IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i10p2640-d361430.html
   My bibliography  Save this article

Short-Term Load Forecasting Algorithm Using a Similar Day Selection Method Based on Reinforcement Learning

Author

Listed:
  • Rae-Jun Park

    (Department of Electrical Engineering, Soongsil University, Seoul 06978, Korea)

  • Kyung-Bin Song

    (Department of Electrical Engineering, Soongsil University, Seoul 06978, Korea)

  • Bo-Sung Kwon

    (Department of Electrical Engineering, Soongsil University, Seoul 06978, Korea)

Abstract

Short-term load forecasting (STLF) is very important for planning and operating power systems and markets. Various algorithms have been developed for STLF. However, numerous utilities still apply additional correction processes, which depend on experienced professionals. In this study, an STLF algorithm that uses a similar day selection method based on reinforcement learning is proposed to substitute the dependence on an expert’s experience. The proposed algorithm consists of the selection of similar days, which is based on the reinforcement algorithm, and the STLF, which is based on an artificial neural network. The proposed similar day selection model based on the reinforcement learning algorithm is developed based on the Deep Q-Network technique, which is a value-based reinforcement learning algorithm. The proposed similar day selection model and load forecasting model are tested using the measured load and meteorological data for Korea. The proposed algorithm shows an improvement accuracy of load forecasting over previous algorithms. The proposed STLF algorithm is expected to improve the predictive accuracy of STLF because it can be applied in a complementary manner along with other load forecasting algorithms.

Suggested Citation

  • Rae-Jun Park & Kyung-Bin Song & Bo-Sung Kwon, 2020. "Short-Term Load Forecasting Algorithm Using a Similar Day Selection Method Based on Reinforcement Learning," Energies, MDPI, vol. 13(10), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:10:p:2640-:d:361430
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/10/2640/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/10/2640/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ying Ji & Jianhui Wang & Jiacan Xu & Xiaoke Fang & Huaguang Zhang, 2019. "Real-Time Energy Management of a Microgrid Using Deep Reinforcement Learning," Energies, MDPI, vol. 12(12), pages 1-21, June.
    2. 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.
    3. Gonzales Chavez, S & Xiberta Bernat, J & Llaneza Coalla, H, 1999. "Forecasting of energy production and consumption in Asturias (northern Spain)," Energy, Elsevier, vol. 24(3), pages 183-198.
    4. 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.
    5. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    6. Vázquez-Canteli, José R. & Nagy, Zoltán, 2019. "Reinforcement learning for demand response: A review of algorithms and modeling techniques," Applied Energy, Elsevier, vol. 235(C), pages 1072-1089.
    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. Janusz Sowinski, 2021. "The Impact of the Selection of Exogenous Variables in the ANFIS Model on the Results of the Daily Load Forecast in the Power Company," Energies, MDPI, vol. 14(2), pages 1-18, January.
    2. Zhang, Xu & Sun, Yongjun & Gao, Dian-ce & Zou, Wenke & Fu, Jianping & Ma, Xiaowen, 2022. "Similarity-based grouping method for evaluation and optimization of dataset structure in machine-learning based short-term building cooling load prediction without measurable occupancy information," Applied Energy, Elsevier, vol. 327(C).
    3. Mohamed Massaoudi & Shady S. Refaat & Haitham Abu-Rub & Ines Chihi & Fakhreddine S. Oueslati, 2020. "PLS-CNN-BiLSTM: An End-to-End Algorithm-Based Savitzky–Golay Smoothing and Evolution Strategy for Load Forecasting," Energies, MDPI, vol. 13(20), pages 1-29, October.
    4. Bo Hu & Jian Xu & Zuoxia Xing & Pengfei Zhang & Jia Cui & Jinglu Liu, 2022. "Short-Term Combined Forecasting Method of Park Load Based on CEEMD-MLR-LSSVR-SBO," Energies, MDPI, vol. 15(8), pages 1-14, April.
    5. Feras Alasali & Khaled Nusair & Lina Alhmoud & Eyad Zarour, 2021. "Impact of the COVID-19 Pandemic on Electricity Demand and Load Forecasting," Sustainability, MDPI, vol. 13(3), pages 1-22, January.
    6. Eduardo Caro & Jesús Juan, 2020. "Short-Term Load Forecasting for Spanish Insular Electric Systems," Energies, MDPI, vol. 13(14), pages 1-26, July.
    7. Yundong Gu & Dongfen Ma & Jiawei Cui & Zhenhua Li & Yaqi Chen, 2022. "Variable-Weighted Ensemble Forecasting of Short-Term Power Load Based on Factor Space Theory," Annals of Data Science, Springer, vol. 9(3), pages 485-501, June.
    8. Dong-Jin Bae & Bo-Sung Kwon & Kyung-Bin Song, 2021. "XGBoost-Based Day-Ahead Load Forecasting Algorithm Considering Behind-the-Meter Solar PV Generation," Energies, MDPI, vol. 15(1), pages 1-16, December.
    9. Yamin Shen & Yuxuan Ma & Simin Deng & Chiou-Jye Huang & Ping-Huan Kuo, 2021. "An Ensemble Model based on Deep Learning and Data Preprocessing for Short-Term Electrical Load Forecasting," Sustainability, MDPI, vol. 13(4), pages 1-21, February.
    10. Zhou, Xinlei & Lin, Wenye & Kumar, Ritunesh & Cui, Ping & Ma, Zhenjun, 2022. "A data-driven strategy using long short term memory models and reinforcement learning to predict building electricity consumption," Applied Energy, Elsevier, vol. 306(PB).
    11. Lalitpat Aswanuwath & Warut Pannakkong & Jirachai Buddhakulsomsiri & Jessada Karnjana & Van-Nam Huynh, 2023. "A Hybrid Model of VMD-EMD-FFT, Similar Days Selection Method, Stepwise Regression, and Artificial Neural Network for Daily Electricity Peak Load Forecasting," Energies, MDPI, vol. 16(4), pages 1-24, February.

    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. Perera, A.T.D. & Kamalaruban, Parameswaran, 2021. "Applications of reinforcement learning in energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    2. Yuhong Wang & Lei Chen & Hong Zhou & Xu Zhou & Zongsheng Zheng & Qi Zeng & Li Jiang & Liang Lu, 2021. "Flexible Transmission Network Expansion Planning Based on DQN Algorithm," Energies, MDPI, vol. 14(7), pages 1-21, April.
    3. Yang, Ting & Zhao, Liyuan & Li, Wei & Zomaya, Albert Y., 2021. "Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning," Energy, Elsevier, vol. 235(C).
    4. Wang, Xuan & Shu, Gequn & Tian, Hua & Wang, Rui & Cai, Jinwen, 2020. "Operation performance comparison of CCHP systems with cascade waste heat recovery systems by simulation and operation optimisation," Energy, Elsevier, vol. 206(C).
    5. Wang, Yi & Qiu, Dawei & Sun, Mingyang & Strbac, Goran & Gao, Zhiwei, 2023. "Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach," Applied Energy, Elsevier, vol. 335(C).
    6. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
    7. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    8. Oleh Lukianykhin & Tetiana Bogodorova, 2021. "Voltage Control-Based Ancillary Service Using Deep Reinforcement Learning," Energies, MDPI, vol. 14(8), pages 1-22, April.
    9. 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.
    10. Arroyo, Javier & Manna, Carlo & Spiessens, Fred & Helsen, Lieve, 2022. "Reinforced model predictive control (RL-MPC) for building energy management," Applied Energy, Elsevier, vol. 309(C).
    11. Harrold, Daniel J.B. & Cao, Jun & Fan, Zhong, 2022. "Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning," Energy, Elsevier, vol. 238(PC).
    12. Yujian Ye & Dawei Qiu & Huiyu Wang & Yi Tang & Goran Strbac, 2021. "Real-Time Autonomous Residential Demand Response Management Based on Twin Delayed Deep Deterministic Policy Gradient Learning," Energies, MDPI, vol. 14(3), pages 1-22, January.
    13. Harrold, Daniel J.B. & Cao, Jun & Fan, Zhong, 2022. "Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 318(C).
    14. 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).
    15. Touzani, Samir & Prakash, Anand Krishnan & Wang, Zhe & Agarwal, Shreya & Pritoni, Marco & Kiran, Mariam & Brown, Richard & Granderson, Jessica, 2021. "Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency," Applied Energy, Elsevier, vol. 304(C).
    16. Park, Keonwoo & Moon, Ilkyeong, 2022. "Multi-agent deep reinforcement learning approach for EV charging scheduling in a smart grid," Applied Energy, Elsevier, vol. 328(C).
    17. Zheng, Lingwei & Wu, Hao & Guo, Siqi & Sun, Xinyu, 2023. "Real-time dispatch of an integrated energy system based on multi-stage reinforcement learning with an improved action-choosing strategy," Energy, Elsevier, vol. 277(C).
    18. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    19. Denis Sidorov & Daniil Panasetsky & Nikita Tomin & Dmitriy Karamov & Aleksei Zhukov & Ildar Muftahov & Aliona Dreglea & Fang Liu & Yong Li, 2020. "Toward Zero-Emission Hybrid AC/DC Power Systems with Renewable Energy Sources and Storages: A Case Study from Lake Baikal Region," Energies, MDPI, vol. 13(5), pages 1-18, March.
    20. Zhang, Wen Yu & Hong, Wei-Chiang & Dong, Yucheng & Tsai, Gary & Sung, Jing-Tian & Fan, Guo-feng, 2012. "Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting," Energy, Elsevier, vol. 45(1), pages 850-858.

    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:2640-:d:361430. 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.