Short-Term Load Forecasting with Multi-Source Data Using Gated Recurrent Unit Neural Networks

Yixing Wang, Meiqin Liu, Zhejing Bao and Senlin Zhang
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Yixing Wang: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Meiqin Liu: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Zhejing Bao: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Senlin Zhang: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Energies, 2018, vol. 11, issue 5, 1-19

Abstract: Short-term load forecasting is an important task for the planning and reliable operation of power grids. High-accuracy forecasting for individual customers helps to make arrangements for generation and reduce electricity costs. Artificial intelligent methods have been applied to short-term load forecasting in past research, but most did not consider electricity use characteristics, efficiency, and more influential factors. In this paper, a method for short-term load forecasting with multi-source data using gated recurrent unit neural networks is proposed. The load data of customers are preprocessed by clustering to reduce the interference of electricity use characteristics. The environmental factors including date, weather and temperature are quantified to extend the input of the whole network so that multi-source information is considered. Gated recurrent unit neural networks are used for extracting temporal features with simpler architecture and less convergence time in the hidden layers. The detailed results of the real-world experiments are shown by the forecasting curve and mean absolute percentage error to prove the availability and superiority of the proposed method compared to the current forecasting methods.

Keywords: short-term load forecasting; artificial intelligence; gated recurrent unit; recurrent neural network; power grid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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