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

Aftab Ahmed Almani () and Xueshan Han
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Aftab Ahmed Almani: Key Laboratory of Power System Intelligent Dispatch and Control, Shandong University, Jingshi Road 17923, Jinan 250000, China
Xueshan Han: Key Laboratory of Power System Intelligent Dispatch and Control, Shandong University, Jingshi Road 17923, Jinan 250000, China

Energies, 2023, vol. 16, issue 5, 1-13

Abstract: 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.

Keywords: real-time pricing; demand response; LSTM; Australian energy market operator (AEMO) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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