With the increasingly serious problem of surface water environmental safety, it is of great significance to study the changing trend of reservoir water quality, and it is necessary to establish a water quality prediction and early warning system for the management and maintenance of water resources. Aiming at the problem of water quality prediction in reservoirs, a CA-NARX algorithm is designed, which combines the improved dynamic clustering algorithm with the idea of machine learning and the forward dynamic regression neural network. The improved dynamic clustering algorithm is used to classify the eutrophication degree of waterbodies according to the total phosphorus and total nitrogen content. Considering four meteorological factors, air temperature, water temperature, water surface evaporation, and rainfall, synthetically for each water quality condition, the total phosphorus and total nitrogen in the waterbody are forecasted by an improved forward NARX dynamic regression neural network. Based on this, the CA-NARX prediction algorithm can realize short period water quality prediction. Compared with the traditional support vector regression machine model, improved GA-BP neural network, and exponential smoothing method, the CA-NARX model has the least prediction error.
ABSTRACT According to the structure of photovoltaic/phase change material (PV/PCM), the mechanism of internal heat transfer, transmission, storage, and temperature control is analyzed, and a two-dimensional finite element analysis model of PV/PCM structure is established. This study is carried out on the effect of PCM thermal conductivity on internal temperature distribution characteristics of PV/PCM and temperature control characteristics of solar cells. The results show that the increase in thermal conductivity of PCM can prolong the temperature control time of solar cell in PV/PCM system, for example, when the thermal conductivity is increased from 0.2 W/(m·K) to1.5 W/(m·K) under a thickness of 4 cm, the duration when PV/PCM solar cell temperature is controlled below 40°C and extended from 52 min to 184 min. In addition, PV/PCM experimental prototypes are designed with the LA-SA-EG composite PCM peak melting point of 46°C and thermal conductivity of 0.8 W/(m·K) and 1.1 W/(m·K), respectively. The results indicate that compared with PCM-free solar cells, the maximum temperature of PV/PCM prototype solar cells with thermal conductivity of 0.8 W/(m·K) and 1.1 W/(m·K) is reduced by 10.8°C and 4.6°C, respectively, with average output power increased by 4.1% and 2.2%, respectively, under simulated light sources. Under natural light conditions, the average output power is increased by 6.9% and 4.3%, respectively. The results provide theoretical and experimental basis for the optimization of PV/PCM design by changing the thermal conductivity of PCM. 相似文献