Journal
APPLIED SCIENCES-BASEL
Volume 11, Issue 21, Pages -Publisher
MDPI
DOI: 10.3390/app112110126
Keywords
machine learning; deep learning; short-term forecasting; industrial electricity load
Categories
Funding
- Technical University of Cluj-Napoca
Ask authors/readers for more resources
In the current trend of consumption, electricity consumption will become a high cost for end-users. This study proposes a deep learning method for accurately forecasting industrial electric usage, automate the prediction process, and optimize the operation of power systems.
In the current trend of consumption, electricity consumption will become a very high cost for the end-users. Consumers acquire energy from suppliers who use short, medium, and long-term forecasts to place bids in the power market. This study offers a detailed analysis of relevant literature and proposes a deep learning methodology for forecasting industrial electric usage for the next 24 h. The hourly load curves forecasted are from a large furniture factory. The hourly data for one year is split into training (80%) and testing (20%). The algorithms use the previous two weeks of hourly consumption and exogenous variables as input in the deep neural networks. The best results prove that deep recurrent neural networks can retain long-term dependencies in high volatility time series. Gated recurrent units (GRU) obtained the lowest mean absolute percentage error of 4.82% for the testing period. The GRU improves the forecast by 6.23% compared to the second-best algorithm implemented, a combination of GRU and Long short-term memory (LSTM). From a practical perspective, deep learning methods can automate the forecasting processes and optimize the operation of power systems.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available