AB-Net: A Novel Deep Learning Assisted Framework for Renewable Energy Generation Forecasting

Noman Khan, Fath U Min Ullah, Ijaz Ul Haq, Samee Ullah Khan, Mi Young Lee and Sung Wook Baik
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Noman Khan: Sejong University, Seoul 143-747, Korea
Fath U Min Ullah: Sejong University, Seoul 143-747, Korea
Ijaz Ul Haq: Sejong University, Seoul 143-747, Korea
Samee Ullah Khan: Sejong University, Seoul 143-747, Korea
Mi Young Lee: Sejong University, Seoul 143-747, Korea
Sung Wook Baik: Sejong University, Seoul 143-747, Korea

Mathematics, 2021, vol. 9, issue 19, 1-18

Abstract: Renewable energy (RE) power plants are deployed globally because the renewable energy sources (RESs) are sustainable, clean, and environmentally friendly. However, the demand for power increases on a daily basis due to population growth, technology, marketing, and the number of installed industries. This challenge has raised a critical issue of how to intelligently match the power generation with the consumption for efficient energy management. To handle this issue, we propose a novel architecture called ‘AB-Net’: a one-step forecast of RE generation for short-term horizons by incorporating an autoencoder (AE) with bidirectional long short-term memory (BiLSTM). Firstly, the data acquisition step is applied, where the data are acquired from various RESs such as wind and solar. The second step performs deep preprocessing of the acquired data via several de-noising and cleansing filters to clean the data and normalize them prior to actual processing. Thirdly, an AE is employed to extract the discriminative features from the cleaned data sequence through its encoder part. BiLSTM is used to learn these features to provide a final forecast of power generation. The proposed AB-Net was evaluated using two publicly available benchmark datasets where the proposed method obtains state-of-the-art results in terms of the error metrics.

Keywords: energy resources; wind power; power generation; power consumption; renewable energy; solar power; machine learning; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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