Agricultural Irrigation Load Forecasting Method Combined with AI Technology and Its Practical Application Evaluation
Abstract：This paper focuses on the agricultural irrigation load forecasting method combined with AI technology and its application evaluation. Taking Zhuolu County as the research object, it deeply explores the principles and practices of using AI algorithms such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) for agricultural irrigation load forecasting. It details the characteristics of agricultural irrigation load data, introduces the entire process of data processing, model construction, training optimization, and evaluation prediction. By comparing AI and manual forecasting results, it quantitatively evaluates the advantages of AI prediction in terms of accuracy, time cost, and economic benefits. At the same time, it analyzes existing problems in application and proposes solutions. The study shows that AI technology has achieved significant results in agricultural irrigation load forecasting, but there is still room for improvement. This research provides an important reference for smart agriculture power management.
Keywords：AI technologyagricultural irrigation load forecastinglong short-term memory networkgated recurrent unitapplication evaluation
Publication Date：2025-10-25
Online Publishing Date：2025-11-14(First online date of this platform, not the publication date of the document)
Pages：4( 40-43 )