  In recent times, non-intrusive load monitoring (NILM) has emerged as an
important tool for distribution-level energy management systems owing to its
potential for energy conservation and management. However, load monitoring in
smart building environments is challenging due to high variability of real-time
load and varied load composition. Furthermore, as the volume and dimensionality
of smart meters data increases, accuracy and computational time are key
concerning factors. In view of these challenges, this paper proposes an
improved NILM technique using multi-head (Mh-Net) convolutional neural network
(CNN) under dynamic grid voltage conditions. An attention layer is introduced
into the proposed CNN model, which helps in improving estimation accuracy of
appliance power consumption. The performance of the developed model has been
verified on an experimental laboratory setup for multiple appliance sets with
varied power consumption levels, under dynamic grid voltages. Moreover, the
effectiveness of the proposed model has been verified on widely used UK-DALE
data, and its performance has been compared with existing NILM techniques.
Results depict that the proposed model accurately identifies appliances, power
consumptions and their time-of-use even during practical dynamic grid voltage
conditions.
