Customer Baseline Load Estimation for Incentive-Based Demand Response Using Long Short-Term Memory Recurrent Neural Network

Oyedokun, J., Bu, S. , Han, Z. and Liu, X. (2019) Customer Baseline Load Estimation for Incentive-Based Demand Response Using Long Short-Term Memory Recurrent Neural Network. In: 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), Bucharest, Romania, 29 Sep - 02 Oct 2019, ISBN 9781538682180 (doi: 10.1109/ISGTEurope.2019.8905582)

Abstract

The transition to an intelligent, reliable and efficient smart grid with a high penetration of renewable energy drives the need to maximise the utilization of customers demand response potential. The availability of smart meter data means this potential can be more accurately estimated and suitable demand response (DR) programs can be targeted to customers for load shifting, clipping and reduction. In this paper, we focus on estimating customer demand baseline for incentive-based DR. We propose a long short-term memory recurrent neural network framework for customer baseline estimation using previous like days data during DR events period. We test the proposed methodology on the publicly available Irish smart meter data and results shows a significant increase in baseline estimation accuracy when compared to traditional baseline estimation methods.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Oyedokun, James and Bu, Dr Shengrong
• Authors: Oyedokun, J., Bu, S., Han, Z., and Liu, X.
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• ISBN: 9781538682180
• Copyright Holders: Copyright © 2019 IEEE
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

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