Zhang & Ye Zhu & Donglan Zha, 2019. "Data-Driven Natural Gas Spot Price Forecasting with Least Squares Regression Boosting Algorithm," Energies, MDPI, vol. 12(6), pages 1-13, March.
    16. Sobhani, Masoud & Hong, Tao & Martin, Claude, 2020. "Temperature anomaly detection for electric load forecasting," International Journal of Forecasting, Elsevier, vol. 36(2), pages 324-333.
    17. Barman, Mayur & Dev Choudhury, Nalin Behari, 2019. "Season specific approach for short-term load forecasting based on hybrid FA-SVM and similarity concept," Energy, Elsevier, vol. 174(C), pages 886-896.
    18. Zang, Haixiang & Xu, Ruiqi & Cheng, Lilin & Ding, Tao & Liu, Ling & Wei, Zhinong & Sun, Guoqiang, 2021. "Residential load forecasting based on LSTM fusing self-attention mechanism with pooling," Energy, Elsevier, vol. 229(C).
    19. Sarah Hadri & Mehdi Najib & Mohamed Bakhouya & Youssef Fakhri & Mohamed El Arroussi, 2021. "Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings," Energies, MDPI, vol. 14(18), pages 1-17, September.
    20. Hong, Tao & Wang, Pu & White, Laura, 2015. "Weather station selection for electric load forecasting," International Journal of Forecasting, Elsevier, vol. 31(2), pages 286-295.

    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:13:y:2020:i:10:p:2672-:d:362866. 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.