Interactive search algorithm of artificial intelligence for household classification on smart electricity meter data

Smart grid (SG) is a future-generation power system commonly used to maintain electricity demand in a reliable and economic way using the latest information and communication technologies. It enables consumers and micro-energy producers to take a more active role in the electricity market and dynamic energy management (DEM). To maximise the accuracy of dynamic energy detection, research work designs an enhanced swarm-based big data analytics model for DEM in SG. In this paper, an artificial neural network (ANN)-based classification model for predicting future power consumption is proposed. A novel bio-inspired optimisation namely interactive search algorithm (ISA) is used for optimising the weights of the ANN. The results of the proposed model are compared with different performance measures to prove its efficiency. A detailed comparative results analysis takes place and the experimental results ensured the betterment of the proposed models over the state of art techniques. The compared results show the significance of the proposed model over existing algorithms.