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Emissions of greenhouse gases such as CO2 are rapidly increasing, causing the greenhouse effect, leading to global warming and serious impacts on the ecosystem, while photovoltaic power generation can reduce the large amount of pollutants and carbon emissions produced in the traditional power generation process. The article takes the photovoltaic power generation system of the national field observatory as the research object, and uses PVsyst software to design the system of two distributed photovoltaic power generation systems. After calculating and analysing the total power of the experimental equipment, the efficiency of photovoltaic power generation and the system cost of the field observatory, the scheme and scale of the photovoltaic power generation system are determined, and the construction of the photovoltaic power generation test system is completed. Finally, through the comparison and analysis of the one-year PV power generation data with the simulated data, the feasibility of the system design method is verified, which can meet the requirements of the field experimental station and create a zero-carbon field observatory to achieve green energy conservation and sustainable energy use, which is in line with the strategic development plan of carbon peaking and carbon neutrality promoted by the state and provides a design basis and useful reference for the construction of zero-carbon field experimental stations.

Forests have ecological functions such as purifying the atmosphere, fixing carbon and releasing oxygen, conserving water sources, preventing wind and sand invading, and harbouring biodiversity. At the same time, they provide human beings with materials and energy, and thus are important for economic and societal development. However, in our national power grid system, trees might cause interference on power transformation lines. Furthermore, if these circumstances cannot be responded timely, huge economic losses would happen. Therefore, research of forest growth models can more accurately predict the growth and the dynamics of forests, as well as provide a reference for the power warning system. This study conducts a forecast simulation study on the growth of eucalyptus in Guangzhou province, China, proposes a multivariate statistical analysis to predict tree species growth, and then compares its performances with traditional Logistic model results. We found that our multivariate statistical model can effectively consider various environmental influencing factors, thus predict tree height excellently (R2=0.9776), so as provide new foundations for tree height prediction.