基于人工神经网络的PV/T热电联供系统性能预测

为研究太阳能PV/T热电联供系统的性能和针对太阳能PV/T系统复杂的能量平衡方程，搭建了太阳能PV/T系统试验台，同时建立了基于改进灰狼优化的BP神经网络(back propagation neural network model based on improved grey wolf algorithm, IGWO-BP)预测模型，在晴朗天气下进行试验，并采用该模型对系统电功率以及蓄热水箱内水温进行预测。结果显示，晴朗日系统的电效率8.7%～12.2%、热效率51.7%；预测结果与BP神经网络预测模型、基于粒子群优化的BP神经网络(back propagation neural network based on particle swarm optimization, PSO-BP)预测模型和卷积神经网络（convolutional neural network, CNN）预测模型预测结果进行比较，结果显示IGWO-BP预测模型电效率预测模型的绝对百分比误差(mean absolute percentage error, MAPE)、决定系数(determination coefficient, R ² )、均方根误差(root mean square error, RMSE)、效率因子(efficient factor, EF)和Pearson相关系数(pearson related coefficient, r )分别为4.5E-05、0.99、0.24、0.99和1.00，在储热罐温度预测中，上述指标分别为8.90E-04、0.98、0.07、0.98、0.99，均优于其他预测模型，IGWO-BP神经网络预测模型具有更好的预测性能。研究结果可为太阳能PV/T热电联供系统性能预测与优化控制提供参考。

Performance prediction of PV/T cogeneration system based on artificial neural network

Solar energy is characterized by clean and sustainable utilization, compared with oil and coal fossil fuels. A better environment can also be obtained to reduce the emissions of carbon dioxide (CO₂), sulfur dioxide (SO₂) and harmful substances. Three systems can be summarized in the utilization of solar energy: solar thermal, photovoltaic (PV), as well as photovoltaic combined heat and power (PV/T). Among them, the PV/T system can be expected to serve better performance and higher efficiency. In this study, a solar PV/T cogeneration system was established at Jiangxi Science and Technology Normal University (116°E, 28° 30'' N) in Nanchang City, Jiangxi Province, China. The whole system consisted of photovoltaic cells, collectors, circulating water pumps and heat storage tanks. Specifically, the substrate of the photovoltaic cell was installed with the cooling copper tubes, which were closely connected using thermal grease; The pipes and interfaces that connected to the system were covered with thermal insulation materials to reduce the heat loss of the working medium during operation. In the experiment in sunny weather, the cooling water first entered the cooling copper tube to absorb the heat while dissipating heat into the photovoltaic cell, then entered the collector for the secondary heating into the heat storage tank, and finally circulated the working medium in the whole system using the circulating pump. The experimental results show that the electrical efficiency of solar PV/T system was 8.7%~12.2%, and the thermal efficiency was 51.7%. At the same time, the back propagation (BP) neural network was optimized to predict the thermal power output of the solar PV/T system, according to the improved grey Wolf optimization algorithm. A pyroelectricity model was then established to predict the solar PV/T cogeneration system using the BP neural network based on the improved grey Wolf optimization algorithm (IGWO-BP). A comparison was made between the traditional BP neural network, the BP neural network based on the particle swarm optimization (PSO-BP), and the convolutional neural network (CNN). The prediction results show that the absolute percentage error (MAPE), determination coefficient (R2), root mean square error (RMSE), efficiency factor (EF), and Pearson correlation coefficient (r) of the IGWO-BP neural network prediction model were 4.5E-05, 0.99, 0.24, 0.99 and 1.00, respectively. In the prediction of heat storage tank temperature, the indexes of the model were 8.90 E-04, 0.98, 0.07, 0.98 and 0.99, respectively. All indexes were better than the traditional BP, PSO-BP and CNN prediction model. The maximum percentage error of the IGWO-BP neural network electrical prediction model was 1.86%, which was lower than 8.78% of the BP, 5.34% of PSO-BP, and 12.41% of the CNN prediction model. The maximum percentage error of the thermal prediction model was 0.29%, which was smaller than the BP neural network of 2.45%, PSO-BP of 2.44%, and CNN of 4.28%.