Integrated energy management system employing pre-emptive priority based load scheduling (PEPLS) approach at residential premises

Krishnamoorthy, Murugaperumal; Raj, P. Ajay-D-Vimal

doi:10.1016/j.energy.2019.07.145

Cited by 22 publications

(13 citation statements)

References 32 publications

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“…In contrast, loads with low priority are restricted. Another pre-emptive preference-based approach was developed to investigate appliance scheduling [84] . In this approach, equipment was segregated into three categories; shift-able, non-shift-able (NS), and interruptible non-shift-able equipment.…”

Section: Appliance Scheduling Based Energy Management Schemes For a S...mentioning

confidence: 99%

Analysis of energy management schemes for renewable-energy-based smart homes against the backdrop of COVID-19

Ayub

Ayob

Tan

et al. 2022

Sustainable Energy Technologies and Assessments

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Section: Appliance Scheduling Based Energy Management Schemes For a S...mentioning

confidence: 99%

Analysis of energy management schemes for renewable-energy-based smart homes against the backdrop of COVID-19

Ayub

Ayob

Tan

et al. 2022

Sustainable Energy Technologies and Assessments

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“…In AS-REMS, power consumption of an appliance a ∈ L at a ts t ∈ T of a day d ∈ D is defined in Equation (2).…”

Section: Appliancesmentioning

confidence: 99%

“…Some REM studies in the literature dealt with cost reduction and keeping user comfort simultaneously. Through these studies, the work in [ 2 ] proposed a pre-emptive priority-based load scheduling approach at residential premises, while a REM algorithm using reinforcement learning and an artificial neural network was presented in [ 3 ]. The work in [ 4 ] demonstrated that comfort and energy consumption can be partially decoupled by an adaptive indoor comfort management approach.…”

Section: Introductionmentioning

confidence: 99%

An Appliance Scheduling System for Residential Energy Management

Ozkan

2021

Sensors

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In this work, an Appliance Scheduling-based Residential Energy Management System (AS-REMS) for reducing electricity cost and avoiding peak demand while keeping user comfort is presented. In AS-REMS, based on the effects of starting times of appliances on user comfort and the user attendance during their operations, appliances are divided into two classes in terms of controllability: MC-controllable (allowed to be scheduled by the Main Controller) and user-controllable (allowed to be scheduled only by a user). Use of all appliances are monitored in the considered home for a while for recording users’ appliance usage preferences and habits on each day of the week. Then, for each MC-controllable appliance, preferred starting times are determined and prioritized according to the recorded user preferences on similar days. When scheduling, assigned priorities of starting times of these appliances are considered for maintaining user comfort, while the tariff rate is considered for reducing electricity cost. Moreover, expected power consumptions of user-controllable appliances corresponding to the recorded user habits and power consumptions of MC-controllable appliances corresponding to the assigned starting times are considered for avoiding peak demand. The corresponding scheduling problem is solved by Brute-Force Closest Pair method. AS-REMS reduces the peak demand levels by 45% and the electricity costs by 39.6%, while provides the highest level of user comfort by 88%. Thus, users’ appliance usage preferences are sustained at a lower cost while their comfort is kept impressively.

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“…However, in such cases, the energy consumption may exceed the limit, or they do not consider the preference of appliances. Recently, several schemes presented with concepts of incorporating the appliances' priorities [28,29]. However, assigning appliances priorities and load scheduling is a complex job due to the stochastic behavior of humans.…”

Section: Smart Home Appliances Scheduling: a Single User Scenariomentioning

confidence: 99%

Real-Time Scheduling of Operational Time for Smart Home Appliances Based on Reinforcement Learning

2020

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The scheduling of the operational time of household appliances requires several parameters to be tuned according to the available energy supplied to a smart home. However, scheduling of operational time of multiple appliances in a smart home itself is the NP-hard problem and thus requires an intelligent, heuristic method to be solved in polynomial time. In this research work, we propose Real-time Scheduling of Operational Time of Household Appliances based on the well-known value iterative reinforcement learning called Quality learning (RSOTHA-QL). The proposed RSOTHA-QL scheme operates in two phases. In the first phase, the agents of the Q learning act by interacting with the smart home environment and obtain a reward. The reward value is further utilized to schedule the operational time of household appliances in the next state ensuring minimum energy consumption. In the second phase, the dissatisfaction arises due to scheduling of operational time of the household appliances of the home user is maintained by categorizing the household appliances into three groups: 1) deferrable, 2) non-deferrable, and 3) controllable. Besides, using the shared memory synchronization phenomenon, the agents attached to each appliance of the smart home become coordinated. The simulation and experiments are performed in a smart home scenario comprised of a single user and multiple appliances. As compared with our previous research work using the Least Slack Time (LST) scheduling algorithm and scheduling based on demand-response strategy, it is revealed that the operational time of the household appliances is efficiently scheduled to reduce the energy consumption and dissatisfaction level of the home users significantly.

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Integrated energy management system employing pre-emptive priority based load scheduling (PEPLS) approach at residential premises

Cited by 22 publications

References 32 publications

Analysis of energy management schemes for renewable-energy-based smart homes against the backdrop of COVID-19

Analysis of energy management schemes for renewable-energy-based smart homes against the backdrop of COVID-19

An Appliance Scheduling System for Residential Energy Management

Real-Time Scheduling of Operational Time for Smart Home Appliances Based on Reinforcement Learning

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