基于延迟回声状态网的光伏电池板温度预测方法

光伏电池温度变化影响光伏系统输出的稳定性, 精准地预测光伏电池板温度的变化趋势, 对光伏系统智能运行具有重要意义. 为了更好地预测温度的变化趋势, 本文考虑了光伏电池板温度的迟滞效应, 将先前的温度输出作为延迟项引入回声状态网中, 提出了一种基于延迟回声状态网的光伏电池板温度预测模型. 给出一个延迟回声状态网具有回声状态特性的判定条件, 使得预测模型能够稳定地预测光伏电池板温度. 同时, 建立了一套光伏多传感器监测系统, 利用该监测系统采集的数据, 训练和验证模型的准确性. 与回声状态网(Echo state network, ESN), Leaky ESN (Leaky-integrator ESN)和VML ESN (ESN with variable memory length)相比, 仿真结果表明, 本文所提出的延迟回声状态网具有更好的预测性能, 平均绝对百分比误差甚至达到3.45%.

Temperature Prediction of Photovoltaic Panels Based on Delayed Echo State Network

The temperature change of photovoltaic (PV) cells can affects the output stability of PV system, and then the temperature change trend of PV panels can be predicted accurately, which will be significance for the intelligent operation of PV system. In order to better predict the change trend of temperature, this paper takes into account the hysteresis effect of PV panels temperature, and the previous temperature output is introduced into the echo state network (ESN), and thus, an improved prediction model of PV panels temperature based on the delayed echo state network is proposed in this paper. A criterion condition for the echo state characteristic of the delayed echo state network is given, such that the prediction model can predict the temperature of the PV panels stably. At the same time, a multi-sensor monitoring system of PV is established, and the collected data by monitoring system are used to train and verify the accuracy of model. Compared with ESN, Leaky ESN (Leaky-integrator ESN) and VML ESN (ESN with variable memory length), the simulation results show that the Delay ESN has better prediction performance, and the average absolute percentage error of 3.45%.