PROJECT TITLE :

Artificial-Intelligence-Based Methodology for Load Disaggregation at Bulk Supply Point

ABSTRACT:

Real-time load composition data will dramatically profit demand-facet management (DSM). Previous works disaggregate the load via either intrusive or nonintrusive load monitoring. However, because of the difficulty in accessing all homes via sensible meters in the slightest degree times and the unavailability of frequently measured high-resolution load signatures at bulk offer points, neither is suitable for frequent or widespread application. This paper employs the unreal intelligence (AI) tool to develop a load disaggregation approach for bulk supply points based mostly on the substation rms measurement without hoping on good meter data, customer surveys, or high-resolution load signatures. Monte Carlo simulation is employed to come up with the training and validation data. Load compositions obtained by the AI tool are compared with the validation data and used for load characteristics estimation and validation. Probabilistic distributions and confidence levels of different confidence intervals for errors of load compositions and cargo characteristics are derived.


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