Abstract
Non-intrusive load disaggregation (NILD) is of great significance to the development of smart grids. Current energy disaggregation methods extract features from sequences, and this process easily leads to a loss of load features and difficulties in detecting, resulting in a low recognition rate of low-use electrical appliances. To solve this problem, a non-intrusive sequential energy disaggregation method based on a multi-scale attention residual network is proposed. Multi-scale convolutions are used to learn features, and the attention mechanism is used to enhance the learning ability of load features. The residual learning further improves the performance of the algorithm, avoids network degradation, and improves the precision of load decomposition. The experimental results on two benchmark datasets show that the proposed algorithm has more advantages than the existing algorithms in terms of load disaggregation accuracy and judgments of the on/off state, and the attention mechanism can further improve the disaggregation accuracy of low-frequency electrical appliances.
Highlights
Load disaggregation technology is a key technology in smart grids [1]
Non-intrusive load disaggregation (NILD) based on a combinatorial optimization algorithm [7] determines the power consumption value of each appliance by investigating load characteristics as well as error comparisons between power states of combined appliances and the total power
Since the neural networks were only trained for each appliance and the computational cost was high, Anthony [26] proposed UNet-NILM for multi-task appliances’ state detection and power estimation, which had a good performance compared with traditional single-task deep learning
Summary
Load disaggregation technology is a key technology in smart grids [1]. Traditional load monitoring adopts intrusive methods, which are able to obtain accurate and reliable data with low data noise [2], but they are difficult to be accepted by users due to their high implementation costs. It mainly uses non-intrusive load disaggregation (NILD) In this method, the total power consumption is disaggregated to each individual electrical appliance. NILD based on a combinatorial optimization algorithm [7] determines the power consumption value of each appliance by investigating load characteristics as well as error comparisons between power states of combined appliances and the total power. Akhilesh [25] proposed a multilayer deep neural network based on the sequence-to-sequence methodology, and the algorithm, by reading the daily load profile for the total power consumption, could identify the state of the appliances according to the device-specific power signature. Since the neural networks were only trained for each appliance and the computational cost was high, Anthony [26] proposed UNet-NILM for multi-task appliances’ state detection and power estimation, which had a good performance compared with traditional single-task deep learning. Experimental results on two benchmark datasets show that our method is superior to other present methods
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
