Fine-Grained Agricultural Facility Power Forecasting Based on Empirical Mode Decomposition

With the popularization of intelligent agricultural facilities, the demand for electricity in modern agricultural systems has also increased. To meet the continuous demand for electricity in agricultural production, including crop growth, storage, and processing, fine-grained electricity load forecasting becomes crucial, which can provide crucial decision support for the power supply, allocation, and management of agricultural facilities. However, the electricity load data in agricultural facilities is a non-stationary time series, which presents significant challenges for achieving accurate and effective forecasting. Thus, we focus on investigating the electricity load data in agricultural facilities and incorporate covariates, such as temperature, humidity, wind speed, and rainfall, into our analysis. Specifically, we propose a deep learning model based on empirical mode decomposition called EMD-BiLSTM-DLSTM. This model initially decomposes the electricity load time series into a sequence of relatively stationary components using empirical mode decomposition. It then employs a bidirectional long short-term memory network to predict each component, obtaining preliminary prediction results. Finally, a deep long short-term memory network is applied to refine the prediction results by incorporating covariates, resulting in more accurate prediction results. Experimental results show that compared with other time series forecasting methods, the proposed model has significant advantages in prediction accuracy and correlation.