Ultra-short-term forecasting of agricultural intelligent greenhouse power load based on ISDS and MDUS-LSTM

Abstract

The agricultural smart greenhouse electric load differs significantly from the traditional building electric load. It is more susceptible to the influence of meteorological conditions, featuring poor regularity and significant random fluctuations, so load forecasting faces new challenges. To address this challenge, this paper focuses on a variety of meteorological and historical similar feature extraction, innovates model updating strategies, and proposes a new ultra-short-term forecasting method for agricultural smart greenhouse electric loads. Firstly, an improved similar day selection (ISDS) method is designed. This method considers both trend similarity and magnitude similarity of time series. It sets weights according to the degree of influence of different meteorological features on load, thus improving the learning efficiency of the model. Next, a model dynamic update strategy (MDUS) is designed. This strategy consists of initial training of forecasting model parameters based on historical similar daily loads and online updating of forecasting model parameters based on adjacent daily load data. Then, the forecasting model is trained online and fine-tuned with the parameters based on the adjacent daily load data. The dynamically updated forecasting model is used to achieve ultra-short-term forecasting of the electric load to improve the forecasting accuracy and adaptability of the model. Finally, the effectiveness of the proposed method is verified by actual electrical load data, real-time meteorological data, and NWP data collected in an agricultural smart greenhouse in Shouguang, China.