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Open AccessArticle

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

1
Department of Electronics Engineering, Mokpo National University, Muan 58554, Korea
2
School of Electrical and Electronic Engineering, Gwangju University, Gwangju 61743, Korea
3
Department of Information and Electronics Engineering, Mokpo National University, Muan 58554, Korea
*
Author to whom correspondence should be addressed.
Energies 2019, 12(18), 3560; https://doi.org/10.3390/en12183560
Received: 16 August 2019 / Revised: 11 September 2019 / Accepted: 15 September 2019 / Published: 17 September 2019
(This article belongs to the Special Issue Short-Term Load Forecasting 2019)
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. View Full-Text
Keywords: data augmentation; convolution neural network; residential load forecasting data augmentation; convolution neural network; residential load forecasting
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MDPI and ACS Style

Acharya, S.K.; Wi, Y.-M.; Lee, J. Short-Term Load Forecasting for a Single Household Based on Convolution Neural Networks Using Data Augmentation. Energies 2019, 12, 3560. https://doi.org/10.3390/en12183560

AMA Style

Acharya SK, Wi Y-M, Lee J. Short-Term Load Forecasting for a Single Household Based on Convolution Neural Networks Using Data Augmentation. Energies. 2019; 12(18):3560. https://doi.org/10.3390/en12183560

Chicago/Turabian Style

Acharya, Shree K.; Wi, Young-Min; Lee, Jaehee. 2019. "Short-Term Load Forecasting for a Single Household Based on Convolution Neural Networks Using Data Augmentation" Energies 12, no. 18: 3560. https://doi.org/10.3390/en12183560

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