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Regulation capability evaluation of individual electric heating load based on RBF neural network

ABSTRACT
As a time-shifting load that is gradually popularized in the northern region, electric heating load has great adjustment potential. Because the electric heating operation characteristics are affected by many nonlinear factors, the traditional equivalent thermal parameters (ETP) model cannot accurately evaluate the regulation capability of individual electric heating load. Aiming at this problem, this paper proposes an evaluation method for the regulation capability of individual electric heating load based on radial basis function (RBF) neural network. Firstly, electric heating load control experiments were carried out in a typical room of a residential quarter in winter and relevant experimental data were collected. Then, based on the operation data, the RBF neural network is used to evaluate the regulation capability of the individual electric heating load. Finally, the evaluation results based on RBF neural network are compared with those based on back propagation (BP) neural network and ETP model. The results show that the proposed method has the least evaluation error and can more accurately evaluate the regulation capability of individual electric heating load.
KEYWORDS
PAPER SUBMITTED: 2019-01-04
PAPER REVISED: 2019-03-05
PAPER ACCEPTED: 2019-03-10
PUBLISHED ONLINE: 2019-05-18
DOI REFERENCE: https://doi.org/10.2298/TSCI190104196Z
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