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Data-driven prosumer-centric energy scheduling using convolutional neural networks

Hua, Weiqi; Jiang, Jing; Sun, Hongjian; Tonello, Andrea; Qadrdan, Meysam; Wu, Jianzhong

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Weiqi Hua

Jing Jiang

Andrea Tonello

Meysam Qadrdan

Jianzhong Wu


The emerging role of energy prosumers (both producers and consumers) enables a more flexible and localised structure of energy markets. However, it leads to challenges for the energy scheduling of individual prosumers in terms of identifying idiosyncratic pricing patterns, cost-effectively predicting power profiles, and scheduling various scales of generation and consumption sources. To overcome these three challenges, this study proposes a novel data-driven energy scheduling model for an individual prosumer. The pricing patterns of a prosumer are represented by three types of dynamic price elasticities, i.e., the price elasticities of the generation, consumption, and carbon emissions. To improve the computational efficiency and scalability, the heuristic algorithms used to solve the optimisation problems is replaced by the convolutional neural networks which map the pricing patterns to scheduling decisions of a prosumer. The variations of uncertainties caused by the intermittency of renewable energy sources, flexible demand, and dynamic prices are predicted by the developed real-time scenarios selection approach, in which each variation is defined as a scenario. Case studies under various IEEE test distribution systems and uncertain scenarios demonstrate the effectiveness of our proposed energy scheduling model in terms of predicting scheduling decisions in microseconds with high accuracy.


Hua, W., Jiang, J., Sun, H., Tonello, A., Qadrdan, M., & Wu, J. (2022). Data-driven prosumer-centric energy scheduling using convolutional neural networks. Applied Energy, 308, Article 118361.

Journal Article Type Article
Acceptance Date Dec 5, 2021
Online Publication Date Dec 31, 2022
Publication Date Feb 15, 2022
Deposit Date Dec 6, 2021
Publicly Available Date Dec 31, 2022
Journal Applied Energy
Print ISSN 0306-2619
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 308
Article Number 118361


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