MyJournals Home  

RSS FeedsEnergies, Vol. 12, Pages 3560: Short-Term Load Forecasting for a Single Household Based on Convolution Neural Networks Using Data Augmentation (Energies)

 
 

17 september 2019 16:00:11

 
Energies, Vol. 12, Pages 3560: Short-Term Load Forecasting for a Single Household Based on Convolution Neural Networks Using Data Augmentation (Energies)
 




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.


Del.icio.us Digg Facebook Google StumbleUpon Twitter
 
17 viewsCategory: Biophysics, Biotechnology, Physics
 
Energies, Vol. 12, Pages 3558: Development of Renewable Energy Sources in the Context of Threats Resulting from Low-Altitude Emissions in Rural Areas in Poland: A Review (Energies)
Energies, Vol. 12, Pages 3559: Impact of Heterogeneity on the Transient Gas Flow Process in Tight Rock (Energies)
 
 
blog comments powered by Disqus


MyJournals.org
The latest issues of all your favorite science journals on one page

Username:
Password:

Register | Retrieve

Search:

Physics

Use these buttons to bookmark us:
Del.icio.us Digg Facebook Google StumbleUpon Twitter


Valid HTML 4.01 Transitional
Copyright © 2008 - 2019 Indigonet Services B.V.. Contact: Tim Hulsen. Read here our privacy notice.
Other websites of Indigonet Services B.V.: Nieuws Vacatures News Tweets Travel Photos Nachrichten Indigonet Finances Leer Mandarijn