Nowadays, electricity energy demands requested from down-stream sectors in a smart grid constantly increase. One way to meet those demands is use of home energy management systems (HEMS). By effectively scheduling major household appliances in response to demand response (DR) schemes, residents can save their electricity bills. In this paper, an advanced HEMS facilitated by a nonintrusive load monitoring (NILM) technique with an automated nondominated sorting genetic algorithm-II (NSGA-II)-based multiobjective in-home power scheduling mechanism is proposed. The NILM as an electricity audit is able to nonintrusively estimate power consumed by each of monitored major household appliances at a certain period of time. Data identified by the NILM are very useful for DR implementation. For DR implementation, the NSGA-II-based multiobjective in-home power scheduling mechanism autonomously and meta-heuristically schedules monitored and enrolled major household appliances without user intervention. It is based on an analysis of the NILM with historical data with past trends. The experimental results reported in this paper reveal that the proposed advanced HEMS with the NILM assessed in a real-house environment with uncertainties is workable and feasible.Index Terms-Data fusion, demand response (DR), energy management system, ensemble learning, nonintrusive load monitoring (NILM), power scheduling, smart grid, smart house.

In contrast with a centralized Home Energy Management System, a Non-intrusive Load Monitoring (NILM) system as an energy audit identifies power-intensive household appliances non-intrusively. In this paper, an NILM system with a novel hybrid classification technique is proposed. The novel hybrid classification technique integrates Fuzzy C-Means clustering-piloting Particle Swarm Optimization with Neuro-Fuzzy Classification considering uncertainties. In reality, household appliances or operation combinations of household appliances in a house field may be identified under similar electrical signatures. The ambiguities on electrical signatures extracted for load identification exist. As a result, the Fuzzy Logic theory is conducted. The ambiguities are addressed by the proposed novel hybrid classification technique for load identification. The proposed NILM system is examined in real lab and house environments with uncertainties. As confirmed in this paper, the proposed approach is feasible.Index Terms-Energy management system, neuro-fuzzy classification, non-intrusive load monitoring, particle swarm optimization, smart grid, smart house.

Feeder reconfiguration is a common technique that is used by distribution system operators during normal or emergency operational planning. By changing the status of switches on the distribution systems, the feeders can be reconfigured. During a feeder reconfiguration, more than one objective is considered by the distribution system operators. Due to the complexity of the reconfiguration problems, the system operators are looking for assistance from computer program that can provide adequate switching plans to reconfigure the feeders such that the desired goal can be achieved. Thus, the feeder reconfiguration is a type of discrete multi-objective optimization problems. Evolutionary programming (EP) technique is a method that can be applied to identify an optimal switching plan for feeder reconfiguration. A fitness function is required in EP for chromosome selection during reproduction process. The fitness function needs to integrate the objectives to provide a measure for each chromosome. Normalizing the objectives is a typical method for multi-objective optimizations such that these objectives are comparable. In this paper, Gray CoRrelation Analysis (GCRA) method is proposed. The proposed method is used to integrate the objectives and provide a relative measure to a particular switching plan associated with a chromosome without any prior knowledge of the system under reconfiguration. Two different distribution systems are used in this paper to demonstrate how the proposed GCRA is applied during the selection process of EP. Several simulations show that the EP can identify the solution more accurately when GCRA is applied than other methods.

No abstract

No abstract