IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i4p739-d208440.html
   My bibliography  Save this article

Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder

Author

Listed:
  • Jin-Young Kim

    (Department of Computer Science, Yonsei University, Seoul 03722, Korea)

  • Sung-Bae Cho

    (Department of Computer Science, Yonsei University, Seoul 03722, Korea)

Abstract

As energy demand grows globally, the energy management system (EMS) is becoming increasingly important. Energy prediction is an essential component in the first step to create a management plan in EMS. Conventional energy prediction models focus on prediction performance, but in order to build an efficient system, it is necessary to predict energy demand according to various conditions. In this paper, we propose a method to predict energy demand in various situations using a deep learning model based on an autoencoder. This model consists of a projector that defines an appropriate state for a given situation and a predictor that forecasts energy demand from the defined state. The proposed model produces consumption predictions for 15, 30, 45, and 60 min with 60-min demand to date. In the experiments with household electric power consumption data for five years, this model not only has a better performance with a mean squared error of 0.384 than the conventional models, but also improves the capacity to explain the results of prediction by visualizing the state with t-SNE algorithm. Despite unsupervised representation learning, we confirm that the proposed model defines the state well and predicts the energy demand accordingly.

Suggested Citation

  • Jin-Young Kim & Sung-Bae Cho, 2019. "Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder," Energies, MDPI, vol. 12(4), pages 1-14, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:739-:d:208440
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/4/739/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/4/739/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cedric De Cauwer & Joeri Van Mierlo & Thierry Coosemans, 2015. "Energy Consumption Prediction for Electric Vehicles Based on Real-World Data," Energies, MDPI, vol. 8(8), pages 1-21, August.
    2. Donghyun Lee & Suna Kang & Jungwoo Shin, 2017. "Using Deep Learning Techniques to Forecast Environmental Consumption Level," Sustainability, MDPI, vol. 9(10), pages 1-17, October.
    3. Chengdong Li & Zixiang Ding & Dongbin Zhao & Jianqiang Yi & Guiqing Zhang, 2017. "Building Energy Consumption Prediction: An Extreme Deep Learning Approach," Energies, MDPI, vol. 10(10), pages 1-20, October.
    4. Karin Kandananond, 2011. "Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach," Energies, MDPI, vol. 4(8), pages 1-12, August.
    5. Ismet Ugursal, V., 2014. "Energy consumption, associated questions and some answers," Applied Energy, Elsevier, vol. 130(C), pages 783-792.
    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. Chongchong Xu & Zhicheng Liao & Chaojie Li & Xiaojun Zhou & Renyou Xie, 2022. "Review on Interpretable Machine Learning in Smart Grid," Energies, MDPI, vol. 15(12), pages 1-31, June.
    2. Salam, Abdulwahed & El Hibaoui, Abdelaaziz, 2021. "Energy consumption prediction model with deep inception residual network inspiration and LSTM," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 97-109.
    3. Gonçalves, Rui & Ribeiro, Vitor Miguel & Pereira, Fernando Lobo, 2023. "Variable Split Convolutional Attention: A novel Deep Learning model applied to the household electric power consumption," Energy, Elsevier, vol. 274(C).
    4. Joanna Henzel & Łukasz Wróbel & Marcin Fice & Marek Sikora, 2022. "Energy Consumption Forecasting for the Digital-Twin Model of the Building," Energies, MDPI, vol. 15(12), pages 1-21, June.
    5. Khan, Zulfiqar Ahmad & Hussain, Tanveer & Baik, Sung Wook, 2023. "Dual stream network with attention mechanism for photovoltaic power forecasting," Applied Energy, Elsevier, vol. 338(C).
    6. Seok-Jun Bu & Sung-Bae Cho, 2020. "Time Series Forecasting with Multi-Headed Attention-Based Deep Learning for Residential Energy Consumption," Energies, MDPI, vol. 13(18), pages 1-16, September.
    7. Wang, Meng & Wang, Wei & Wu, Lifeng, 2022. "Application of a new grey multivariate forecasting model in the forecasting of energy consumption in 7 regions of China," Energy, Elsevier, vol. 243(C).
    8. 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.
    9. Fath U Min Ullah & Noman Khan & Tanveer Hussain & Mi Young Lee & Sung Wook Baik, 2021. "Diving Deep into Short-Term Electricity Load Forecasting: Comparative Analysis and a Novel Framework," Mathematics, MDPI, vol. 9(6), pages 1-22, March.
    10. Zheng, Peijun & Zhou, Heng & Liu, Jiang & Nakanishi, Yosuke, 2023. "Interpretable building energy consumption forecasting using spectral clustering algorithm and temporal fusion transformers architecture," Applied Energy, Elsevier, vol. 349(C).
    11. Ijaz Ul Haq & Amin Ullah & Samee Ullah Khan & Noman Khan & Mi Young Lee & Seungmin Rho & Sung Wook Baik, 2021. "Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors," Mathematics, MDPI, vol. 9(6), pages 1-17, March.
    12. Abdulgani Kahraman & Mehmed Kantardzic & Muhammet Mustafa Kahraman & Muhammed Kotan, 2021. "A Data-Driven Multi-Regime Approach for Predicting Energy Consumption," Energies, MDPI, vol. 14(20), pages 1-17, October.
    13. 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.
    14. Katarzyna Poczeta & Elpiniki I. Papageorgiou & Vassilis C. Gerogiannis, 2020. "Fuzzy Cognitive Maps Optimization for Decision Making and Prediction," Mathematics, MDPI, vol. 8(11), pages 1-15, November.
    15. Chen, Jiandong & Xu, Chong & Shahbaz, Muhammad & Song, Malin, 2021. "Interaction determinants and projections of China’s energy consumption: 1997–2030," Applied Energy, Elsevier, vol. 283(C).
    16. Fernández, Joaquín Delgado & Menci, Sergio Potenciano & Lee, Chul Min & Rieger, Alexander & Fridgen, Gilbert, 2022. "Privacy-preserving federated learning for residential short-term load forecasting," Applied Energy, Elsevier, vol. 326(C).

    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. Prince Waqas Khan & Yongjun Kim & Yung-Cheol Byun & Sang-Joon Lee, 2021. "Influencing Factors Evaluation of Machine Learning-Based Energy Consumption Prediction," Energies, MDPI, vol. 14(21), pages 1-22, November.
    2. Atul Anand & L Suganthi, 2018. "Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand," Energies, MDPI, vol. 11(4), pages 1-15, March.
    3. Katsaprakakis, Dimitris Al & Voumvoulakis, Manolis, 2018. "A hybrid power plant towards 100% energy autonomy for the island of Sifnos, Greece. Perspectives created from energy cooperatives," Energy, Elsevier, vol. 161(C), pages 680-698.
    4. Namrye Son, 2021. "Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting," Sustainability, MDPI, vol. 13(22), pages 1-25, November.
    5. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    6. Cox, Brian & Bauer, Christian & Mendoza Beltran, Angelica & van Vuuren, Detlef P. & Mutel, Christopher L., 2020. "Life cycle environmental and cost comparison of current and future passenger cars under different energy scenarios," Applied Energy, Elsevier, vol. 269(C).
    7. Manfred Dollinger & Gerhard Fischerauer, 2023. "Physics-Based Prediction for the Consumption and Emissions of Passenger Vehicles and Light Trucks up to 2050," Energies, MDPI, vol. 16(8), pages 1-29, April.
    8. Nyoni, Thabani, 2019. "Modeling and forecasting demand for electricity in Zimbabwe using the Box-Jenkins ARIMA technique," MPRA Paper 96903, University Library of Munich, Germany.
    9. Nils Hooftman & Luis Oliveira & Maarten Messagie & Thierry Coosemans & Joeri Van Mierlo, 2016. "Environmental Analysis of Petrol, Diesel and Electric Passenger Cars in a Belgian Urban Setting," Energies, MDPI, vol. 9(2), pages 1-24, January.
    10. Uzlu, Ergun & Akpınar, Adem & Özturk, Hasan Tahsin & Nacar, Sinan & Kankal, Murat, 2014. "Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey," Energy, Elsevier, vol. 69(C), pages 638-647.
    11. Xingcai Zhou & Jiangyan Wang, 2021. "Panel semiparametric quantile regression neural network for electricity consumption forecasting," Papers 2103.00711, arXiv.org.
    12. Jiangbo Wang & Kai Liu & Toshiyuki Yamamoto, 2017. "Improving Electricity Consumption Estimation for Electric Vehicles Based on Sparse GPS Observations," Energies, MDPI, vol. 10(1), pages 1-12, January.
    13. Shailendra Singh & Abdulsalam Yassine, 2018. "Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting," Energies, MDPI, vol. 11(2), pages 1-26, February.
    14. Gonçalves, Rui & Ribeiro, Vitor Miguel & Pereira, Fernando Lobo, 2023. "Variable Split Convolutional Attention: A novel Deep Learning model applied to the household electric power consumption," Energy, Elsevier, vol. 274(C).
    15. Yun Bai & Zhenzhong Sun & Jun Deng & Lin Li & Jianyu Long & Chuan Li, 2017. "Manufacturing Quality Prediction Using Intelligent Learning Approaches: A Comparative Study," Sustainability, MDPI, vol. 10(1), pages 1-15, December.
    16. Katarzyna Poczeta & Elpiniki I. Papageorgiou & Vassilis C. Gerogiannis, 2020. "Fuzzy Cognitive Maps Optimization for Decision Making and Prediction," Mathematics, MDPI, vol. 8(11), pages 1-15, November.
    17. Fateme Dinmohammadi & Yuxuan Han & Mahmood Shafiee, 2023. "Predicting Energy Consumption in Residential Buildings Using Advanced Machine Learning Algorithms," Energies, MDPI, vol. 16(9), pages 1-23, April.
    18. Teresa Pamuła & Wiesław Pamuła, 2020. "Estimation of the Energy Consumption of Battery Electric Buses for Public Transport Networks Using Real-World Data and Deep Learning," Energies, MDPI, vol. 13(9), pages 1-17, May.
    19. Muhammad Waseem Ahmad & Anthony Mouraud & Yacine Rezgui & Monjur Mourshed, 2018. "Deep Highway Networks and Tree-Based Ensemble for Predicting Short-Term Building Energy Consumption," Energies, MDPI, vol. 11(12), pages 1-21, December.
    20. Abdurahman Alrobaie & Moncef Krarti, 2022. "A Review of Data-Driven Approaches for Measurement and Verification Analysis of Building Energy Retrofits," Energies, MDPI, vol. 15(21), pages 1-30, October.

    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:12:y:2019:i:4:p:739-:d:208440. 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.