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Non-intrusive load monitoring (NILM) techniques estimate the consumption of individual appliances in a household or facility, based on readings of a centralized meter. In this work a new method for multi-dimensional NILM signals is proposed—the Multi-objective NILM (MO-NILM). While classical NILM algorithms are based on a single objective function, MO-NILM classifies NILM events by solving a multi-objective optimization problem. The main idea is to model each NILM feature as an objective function, and to mutually minimize these objectives based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The presented algorithms can operate in real time using low sampling rates (0.25 Hz and lower) without training the system. In addition, the proposed algorithm is simple, and requires information on the average power signatures of each appliance. The method shows good performance in terms of standard measures when tested on the popular REDD and AMPds datasets. [ABSTRACT FROM AUTHOR]

As a response to increased power demand, electrical utilities have started to use Demand Response (DR) programs to improve the stability, efficiency and reliability of their networks. One type of DR, an Emergency Demand Response Program (EDRP), addresses stability challenges by sending curtailment orders to consumers associated with the program. There are different methods for calculating the amount of power that should be curtailed and how it should be divided among consumers. An important aspect of curtailment assignment is fairness, yet there is no agreed-upon manner of achieving fairness in curtailment allocation. This paper presents a new fairness-based EDRP for a residential area. The fairness is manifested in the algorithm by taking into account the number of times and the amount of energy a customer is asked to curtail for a predetermined period of time. The proposed algorithm is demonstrated and verified using simulations. • A set of four criteria are presented for fair curtailment of loads in emergency demand response operation. • Algorithm that takes into consideration the above criteria is developed. • The algorithm is based on a weigh function for deciding the chosen concentrator by scores. • The algorithm takes into account the time interval for each request. • Two test cases are simulated to support the theoretical method. [ABSTRACT FROM AUTHOR]