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

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  • 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
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    6. 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).
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    8. 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).
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    11. 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).
    12. 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).
    13. 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.
    14. 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).
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