Demand Side Management with NILM in a Prepaid Smart Metering Scenario

With advanced metering infrastructure (AMI) in place, an important task on the consumer side is to apply residential load pattern recognition to provide the consumer with options to fulfill their energy requirements given various constraints, especially in a pre-payment-based energy metering system. On the utility side, Energy Management involves the planning and coordination of energy production (supply) and energy consumption (demand) to meet certain requirements taking into consideration, environmental and economic constraints. Managing of the consumer side energy is known as Demand Side Management (DSM). With Distributed Energy Resources (DERs) increasingly penetrating the system it is important to balance supply and demand at the consumer end. Monitoring and Control are important facets of demand-side management as they make possible the control loop action (measuring and actuation). Advancements in Artificial Intelligence (AI) and Machine Learning along with the concept of the Internet of Things have led to various advances in Demand Side Management. This paper introduces a framework for DSM using the concepts of Non Intrusive Load Monitoring for prepaid system to fulfill their energy requirements whatever the constraints. Supply and Demand must be met by monitoring and control of the Source based on availability and cost, and the Load based on individual consumption, curtailability and incentives.