Affiliations: School of Information Engineering, Nanchang University, Nanchang, Jiangxi, China
Correspondence:
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Corresponding author: Dongmin Yu, School of Information Engineering, Nanchang University, Nanchang, Jiangxi 330000, China. E-mail: d.yu@neepu.edu.cn.
Abstract: As a new type of energy which is developing vigorously in China, nuclear energy has been widely concerned in all aspects. The circulating water system in the nuclear power plant takes water from seawater, cools the steam engine through the condenser, and then carries waste heat from the outlet to the sea. If the temperature of the outlet is too high, it will not only cause the temperature rise near the water surface of the atmosphere and the ground layer near the shore, but also affect the ecological environment inside the ocean. In this paper, a model based on the echo state network with variable memory length (VML-ESN) is proposed to predict outlet temperature of the nuclear power plant. It can get memory according to the different input autocorrelation characteristic length to adjust the status update equation. The simulation results show that compared with ESN, Leaky-ESN, and Twi-ESN, the proposed model has better prediction performance, with a MAPE of 3.42%. In addition, when the reservoir size is 40, the error of VML-ESN is smaller than that of other models.
Keywords: Nuclear power, temperature, echo state network, prediction
