Real-Time Pricing-Enabled Demand Response Using Long Short-Time Memory Deep Learning

Sustainable energy development requires environment-friendly energy-generating methods. Pricing system constraints influence the efficient use of energy resources. Real-Time Pricing (RTP) is theoretically superior to previous pricing systems for allowing demand response (DR) activities. The DR appro...

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Bibliographic Details
Published in:Energies (Basel) 2023-03, Vol.16 (5), p.2410
Main Authors: Almani, Aftab Ahmed, Han, Xueshan
Format: Article
Language:English
Subjects:
Accuracy
Alternative energy
Alternative energy sources
Analysis
Australian energy market operator (AEMO)
Datasets
Deep learning
demand response
Demand side management
Energy consumption
Energy development
Energy management
Energy management systems
Energy resources
Energy sources
Estimates
Forecasting
Long short-term memory
LSTM
Machine learning
Methods
Neural networks
Planning
Power
Pricing
Real time
real-time pricing
Sustainability
Sustainable development
Time series
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Summary:Sustainable energy development requires environment-friendly energy-generating methods. Pricing system constraints influence the efficient use of energy resources. Real-Time Pricing (RTP) is theoretically superior to previous pricing systems for allowing demand response (DR) activities. The DR approach has been useful for correcting supply–demand imbalances as technology has evolved. There are several systems for determining and controlling the DR. However, most of these solutions are unable to control rising demand or forecast prices for future time slots. This research provides a Real-Time Pricing DR model for energy management based on deep learning, where the learning framework is trained on demand response and real-time pricing. The study data in this article were taken from the Australian Energy Market Operator (AEMO), and the learning framework was trained over 17 years of data to forecast the real future energy price and demand. To investigate the suggested deep learning-based dynamic pricing strategy, two prediction instances are addressed: actual–predicted demand and actual–predicted price. We estimated pricing and demand outcomes using long short-term memory (LSTM), which were then greatly improved by architectural changes in the model. The findings showed that the suggested model is suitable for energy management in terms of demand and price prediction.
ISSN:1996-1073
1996-1073
DOI:10.3390/en16052410