Low-Frequency Load Identification using CNN-BiLSTM Attention Mechanism
This addresses energy management for utilities or consumers by improving load disaggregation with low-frequency data, but it is incremental as it adapts existing neural network techniques to a less common data type.
The paper tackled the problem of appliance-level power consumption estimation from low-frequency aggregated power data in Non-intrusive Load Monitoring, and the result showed that their hybrid CNN-BiLSTM model with attention outperformed existing methods in accuracy and computation time.
Non-intrusive Load Monitoring (NILM) is an established technique for effective and cost-efficient electricity consumption management. The method is used to estimate appliance-level power consumption from aggregated power measurements. This paper presents a hybrid learning approach, consisting of a convolutional neural network (CNN) and a bidirectional long short-term memory (BILSTM), featuring an integrated attention mechanism, all within the context of disaggregating low-frequency power data. While prior research has been mainly focused on high-frequency data disaggregation, our study takes a distinct direction by concentrating on low-frequency data. The proposed hybrid CNN-BILSTM model is adept at extracting both temporal (time-related) and spatial (location-related) features, allowing it to precisely identify energy consumption patterns at the appliance level. This accuracy is further enhanced by the attention mechanism, which aids the model in pinpointing crucial parts of the data for more precise event detection and load disaggregation. We conduct simulations using the existing low-frequency REDD dataset to assess our model performance. The results demonstrate that our proposed approach outperforms existing methods in terms of accuracy and computation time.