Short-Term Load Forecasting for a Single Household Based on Convolution Neural Networks Using Data Augmentation

Shree Krishna Acharya, Young-Min Wi and Jaehee Lee
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Shree Krishna Acharya: Department of Electronics Engineering, Mokpo National University, Muan 58554, Korea
Young-Min Wi: School of Electrical and Electronic Engineering, Gwangju University, Gwangju 61743, Korea
Jaehee Lee: Department of Information and Electronics Engineering, Mokpo National University, Muan 58554, Korea

Energies, 2019, vol. 12, issue 18, 1-19

Abstract: Advanced metering infrastructure (AMI) is spreading to households in some countries, and could be a source for forecasting the residential electric demand. However, load forecasting of a single household is still a fairly challenging topic because of the high volatility and uncertainty of the electric demand of households. Moreover, there is a limitation in the use of historical load data because of a change in house ownership, change in lifestyle, integration of new electric devices, and so on. The paper proposes a novel method to forecast the electricity loads of single residential households. The proposed forecasting method is based on convolution neural networks (CNNs) combined with a data-augmentation technique, which can artificially enlarge the training data. This method can address issues caused by a lack of historical data and improve the accuracy of residential load forecasting. Simulation results illustrate the validation and efficacy of the proposed method.

Keywords: data augmentation; convolution neural network; residential load forecasting (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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