IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v305y2024ics0360544224019662.html
   My bibliography  Save this article

A paradigm shift in solar energy forecasting: A novel two-phase model for monthly residential consumption

Author

Listed:
  • Xu, Yan
  • Yu, Qi
  • Du, Pei
  • Wang, Jianzhou

Abstract

Accurately predicting residential solar energy consumption is crucial for efficient electricity production, supply, and power dispatch. However, conventional forecasting methods often struggle to handle complex energy consumption data. In response to this challenge, this study develops a pioneering two-stage error-corrected combined forecasting model that integrates traditional linear methods, seasonal processing techniques, deep learning models, and intelligent optimization algorithms to outperform other combined forecasting methods in terms of performance. This research analyzes the combined weight values, shedding light on why the proposed model consistently outperforms its counterparts. To confirm its superiority, the proposed model and five benchmark models are rigorously tested in this paper using four evaluation metrics and a hypothesis testing method. The empirical results show that the proposed combined model performs well in terms of accuracy and stability. Notably, the average absolute percentage error of its 24-step ahead prediction is 2.9053 %, which outperforms all comparative models, both single and combined model. These results fully illustrate the advantages of the combined model and reaffirm the excellence of its prediction performance in predicting energy consumption.

Suggested Citation

  • Xu, Yan & Yu, Qi & Du, Pei & Wang, Jianzhou, 2024. "A paradigm shift in solar energy forecasting: A novel two-phase model for monthly residential consumption," Energy, Elsevier, vol. 305(C).
    • Handle: RePEc:eee:energy:v:305:y:2024:i:c:s0360544224019662
    DOI: 10.1016/j.energy.2024.132192
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224019662
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.132192?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. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    2. Hu, Yusha & Li, Jigeng & Hong, Mengna & Ren, Jingzheng & Lin, Ruojue & Liu, Yue & Liu, Mengru & Man, Yi, 2019. "Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm—A case study of papermaking process," Energy, Elsevier, vol. 170(C), pages 1215-1227.
    3. Weng, Futian & Zhang, Hongwei & Yang, Cai, 2021. "Volatility forecasting of crude oil futures based on a genetic algorithm regularization online extreme learning machine with a forgetting factor: The role of news during the COVID-19 pandemic," Resources Policy, Elsevier, vol. 73(C).
    4. Niu, Xinsong & Wang, Jiyang, 2019. "A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 241(C), pages 519-539.
    5. Shine, P. & Scully, T. & Upton, J. & Murphy, M.D., 2019. "Annual electricity consumption prediction and future expansion analysis on dairy farms using a support vector machine," Applied Energy, Elsevier, vol. 250(C), pages 1110-1119.
    6. Wang, Zheng-Xin & Li, Qin & Pei, Ling-Ling, 2018. "A seasonal GM(1,1) model for forecasting the electricity consumption of the primary economic sectors," Energy, Elsevier, vol. 154(C), pages 522-534.
    7. Wang, Jianzhou & Du, Pei & Niu, Tong & Yang, Wendong, 2017. "A novel hybrid system based on a new proposed algorithm—Multi-Objective Whale Optimization Algorithm for wind speed forecasting," Applied Energy, Elsevier, vol. 208(C), pages 344-360.
    8. Namrye Son & Yoonjeong Shin, 2023. "Short- and Medium-Term Electricity Consumption Forecasting Using Prophet and GRU," Sustainability, MDPI, vol. 15(22), pages 1-16, November.
    9. Zhou, Wenhao & Zeng, Bo & Wang, Jianzhou & Luo, Xiaoshuang & Liu, Xianzhou, 2021. "Forecasting Chinese carbon emissions using a novel grey rolling prediction model," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
    10. Jianzhou Wang & Pei Du, 2021. "Quarterly PM2.5 prediction using a novel seasonal grey model and its further application in health effects and economic loss assessment: evidences from Shanghai and Tianjin, China," 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(1), pages 889-909, May.
    11. Jamil, Rehan, 2020. "Hydroelectricity consumption forecast for Pakistan using ARIMA modeling and supply-demand analysis for the year 2030," Renewable Energy, Elsevier, vol. 154(C), pages 1-10.
    12. Wu, Han & Gao, Xiao-Zhi & Heng, Jia-Ni, 2024. "Bio-multisensory-inspired gate-attention coordination model for forecasting short-term significant wave height," Energy, Elsevier, vol. 294(C).
    13. Tang, Tao & Jiang, Weiheng & Zhang, Hui & Nie, Jiangtian & Xiong, Zehui & Wu, Xiaogang & Feng, Wenjiang, 2022. "GM(1,1) based improved seasonal index model for monthly electricity consumption forecasting," Energy, Elsevier, vol. 252(C).
    14. Du, Pei & Yang, Dongchuan & Li, Yanzhao & Wang, Jianzhou, 2024. "An innovative interpretable combined learning model for wind speed forecasting," Applied Energy, Elsevier, vol. 358(C).
    15. Niu, Dongxiao & Ji, Zhengsen & Li, Wanying & Xu, Xiaomin & Liu, Da, 2021. "Research and application of a hybrid model for mid-term power demand forecasting based on secondary decomposition and interval optimization," Energy, Elsevier, vol. 234(C).
    16. Wang, Jianzhou & Zhang, Linyue & Li, Zhiwu, 2022. "Interval forecasting system for electricity load based on data pre-processing strategy and multi-objective optimization algorithm," Applied Energy, Elsevier, vol. 305(C).
    17. Wang, Jianing & Zhu, Hongqiu & Zhang, Yingjie & Cheng, Fei & Zhou, Can, 2023. "A novel prediction model for wind power based on improved long short-term memory neural network," Energy, Elsevier, vol. 265(C).
    18. Ghasemi, A. & Shayeghi, H. & Moradzadeh, M. & Nooshyar, M., 2016. "A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management," Applied Energy, Elsevier, vol. 177(C), pages 40-59.
    19. Yuan, Chaoqing & Liu, Sifeng & Fang, Zhigeng, 2016. "Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model," Energy, Elsevier, vol. 100(C), pages 384-390.
    20. Zeng, Bo & He, Chengxiang & Mao, Cuiwei & Wu, You, 2023. "Forecasting China's hydropower generation capacity using a novel grey combination optimization model," Energy, Elsevier, vol. 262(PA).
    21. Hao, Yan & Tian, Chengshi, 2019. "A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting," Applied Energy, Elsevier, vol. 238(C), pages 368-383.
    22. Yang, Dongchuan & Guo, Ju-e & Sun, Shaolong & Han, Jing & Wang, Shouyang, 2022. "An interval decomposition-ensemble approach with data-characteristic-driven reconstruction for short-term load forecasting," Applied Energy, Elsevier, vol. 306(PA).
    23. Yang, Qiuling & Deng, Changhong & Chang, Xiqiang, 2022. "Ultra-short-term / short-term wind speed prediction based on improved singular spectrum analysis," Renewable Energy, Elsevier, vol. 184(C), pages 36-44.
    Full references (including those not matched with items on IDEAS)

    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. Wang, Kang & Wang, Jianzhou & Zeng, Bo & Lu, Haiyan, 2022. "An integrated power load point-interval forecasting system based on information entropy and multi-objective optimization," Applied Energy, Elsevier, vol. 314(C).
    2. Du, Pei & Guo, Ju'e & Sun, Shaolong & Wang, Shouyang & Wu, Jing, 2022. "A novel two-stage seasonal grey model for residential electricity consumption forecasting," Energy, Elsevier, vol. 258(C).
    3. Zhang, Guowei & Zhang, Yi & Wang, Hui & Liu, Da & Cheng, Runkun & Yang, Di, 2024. "Short-term wind speed forecasting based on adaptive secondary decomposition and robust temporal convolutional network," Energy, Elsevier, vol. 288(C).
    4. Wang, Jianzhou & Wang, Shuai & Zeng, Bo & Lu, Haiyan, 2022. "A novel ensemble probabilistic forecasting system for uncertainty in wind speed," Applied Energy, Elsevier, vol. 313(C).
    5. Hu, Yusha & Man, Yi, 2023. "Energy consumption and carbon emissions forecasting for industrial processes: Status, challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    6. Hu, Yusha & Li, Jigeng & Hong, Mengna & Ren, Jingzheng & Man, Yi, 2022. "Industrial artificial intelligence based energy management system: Integrated framework for electricity load forecasting and fault prediction," Energy, Elsevier, vol. 244(PB).
    7. Wang, Jianzhou & Gao, Jialu & Wei, Danxiang, 2022. "Electric load prediction based on a novel combined interval forecasting system," Applied Energy, Elsevier, vol. 322(C).
    8. Yang, Dongchuan & Guo, Ju-e & Li, Yanzhao & Sun, Shaolong & Wang, Shouyang, 2023. "Short-term load forecasting with an improved dynamic decomposition-reconstruction-ensemble approach," Energy, Elsevier, vol. 263(PA).
    9. Zhou, Wenhao & Li, Hailin & Zhang, Zhiwei, 2022. "A novel seasonal fractional grey model for predicting electricity demand: A case study of Zhejiang in China," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 200(C), pages 128-147.
    10. Zhang, Dongqin & Hu, Gang & Song, Jie & Gao, Huanxiang & Ren, Hehe & Chen, Wenli, 2024. "A novel spatio-temporal wind speed forecasting method based on the microscale meteorological model and a hybrid deep learning model," Energy, Elsevier, vol. 288(C).
    11. Cao, Xin & Liu, Chang & Wu, Mingxuan & Li, Zhi & Wang, Yihan & Wen, Zongguo, 2023. "Heterogeneity and connection in the spatial–temporal evolution trend of China’s energy consumption at provincial level," Applied Energy, Elsevier, vol. 336(C).
    12. Jiang, Ping & Li, Ranran & Liu, Ningning & Gao, Yuyang, 2020. "A novel composite electricity demand forecasting framework by data processing and optimized support vector machine," Applied Energy, Elsevier, vol. 260(C).
    13. Laouafi, Abderrezak & Laouafi, Farida & Boukelia, Taqiy Eddine, 2022. "An adaptive hybrid ensemble with pattern similarity analysis and error correction for short-term load forecasting," Applied Energy, Elsevier, vol. 322(C).
    14. Yang, Wendong & Sun, Shaolong & Hao, Yan & Wang, Shouyang, 2022. "A novel machine learning-based electricity price forecasting model based on optimal model selection strategy," Energy, Elsevier, vol. 238(PC).
    15. 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.
    16. Kailai Ni & Jianzhou Wang & Guangyu Tang & Danxiang Wei, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia," Energies, MDPI, vol. 12(13), pages 1-30, June.
    17. Zhang, Lifang & Wang, Jianzhou & Niu, Xinsong & Liu, Zhenkun, 2021. "Ensemble wind speed forecasting with multi-objective Archimedes optimization algorithm and sub-model selection," Applied Energy, Elsevier, vol. 301(C).
    18. Xiwen Cui & Xinyu Guan & Dongyu Wang & Dongxiao Niu & Xiaomin Xu, 2022. "Can China Meet Its 2030 Total Energy Consumption Target? Based on an RF-SSA-SVR-KDE Model," Energies, MDPI, vol. 15(16), pages 1-13, August.
    19. Lu, Hongfang & Ma, Xin & Huang, Kun & Azimi, Mohammadamin, 2020. "Prediction of offshore wind farm power using a novel two-stage model combining kernel-based nonlinear extension of the Arps decline model with a multi-objective grey wolf optimizer," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    20. Meng, Anbo & Zhang, Haitao & Yin, Hao & Xian, Zikang & Chen, Shu & Zhu, Zibin & Zhang, Zheng & Rong, Jiayu & Li, Chen & Wang, Chenen & Wu, Zhenbo & Deng, Weisi & Luo, Jianqiang & Wang, Xiaolin, 2023. "A novel multi-gradient evolutionary deep learning approach for few-shot wind power prediction using time-series GAN," Energy, Elsevier, vol. 283(C).

    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:energy:v:305:y:2024:i:c:s0360544224019662. 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.journals.elsevier.com/energy .

    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.