Non‐Intrusive Short‐Term Load Forecasting Based on VMD‐CNN‐BiLSTM

Accurately screening the characteristic factors that influence short‐term power load forecasting is an effective means to improve prediction accuracy. Non‐critical features in multidimensional datasets can make it difficult for the prediction model to distinguish electrical loads, thereby reducing model accuracy. To tackle this challenge, a novel Non‐Intrusive Load Monitoring (NILM) framework for appliance recognition is proposed to overcome the problem of distinguishable electrical load features, which combines a Variational Mode Decomposition (VMD) module, a convolutional Neural Network (CNN), and a Bidirectional Long Short‐Term Memory (BiLSTM) network. First, the original electrical power data is first decomposed by the VMD module, which excellently achieves noise reduction and stationary processing of non‐stationary load data, effectively separating valid load features from interference components and laying a high‐quality data foundation for subsequent feature extraction and forecasting. Second, CNN is adopted to extract the local spatial features of the decomposed load data for accurate appliance recognition and classification, which excels in automatically mining hidden spatial features of electrical loads and greatly improves the distinguishability of load features among different electrical equipment. Meanwhile, BiLSTM is used to capture the bidirectional temporal dependencies in the load data for short‐term power load forecasting, which surpasses traditional unidirectional time‐series models in mining long‐term and bidirectional temporal correlation of load data and makes the forecasting results more consistent with the actual operation law of electrical loads. To further exploit the model's potential, an optimised strategy based on the sparrow search algorithm (SSA) is developed to optimise the key parameters of the CNN‐BiLSTM model, which optimises the model's parameter configuration efficiently and adaptively and avoids the accuracy loss caused by manual parameter adjustment. The results show that the proposed method effectively improves the accuracy of short‐term power load forecasting.