Multi-directional gated recurrent unit and convolutional neural network for load and energy forecasting: A novel hybridization

Journal article


Abid, F., Alam, M., Alamri, F.S. and Siddique, I. (2023). Multi-directional gated recurrent unit and convolutional neural network for load and energy forecasting: A novel hybridization. AIMS Mathematics. 8 (9), pp. 19993-20017. https://doi.org/10.3934/math.20231019
AuthorsAbid, F., Alam, M., Alamri, F.S. and Siddique, I.
AbstractEnergy operations and schedules are significantly impacted by load and energy forecasting systems. An effective system is a requirement for a sustainable and equitable environment. Additionally, a trustworthy forecasting management system enhances the resilience of power systems by cutting power and load-forecast flaws. However, due to the numerous inherent nonlinear properties of huge and diverse data, the classical statistical methodology cannot appropriately learn this non-linearity in data. Energy systems can appropriately evaluate data and regulate energy consumption because of advanced techniques. In comparison to machine learning, deep learning techniques have lately been used to predict energy consumption as well as to learn long-term dependencies. In this work, a fusion of novel multi-directional gated recurrent unit (MD-GRU) with convolutional neural network (CNN) using global average pooling (GAP) as hybridization is being proposed for load and energy forecasting. The spatial and temporal aspects, along with the high dimensionality of the data, are addressed by employing the capabilities of MD-GRU and CNN integration. The obtained results are compared to baseline algorithms including CNN, Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), and Bidirectional Gated Recurrent Unit (Bi-GRU). The experimental findings indicate that the proposed approach surpasses conventional approaches in terms of accuracy, Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RSME).</p> </abstract>
KeywordsGeneral Mathematics
Year2023
JournalAIMS Mathematics
Journal citation8 (9), pp. 19993-20017
PublisherAmerican Institute of Mathematical Sciences (AIMS)
ISSN2473-6988
Digital Object Identifier (DOI)https://doi.org/10.3934/math.20231019
Publication dates
Print15 Jun 2023
Publication process dates
Accepted23 May 2023
Deposited28 Jun 2023
Publisher's version
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File Access Level
Open
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