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.

In a smart home linked to a smart grid (SG), demand-side management (DSM) has the potential to reduce electricity costs and carbon/chlorofluorocarbon emissions, which are associated with electricity used in today’s modern society. To meet continuously increasing electrical energy demands requested from downstream sectors in an SG, energy management systems (EMS), developed with paradigms of artificial intelligence (AI) across Internet of things (IoT) and conducted in fields of interest, monitor, manage, and analyze industrial, commercial, and residential electrical appliances efficiently in response to demand response (DR) signals as DSM. Usually, a DSM service provided by utilities for consumers in an SG is based on cloud-centered data science analytics. However, such cloud-centered data science analytics service involved for DSM is mostly far away from on-site IoT end devices, such as DR switches/power meters/smart meters, which is usually unacceptable for latency-sensitive user-centric IoT applications in DSM. This implies that, for instance, IoT end devices deployed on-site for latency-sensitive user-centric IoT applications in DSM should be aware of immediately analytical, interpretable, and real-time actionable data insights processed on and identified by IoT end devices at IoT sources. Therefore, this work designs and implements a smart edge analytics-empowered power meter prototype considering advanced AI in DSM for smart homes. The prototype in this work works in a cloud analytics-assisted electrical EMS architecture, which is designed and implemented as edge analytics in the architecture described and developed toward a next-generation smart sensing infrastructure for smart homes. Two different types of AI deployed on-site on the prototype are conducted for DSM and compared in this work. The experimentation reported in this work shows the architecture described with the prototype in this work is feasible and workable.

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