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Non-intrusive Appliance Load Monitoring for Electrical Energy Systems Simulation and Analysis – A case study in India
Published in Elsevier B.V.
2016
Volume: 38
   
Pages: 2061 - 2066
Abstract
Residential customers play an important role in total power consumption with a highly varying demand profile depending upon climatic conditions. With recent technological advancements, it is possible to deploy Renewable Energy Sources (RES) at customer end to support their power demand. To take advantage of this opportunity, it is important to understand the load profile of residential users. Also, deployment of RES at customer end will lead to a more decentralized power grid adding to the complexity of the system. Modelling and analysis of the resulting system is therefore necessary for optimal electric energy utilization. Bottom up approaches estimates the demand profile of customers which is then utilized to obtain consumption patterns at distribution and generation levels. Demand profile of residential users could be estimated using a technique known as Non-intrusive Load Monitoring (NILM). NILM disaggregates the aggregate power consumption measured at utility entry point to identify the operational states of individual appliance. In this work, we propose an NILM technique which uses Neural Network (NN) along with Hidden Markov Model (HMM) to detect the operating state of an appliance. The appliance level consumption obtained using this approach for a community can be then utilized for developing bottom up models for energy system simulation. © 2016 Elsevier B.V.
About the journal
JournalData powered by TypesetComputer Aided Chemical Engineering
PublisherData powered by TypesetElsevier B.V.
ISSN15707946
Open AccessNo