Forecasting and Comparative Application of PV System Electricity Generation for Sprinkler Irrigation Machines Based on Multiple Models

• Published in: Agronomy (Basel) Vol. 14; no. 11; p. 2696
• Main Authors: Li, Bohan, Liu, Kenan, Cai, Yaohui, Sun, Wei, Feng, Quan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2024
• Subjects: Accuracy; Agricultural industry; Agricultural production; Agriculture; Algorithms; Alternative energy sources; Comparative analysis; Configuration management; configuration optimization; Cultural heritage; Electric power production; Energy consumption; Energy costs; Forecasting; Industry forecasts; Irrigation; Irrigation systems; Machine learning; machine learning models; Mathematical models; Neural networks; Optimization; Overflow; Photovoltaic cells; Photovoltaics; Power supply; Prediction models; PVPG prediction; Random variables; Regional development; Reliability; Renewable resources; Scale models; Solar energy; Solar energy industry; solar sprinkler irrigation machine; Sprinkler irrigation; statistical model; Support vector machines; Sustainable development; time scales; China
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy14112696

Currently, photovoltaic (PV) resources have been widely applied in the agricultural sector. However, due to the unreasonable configuration of multi-energy collaboration, issues such as unstable power supply and high investment costs still persist. Therefore, this study proposes a solution to reasonably determine the area and capacity of PV panels for irrigation machines, addressing the fluctuations in power generation of solar sprinkler PV systems under different regional and meteorological conditions. The aim is to more accurately predict photovoltaic power generation (PVPG) to optimize the configuration of the solar sprinkler power supply system, ensuring reliability while reducing investment costs. This paper first establishes a PVPG prediction model based on four forecasting models and conducts a comparative analysis to identify the optimal model. Next, annual, seasonal, and solar term scale models are developed and further studied in conjunction with the optimal model, using evaluation metrics to assess and compare the models. Finally, a mathematical model is established based on the optimal combination and solved to optimize the configuration of the power supply system in the irrigation machines. The results indicate that among the four PVPG prediction models, the SARIMAX model performs the best, as the R2 index reached 0.948, which was 19.4% higher than the others, while the MAE index was 10% lower than the others. The solar term scale model exhibited the highest accuracy among the three time scale models, the RMSE index was 4.8% lower than the others, and the MAE index was 1.1% lower than the others. After optimizing the configuration of the power supply system for the irrigation machine using the SARIMAX model based on the solar term scale, it is verified that the model can ensure both power supply reliability and manage energy overflow effectively.