IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i3p1236-d744482.html
   My bibliography  Save this article

Power Load Forecasting Considering Climate Factors Based on IPSO-Elman Method in China

Author

Listed:
  • Jicheng Liu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Yu Yin

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

Abstract

In order to implement the national need for the optimal allocation of power resources, power load forecasting, as an important research topic, has important theoretical and practical significance. The purpose of this study is to construct a prediction model considering climate factors based on a large amount of historical data, and to prove that the prediction accuracy is related to both climate factors and load regularity. The results of load forecasting are affected by many climate factors, so firstly the climate variables affecting load forecasting are screened. Secondly, a load prediction model based on the IPSO-Elman network learning algorithm is constructed by taking the difference between the predicted value of the neural network and the actual value as the fitness function of particle swarm optimization. In view of the great influence of weights and thresholds on the prediction accuracy of the Elman neural network, the particle swarm optimization algorithm (PSO) is used to optimize parameters in order to improve the prediction accuracy of ELMAN neural network. Thirdly, prediction with and without climate factors is compared and analyzed, and the prediction accuracy of the model compared by using cosine distance and various error indicators. Finally, the stability discriminant index of historical load regularity is introduced to prove that the accuracy of the prediction model is related to the regularity of historical load in the forecast area. The prediction method proposed in this paper can provide reference for power system scheduling.

Suggested Citation

  • Jicheng Liu & Yu Yin, 2022. "Power Load Forecasting Considering Climate Factors Based on IPSO-Elman Method in China," Energies, MDPI, vol. 15(3), pages 1-23, February.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:1236-:d:744482
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/3/1236/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/3/1236/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liangpeng Wu & Qingyuan Zhu, 2021. "Impacts of the carbon emission trading system on China’s carbon emission peak: a new data-driven approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(3), pages 2487-2515, July.
    2. Xiao, Liye & Shao, Wei & Yu, Mengxia & Ma, Jing & Jin, Congjun, 2017. "Research and application of a hybrid wavelet neural network model with the improved cuckoo search algorithm for electrical power system forecasting," Applied Energy, Elsevier, vol. 198(C), pages 203-222.
    3. Dariush Khezrimotlagh & Yao Chen, 2018. "Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 217-234, Springer.
    4. Kei Hirose & Keigo Wada & Maiya Hori & Rin-ichiro Taniguchi, 2020. "Event Effects Estimation on Electricity Demand Forecasting," Energies, MDPI, vol. 13(21), pages 1-20, November.
    5. 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.
    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. Shaoqian Pei & Hui Qin & Liqiang Yao & Yongqi Liu & Chao Wang & Jianzhong Zhou, 2020. "Multi-Step Ahead Short-Term Load Forecasting Using Hybrid Feature Selection and Improved Long Short-Term Memory Network," Energies, MDPI, vol. 13(16), pages 1-23, August.
    8. Feng Dong & Yifei Hua & Bolin Yu, 2018. "Peak Carbon Emissions in China: Status, Key Factors and Countermeasures—A Literature Review," Sustainability, MDPI, vol. 10(8), pages 1-34, August.
    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. Saidjon Shiralievich Tavarov & Alexander Sidorov & Zsolt Čonka & Murodbek Safaraliev & Pavel Matrenin & Mihail Senyuk & Svetlana Beryozkina & Inga Zicmane, 2023. "Control of Operational Modes of an Urban Distribution Grid under Conditions of Uncertainty," Energies, MDPI, vol. 16(8), pages 1-18, April.

    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. Quintano, Claudio & Mazzocchi, Paolo & Rocca, Antonella, 2021. "Evaluation of the eco-efficiency of territorial districts with seaport economic activities," Utilities Policy, Elsevier, vol. 71(C).
    2. Meike Weltin & Silke Hüttel, 2023. "Sustainable Intensification Farming as an Enabler for Farm Eco-Efficiency?," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 84(1), pages 315-342, January.
    3. Ziyan Zheng & Fangdao Qiu & Xinlin Zhang, 2020. "Heterogeneity of correlation between the locational condition and industrial transformation of regenerative resource‐based cities in China," Growth and Change, Wiley Blackwell, vol. 51(2), pages 771-791, June.
    4. Yaozu Xue, 2022. "Evaluation analysis on industrial green total factor productivity and energy transition policy in resource-based region," Energy & Environment, , vol. 33(3), pages 419-434, May.
    5. Yujian Jin & Lihong Yu & Yan Wang, 2022. "Green Total Factor Productivity and Its Saving Effect on the Green Factor in China’s Strategic Minerals Industry from 1998–2017," IJERPH, MDPI, vol. 19(22), pages 1-20, November.
    6. Yiming Zhuang & Meltem Denizel & Frank Montabon, 2023. "Examining Firms’ Sustainability Frontier: Efficiency in Reaching the Triple Bottom Line," Sustainability, MDPI, vol. 15(11), pages 1-22, May.
    7. Zhang, Yijun & Li, Xiaoping & Song, Yi & Jiang, Feitao, 2021. "Can green industrial policy improve total factor productivity? Firm-level evidence from China," Structural Change and Economic Dynamics, Elsevier, vol. 59(C), pages 51-62.
    8. Trinks, Arjan & Mulder, Machiel & Scholtens, Bert, 2020. "An Efficiency Perspective on Carbon Emissions and Financial Performance," Ecological Economics, Elsevier, vol. 175(C).
    9. Shuying Wang & Yifei Gao & Hongchang Zhou, 2022. "Research on Green Total Factor Productivity Enhancement Path from the Configurational Perspective—Based on the TOE Theoretical Framework," Sustainability, MDPI, vol. 14(21), pages 1-20, October.
    10. Shabani, Amir & Visani, Franco & Barbieri, Paolo & Dullaert, Wout & Vigo, Daniele, 2019. "Reliable estimation of suppliers’ total cost of ownership: An imprecise data envelopment analysis model with common weights," Omega, Elsevier, vol. 87(C), pages 57-70.
    11. Xiangqian Wang & Shudong Wang & Yongqiu Xia, 2022. "Evaluation and Dynamic Evolution of the Total Factor Environmental Efficiency in China’s Mining Industry," Energies, MDPI, vol. 15(3), pages 1-19, February.
    12. Heinz Ahn & Peter Bogetoft & Ana Lopes, 2019. "Measuring potential sub-unit efficiency to counter the aggregation bias in benchmarking," Journal of Business Economics, Springer, vol. 89(1), pages 53-77, February.
    13. Victoria Vicario-Modroño & Rosa Gallardo-Cobos & Pedro Sánchez-Zamora, 2023. "Sustainability evaluation of olive oil mills in Andalusia (Spain): a study based on composite indicators," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 6363-6392, July.
    14. Xing Zhang, 2018. "Short-Term Load Forecasting for Electric Bus Charging Stations Based on Fuzzy Clustering and Least Squares Support Vector Machine Optimized by Wolf Pack Algorithm," Energies, MDPI, vol. 11(6), pages 1-18, June.
    15. Tonglin Fu & Chen Wang, 2018. "A Hybrid Wind Speed Forecasting Method and Wind Energy Resource Analysis Based on a Swarm Intelligence Optimization Algorithm and an Artificial Intelligence Model," Sustainability, MDPI, vol. 10(11), pages 1-24, October.
    16. Kaffash, Sepideh & Azizi, Roza & Huang, Ying & Zhu, Joe, 2020. "A survey of data envelopment analysis applications in the insurance industry 1993–2018," European Journal of Operational Research, Elsevier, vol. 284(3), pages 801-813.
    17. Ghazala Aziz & Zouheir Mighri, 2022. "Carbon Dioxide Emissions and Forestry in China: A Spatial Panel Data Approach," Sustainability, MDPI, vol. 14(19), pages 1-40, October.
    18. Filip Fidanoski & Kiril Simeonovski & Violeta Cvetkoska, 2021. "Energy Efficiency in OECD Countries: A DEA Approach," Energies, MDPI, vol. 14(4), pages 1-21, February.
    19. Thomas Bournaris & George Vlontzos & Christina Moulogianni, 2019. "Efficiency of Vegetables Produced in Glasshouses: The Impact of Data Envelopment Analysis (DEA) in Land Management Decision Making," Land, MDPI, vol. 8(1), pages 1-11, January.
    20. Nazila Pourhaji & Mohammad Asadpour & Ali Ahmadian & Ali Elkamel, 2022. "The Investigation of Monthly/Seasonal Data Clustering Impact on Short-Term Electricity Price Forecasting Accuracy: Ontario Province Case Study," Sustainability, MDPI, vol. 14(5), pages 1-14, March.

    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:15:y:2022:i:3:p:1236-:d:744482. 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.