Abstract
This paper proposes a cyber–physical approach to enhance the prediction accuracy of electricity consumption of solid electric thermal storage (SETS) system, which integrates a physical model and a data-based cyber model. In the cyber–physical model, the prediction error of the physical model is used as an input of the cyber model to further calibrate the prediction error. Firstly, customers’ behavior characteristics are extracted by the integration of K-means and one-versus-one support vector machine. Secondly, based on the behavior characteristics and ambient temperature, the physical model is developed to predict daily electricity consumption. Finally, the error levels of physical model are classified, together with the temperature and prediction values of the physical model, are selected as the inputs of the cyber model using the back propagation (BP) neural network to calibrate the results of the physical model. The effectiveness of the proposed cyber–physical model (CPM) is verified by a 1 MW SETS system. The simulation results show that, compared with the physical model (PM) and cyber model (CM), the maximum relative errors (MRE) with the CPM are reduced to 25.4% and 4.8%, respectively.
Highlights
Thermal energy storage is considered as one of the advanced energy technologies [1].Electric energy can be stored in the form of heat during off-peak demand periods and used for heating of rooms during peak demand periods
The load data of 1MW solid electric thermal storage (SETS) is used to validate the cyber–physical model (CPM), and the results show that, compared with the physical model (PM) and the cyber model (CM), the maximum relative errors (MRE) with the CPM are reduced to 25.4% and
According to the order of es (e5 > e1 > e2 > e4 > e3 ), the error is divided into 5 levels, and the data is input in the CM which is used in back propagation (BP) neural network
Summary
Thermal energy storage is considered as one of the advanced energy technologies [1]. Electric energy can be stored in the form of heat during off-peak demand periods and used for heating of rooms during peak demand periods. This paper considers the behavior characteristics into the PM to enhance the accuracy of SETS Another prediction approach of electric load is based on cyber models (CMs), such as auto-regression algorithm [10,11], fuzzy algorithm [12], support vector machine [13], extreme learning machine [14], stochastic methods [15], and multi-stages estimators of nonlinear additive models [16]. A linear regression model with the ambient temperature is proposed to predict heat load in [19] These prediction methods are based on historical data combined with multiple machine learning methods.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
