Low-Frequency Load Identification using CNN-BiLSTM Attention Mechanism
Amanie Azzam Saba Sanami Amir G. Aghdam
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.