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A preliminary study towards conceptualization and implementation of a load learning model for smart automated demand response

Demand Response is an essential paradigm under the smart grid framework. The emergence of deregulated markets and dynamic pricing schemes have given an impetus to active participation from the load side. The demand patterns of a residential building show interesting trends that can provide valuable information for predicting the likelihood of occurrence of a particular load at a particular time. This work discusses a data processing and pattern recognition framework for estimating the presence of a particular load at a given instant of time. The domestic appliance data of a household in the United Kingdom has been taken and feature reduction has been applied on the same in order to characterize loads with significant consumption variation. This feature reduced data has been used to forecast the likely consumption for such loads. There are instances of large error however the instances for which load availability is given have shown the correct prediction of trend and value.