Roshan Godaliyadda

TL;DR: A residential power consumption forecasting mechanism, which can predict the total active power demand of an aggregated set of houses, 5 min ahead of real time, was successfully formulated and implemented utilizing the proposed AUP based technique.

TL;DR: The presented results demonstrate the ability of the proposed NILM method to accurately identify and disaggregate individual energy contributions of turned-on appliance combinations in real households.

TL;DR: In this article, the authors proposed a novel non-intrusive load monitoring (NILM) method for a consumer premises with a residentially installed solar plant, which simultaneously identifies the amount of solar power influx as well as the turned ON appliances, their operating modes, and power consumption levels.

TL;DR: The proposed RT-NILM algorithm was implemented to maintain high accuracy levels even under severe supply voltage fluctuations, and a fast deconvolution based technique was introduced for the disaggregation of individual power levels of active appliances in an computationally efficient manner.

TL;DR: A methodology is proposed that can generate a multitude of network topologies that have statistically similar characteristics to a selected cohort of existing networks and facilitates an efficient process for the utility supplier to determine the impact of incorporating new PV connections without the need for extensive studies.