Residential Consumer-Centric Demand-Side Management Based on Energy Disaggregation-Piloting Constrained Swarm Intelligence: Towards Edge Computing

Abstract

The emergence of smart Internet of Things (IoT) devices has highly favored the realization of smart homes in a down-stream sector of a smart grid. The underlying objective of Demand Response (DR) schemes is to actively engage customers to modify their energy consumption on domestic appliances in response to pricing signals. Domestic appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption intelligently. Besides, to residential customers for DR implementation, maintaining a balance between energy consumption cost and users’ comfort satisfaction is a challenge. Hence, in this paper, a constrained Particle Swarm Optimization (PSO)-based residential consumer-centric load-scheduling method is proposed. The method can be further featured with edge computing. In contrast with cloud computing, edge computing—a method of optimizing cloud computing technologies by driving computing capabilities at the IoT edge of the Internet as one of the emerging trends in engineering technology—addresses bandwidth-intensive contents and latency-sensitive applications required among sensors and central data centers through data analytics at or near the source of data. A non-intrusive load-monitoring technique proposed previously is utilized to automatic determination of physical characteristics of power-intensive home appliances from users’ life patterns. The swarm intelligence, constrained PSO, is used to minimize the energy consumption cost while considering users’ comfort satisfaction for DR implementation. The residential consumer-centric load-scheduling method proposed in this paper is evaluated under real-time pricing with inclining block rates and is demonstrated in a case study. The experimentation reported in this paper shows the proposed residential consumer-centric load-scheduling method can re-shape loads by home appliances in response to DR signals. Moreover, a phenomenal reduction in peak power consumption is achieved by 13.97%.

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

“…The same authors subsequently proposed a model of a residential consumer-centric Demand-Side Management [123], employing NILM, achieving, in simulations, a significant reduction (14%) of the Peak-to-Average Ratio (PAR). For future implementations, the authors proposed employing edge/IoT-based computing, in order to improve cloud computing technologies [124].…”

Novel smart home system architecture facilitated with distributed and embedded flexible edge analytics in demand‐side management

“…The designed distributed and embedded flexible HES devices wirelessly networked together with the edge HG in the architecture in this paper will be further developed and used to form a multiagent system and to work in parallel for advanced SHT/decision making, in contrast to centralized smart home systems and cloud‐centered IoT analytics…”

Electrical Energy Management Based on a Hybrid Artificial Neural Network-Particle Swarm Optimization-Integrated Two-Stage Non-Intrusive Load Monitoring Process in Smart Homes

“…The conventional PSO method [18][19][20][21][22][23], inspired by the swarming or collaborative behavior of biological populations and developed by Dr. Eberhart and Dr. Kennedy in 1995, is a populationbased stochastic optimization technique. PSO is similar to Genetic Algorithms (GAs) in the sense that The adjustable parameters (that is, the weighting connections) of the BP-ANN network in Figure 5 include v qj and w iq .…”

“…PSO is similar to Genetic Algorithms (GAs) in the sense that these two meta-heuristics are population-based search methods. Compared with the GAs solving engineering optimization problems, in which the search space is highly modal, discontinuous, and/or constrained [18], the PSO technique that is able to solve the same engineering optimization problems addressed by the GA has the following three advantages: First, there are no explicit evolution operators-such as selection operations, crossover operations, and mutation operations. Second, it has a low probability of solutions falling into local optimization regions.…”

“…In each generation of the optimization process, each particle is updated by p best and g best , where p best is the best solution (from the local view) that has been achieved so far and g best is the best solution (from the global view) that is obtained currently by any particle in the population. After finding p best and g best , the particle updates its velocity and position as follows [18].…”