A New Evaluation Metric for Demand Response-Driven Real-Time Price Prediction Towards Sustainable Manufacturing

Yun, Lingxiang; Li, Lin

doi:10.1115/1.4055604

A New Evaluation Metric for Demand Response-Driven Real-Time Price Prediction Towards Sustainable Manufacturing

Journal Article · Wed Oct 19 04:00:00 EDT 2022 · Journal of Manufacturing Science and Engineering

DOI:https://doi.org/10.1115/1.4055604· OSTI ID:2418830

Yun, Lingxiang ^[1]; Li, Lin

OSTI

Abstract

The increasing industry energy demand highlights the urgency of demand response management, while the emerging smart manufacturing technologies pave the way for the implementation of real-time price (RTP)-based demand response management towards sustainable manufacturing. The demand response management requires scheduling of manufacturing systems based on RTP predictions, and thus the prediction quality can directly alter the effectiveness of demand response. However, since the general price prediction algorithms and prediction evaluation metrics are not specifically designed for RTP in demand response problems, a good RTP prediction obtained and evaluated by these algorithms and metrics may not be suitable for demand response scheduling. Therefore, in this study, the relationships between the effectiveness of demand response for manufacturing systems and evaluation results from six commonly used metrics are investigated. Meanwhile, a new metric called k-peak distance (KPD), considering the characteristics of the demand response problem, is proposed and compared with the other six metrics. Furthermore, an encoder-decoder long short-term memory recurrent neural network with KPD is proposed to provide better RTP prediction for manufacturing demand response problems. The case studies indicate that the proposed KPD metric shows a 1.8–3.6 times higher correlation with the demand response effectiveness compared to the other metrics. In addition, the production schedule based on the RTP prediction obtained from the proposed algorithm can improve the effectiveness of demand response by 23.4% on average.

Research Organization:: Univ. of Illinois at Urbana-Champaign, IL (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

DOE Contract Number:: EE0009714

OSTI ID:: 2418830

Journal Information:: Journal of Manufacturing Science and Engineering, Journal Name: Journal of Manufacturing Science and Engineering Journal Issue: 2 Vol. 145; ISSN 1087-1357

Publisher:: ASME

Country of Publication:: United States

Language:: English

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