%0 Journal Article
%T Short-Term Load Forecasting for a Single Household Based on Convolution Neural Networks Using Data Augmentation
%J Energies | An Open Access Journal from MDPI
%D 2019
%R https://doi.org/10.3390/en12183560
%X 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-Tex
%U https://www.mdpi.com/1996-1073/12/18/3560