A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model Article Swipe

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Lizhen Wu , Kong Chun , Xiaohong Hao , Wei Chen ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.1155/2020/1428104

Short-term load forecasting (STLF) plays a very important role in improving the economy and stability of the power system operation. With the smart meters and smart sensors widely deployed in the power system, a large amount of data was generated but not fully utilized, these data are complex and diverse, and most of the STLF methods cannot well handle such a huge, complex, and diverse data. For better accuracy of STLF, a GRU-CNN hybrid neural network model which combines the gated recurrent unit (GRU) and convolutional neural networks (CNN) was proposed; the feature vector of time sequence data is extracted by the GRU module, and the feature vector of other high-dimensional data is extracted by the CNN module. The proposed model was tested in a real-world experiment, and the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the GRU-CNN model are the lowest among BPNN, GRU, and CNN forecasting methods; the proposed GRU-CNN model can more fully use data and achieve more accurate short-term load forecasting.

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Convolutional Neural Network

Computer Science

Artificial Intelligence

Support Vector Machine

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Mean absolute percentage error Mean squared error Convolutional neural network Artificial neural network Computer science Term (time) Artificial intelligence Feature (linguistics) Pattern recognition (psychology) Cellular neural network Support vector machine Data mining Mathematics Statistics Quantum mechanics Physics Linguistics Philosophy

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Type: article
Language: en
Landing Page: https://doi.org/10.1155/2020/1428104
PDF: https://downloads.hindawi.com/journals/mpe/2020/1428104.pdf
OA Status: hybrid
Cited By: 201
References: 34
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DOI: https://doi.org/10.1155/2020/1428104

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Title: A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model

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Language: en

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Publication year: 2020

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Publication date: 2020-03-21

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Authors: Lizhen Wu, Kong Chun, Xiaohong Hao, Wei Chen

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Concepts: Mean absolute percentage error, Mean squared error, Convolutional neural network, Artificial neural network, Computer science, Term (time), Artificial intelligence, Feature (linguistics), Pattern recognition (psychology), Cellular neural network, Support vector machine, Data mining, Mathematics, Statistics, Quantum mechanics, Physics, Linguistics, Philosophy

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Cited by: 201

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