IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v205y2020ics0360544220312135.html
   My bibliography  Save this article

A novel electrical net-load forecasting model based on deep neural networks and wavelet transform integration

Author

Listed:
  • Alipour, Mohammadali
  • Aghaei, Jamshid
  • Norouzi, Mohammadali
  • Niknam, Taher
  • Hashemi, Sattar
  • Lehtonen, Matti

Abstract

The increasing growth of renewable energy resources (RESs) in microgrids and the uncertainty associated with their generation have led to problems in the management of smart active distribution networks. One of the main challenges in the planning for the operation of renewable power systems is net electrical load forecasting, therefore, increasing the accuracy of net-load forecasting is a vital issue. In this study, a deep neural network model is used to forecast the net-load. The structure of the deep neural network used in the proposed forecasting model has been constituted by several autoencoders and a cascade neural network. The net-load forecasting has been done in the presence of uncertainties arising from wind and photovoltaic (PV) generation and electrical load consumption. To improve the net-load forecasting precision, the wavelet transform has been applied to the inputs of the proposed model. The net-load forecast in different scenarios is conducted on an open dataset from 37 central European countries, from the perspective of different types of forecasting strategies and changes in the neural network architecture. The simulation results significantly confirm the accuracy of the proposed forecasting model by different indices.

Suggested Citation

  • Alipour, Mohammadali & Aghaei, Jamshid & Norouzi, Mohammadali & Niknam, Taher & Hashemi, Sattar & Lehtonen, Matti, 2020. "A novel electrical net-load forecasting model based on deep neural networks and wavelet transform integration," Energy, Elsevier, vol. 205(C).
    • Handle: RePEc:eee:energy:v:205:y:2020:i:c:s0360544220312135
    DOI: 10.1016/j.energy.2020.118106
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220312135
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.118106?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. Yagli, Gokhan Mert & Yang, Dazhi & Srinivasan, Dipti, 2019. "Automatic hourly solar forecasting using machine learning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 487-498.
    2. Kaur, Amanpreet & Nonnenmacher, Lukas & Coimbra, Carlos F.M., 2016. "Net load forecasting for high renewable energy penetration grids," Energy, Elsevier, vol. 114(C), pages 1073-1084.
    3. Shaker, Hamid & Zareipour, Hamidreza & Wood, David, 2016. "Impacts of large-scale wind and solar power integration on California׳s net electrical load," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 761-774.
    4. Panapakidis, Ioannis P. & Dagoumas, Athanasios S., 2017. "Day-ahead natural gas demand forecasting based on the combination of wavelet transform and ANFIS/genetic algorithm/neural network model," Energy, Elsevier, vol. 118(C), pages 231-245.
    5. Ahmad, Muhammad Waseem & Mourshed, Monjur & Rezgui, Yacine, 2018. "Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression," Energy, Elsevier, vol. 164(C), pages 465-474.
    6. Khosravi, A. & Machado, L. & Nunes, R.O., 2018. "Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil," Applied Energy, Elsevier, vol. 224(C), pages 550-566.
    7. Liu, Da & Sun, Kun, 2019. "Random forest solar power forecast based on classification optimization," Energy, Elsevier, vol. 187(C).
    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. Yixiang Ma & Lean Yu & Guoxing Zhang, 2022. "A Hybrid Short-Term Load Forecasting Model Based on a Multi-Trait-Driven Methodology and Secondary Decomposition," Energies, MDPI, vol. 15(16), pages 1-20, August.
    2. Matheus Henrique Dal Molin Ribeiro & Stéfano Frizzo Stefenon & José Donizetti de Lima & Ademir Nied & Viviana Cocco Mariani & Leandro dos Santos Coelho, 2020. "Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning," Energies, MDPI, vol. 13(19), pages 1-22, October.
    3. Wang, Han & Yan, Jie & Zhang, Jiawei & Liu, Shihua & Liu, Yongqian & Han, Shuang & Qu, Tonghui, 2024. "Short-term integrated forecasting method for wind power, solar power, and system load based on variable attention mechanism and multi-task learning," Energy, Elsevier, vol. 304(C).
    4. Wei, Nan & Yin, Lihua & Li, Chao & Wang, Wei & Qiao, Weibiao & Li, Changjun & Zeng, Fanhua & Fu, Lingdi, 2022. "Short-term load forecasting using detrend singular spectrum fluctuation analysis," Energy, Elsevier, vol. 256(C).
    5. Norouzi, Mohammadali & Aghaei, Jamshid & Niknam, Taher & Alipour, Mohammadali & Pirouzi, Sasan & Lehtonen, Matti, 2023. "Risk-averse and flexi-intelligent scheduling of microgrids based on hybrid Boltzmann machines and cascade neural network forecasting," Applied Energy, Elsevier, vol. 348(C).
    6. Yuan-Kang Wu & Cheng-Liang Huang & Quoc-Thang Phan & Yuan-Yao Li, 2022. "Completed Review of Various Solar Power Forecasting Techniques Considering Different Viewpoints," Energies, MDPI, vol. 15(9), pages 1-22, May.
    7. Kalhori, M. Rostam Niakan & Emami, I. Taheri & Fallahi, F. & Tabarzadi, M., 2022. "A data-driven knowledge-based system with reasoning under uncertain evidence for regional long-term hourly load forecasting," Applied Energy, Elsevier, vol. 314(C).
    8. Sharma, Abhishek & Jain, Sachin Kumar, 2022. "A novel seasonal segmentation approach for day-ahead load forecasting," Energy, Elsevier, vol. 257(C).
    9. Tziolis, Georgios & Spanias, Chrysovalantis & Theodoride, Maria & Theocharides, Spyros & Lopez-Lorente, Javier & Livera, Andreas & Makrides, George & Georghiou, George E., 2023. "Short-term electric net load forecasting for solar-integrated distribution systems based on Bayesian neural networks and statistical post-processing," Energy, Elsevier, vol. 271(C).
    10. Hu, Gang & Xu, Zhaoqiang & Wang, Guorong & Zeng, Bin & Liu, Yubing & Lei, Ye, 2021. "Forecasting energy consumption of long-distance oil products pipeline based on improved fruit fly optimization algorithm and support vector regression," Energy, Elsevier, vol. 224(C).
    11. 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).
    12. He, Yan & Zhang, Hongli & Dong, Yingchao & Wang, Cong & Ma, Ping, 2024. "Residential net load interval prediction based on stacking ensemble learning," Energy, Elsevier, vol. 296(C).
    13. Navarkar, Abhishek & Hasti, Veeraraghava Raju & Deneke, Elihu & Gore, Jay P., 2020. "A data-driven model for thermodynamic properties of a steam generator under cycling operation," Energy, Elsevier, vol. 211(C).
    14. Ali, Aliyuda, 2021. "Data-driven based machine learning models for predicting the deliverability of underground natural gas storage in salt caverns," Energy, Elsevier, vol. 229(C).
    15. Biyun Chen & Qi Xu & Zhuoli Zhao & Xiaoxuan Guo & Yongjun Zhang & Jingmin Chi & Canbing Li, 2023. "A Prosumer Power Prediction Method Based on Dynamic Segmented Curve Matching and Trend Feature Perception," Sustainability, MDPI, vol. 15(4), pages 1-18, February.
    16. Erdener, Burcin Cakir & Feng, Cong & Doubleday, Kate & Florita, Anthony & Hodge, Bri-Mathias, 2022. "A review of behind-the-meter solar forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    17. Yin, Linfei & Luo, Shikui & Ma, Chenxiao, 2021. "Expandable depth and width adaptive dynamic programming for economic smart generation control of smart grids," Energy, Elsevier, vol. 232(C).
    18. Yu Shi & Fei Lv & Xuefeng Gao & Minglei Jiang & Huan Luo & Ruhang Xu, 2023. "A Bi-Level Optimal Operation Model for Small-Scale Active Distribution Networks Considering the Coupling Fluctuation of Spot Electricity Prices and Renewable Energy Sources," Energies, MDPI, vol. 16(11), pages 1-26, June.

    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. Ayub, Yousaf & Ren, Jingzheng & Shi, Tao & Shen, Weifeng & He, Chang, 2023. "Poultry litter valorization: Development and optimization of an electro-chemical and thermal tri-generation process using an extreme gradient boosting algorithm," Energy, Elsevier, vol. 263(PC).
    2. Zhou, Yi & Zhou, Nanrun & Gong, Lihua & Jiang, Minlin, 2020. "Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine," Energy, Elsevier, vol. 204(C).
    3. Andi A. H. Lateko & Hong-Tzer Yang & Chao-Ming Huang, 2022. "Short-Term PV Power Forecasting Using a Regression-Based Ensemble Method," Energies, MDPI, vol. 15(11), pages 1-21, June.
    4. Korkmaz, Deniz, 2021. "SolarNet: A hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting," Applied Energy, Elsevier, vol. 300(C).
    5. Jebli, Imane & Belouadha, Fatima-Zahra & Kabbaj, Mohammed Issam & Tilioua, Amine, 2021. "Prediction of solar energy guided by pearson correlation using machine learning," Energy, Elsevier, vol. 224(C).
    6. Carneiro, Tatiane C. & Rocha, Paulo A.C. & Carvalho, Paulo C.M. & Fernández-Ramírez, Luis M., 2022. "Ridge regression ensemble of machine learning models applied to solar and wind forecasting in Brazil and Spain," Applied Energy, Elsevier, vol. 314(C).
    7. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
    8. Li, Shunxi & Su, Bowen & St-Pierre, David L. & Sui, Pang-Chieh & Zhang, Guofang & Xiao, Jinsheng, 2017. "Decision-making of compressed natural gas station siting for public transportation: Integration of multi-objective optimization, fuzzy evaluating, and radar charting," Energy, Elsevier, vol. 140(P1), pages 11-17.
    9. Hu, Jiaxiang & Hu, Weihao & Cao, Di & Sun, Xinwu & Chen, Jianjun & Huang, Yuehui & Chen, Zhe & Blaabjerg, Frede, 2024. "Probabilistic net load forecasting based on transformer network and Gaussian process-enabled residual modeling learning method," Renewable Energy, Elsevier, vol. 225(C).
    10. Xilong Lin & Yisen Niu & Zixuan Yan & Lianglin Zou & Ping Tang & Jifeng Song, 2024. "Hybrid Photovoltaic Output Forecasting Model with Temporal Convolutional Network Using Maximal Information Coefficient and White Shark Optimizer," Sustainability, MDPI, vol. 16(14), pages 1-20, July.
    11. Wang, Zhenyu & Zhang, Yunpeng & Li, Guorong & Zhang, Jinlong & Zhou, Hai & Wu, Ji, 2024. "A novel solar irradiance forecasting method based on multi-physical process of atmosphere optics and LSTM-BP model," Renewable Energy, Elsevier, vol. 226(C).
    12. Rao, Congjun & Zhang, Yue & Wen, Jianghui & Xiao, Xinping & Goh, Mark, 2023. "Energy demand forecasting in China: A support vector regression-compositional data second exponential smoothing model," Energy, Elsevier, vol. 263(PC).
    13. Zhang, Meng & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2022. "A novel flexible grey multivariable model and its application in forecasting energy consumption in China," Energy, Elsevier, vol. 239(PE).
    14. 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).
    15. Dunguo Mou, 2018. "Wind Power Development and Energy Storage under China’s Electricity Market Reform—A Case Study of Fujian Province," Sustainability, MDPI, vol. 10(2), pages 1-20, January.
    16. Kong, Xiangyu & Li, Chuang & Wang, Chengshan & Zhang, Yusen & Zhang, Jian, 2020. "Short-term electrical load forecasting based on error correction using dynamic mode decomposition," Applied Energy, Elsevier, vol. 261(C).
    17. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    18. Khan, Waqas & Walker, Shalika & Zeiler, Wim, 2022. "Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach," Energy, Elsevier, vol. 240(C).
    19. Neethu Elizabeth Michael & Manohar Mishra & Shazia Hasan & Ahmed Al-Durra, 2022. "Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique," Energies, MDPI, vol. 15(6), pages 1-20, March.
    20. Mohamed Massaoudi & Ines Chihi & Lilia Sidhom & Mohamed Trabelsi & Shady S. Refaat & Fakhreddine S. Oueslati, 2021. "Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements," Energies, MDPI, vol. 14(13), pages 1-20, July.

    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:205:y:2020:i:c:s0360544220312135. 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.