A Smart Home Energy Management Strategy Based on Demand Side Management

Iqbal, Zafar; Javaid, Nadeem; Khan, Mobushir Riaz; Khan, Farman Ali; Khan, Zahoor Ali; Qasim, Umar

doi:10.1109/aina.2016.162

Cited by 8 publications

(4 citation statements)

References 7 publications

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“…In the realm of scheduling problems, various approaches, including machine learning and artificial intelligence, are employed, necessitating tradeoffs between solution quality and computational time [2]. A critical challenge arises in the form of the resource-constrained project scheduling problem (RCPSP), involving the management of activities with limited resources and capacities [3]. Researchers have explored a multitude of methods, encompassing both heuristic and mathematical techniques, primarily focusing on time constraints [4][5][6].…”

Section: Literature Reviewmentioning

confidence: 99%

“…A common strategy involves using linear programming models to optimize demand and response, especially in microgrids, utilizing mixed integer approaches and constraints to schedule power consumption across various appliances and time slots [19]. To enhance solution quality and accuracy, optimization techniques such as particle swarm optimization and stochastic methods are employed [3].…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

A Scheduling Algorithm for Appliance Energy Consumption Optimization in a Dynamic Pricing Environment

Touhs,

Temouden,

Khallaayoun

et al. 2023

WEVJ

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This research delves into the intricate landscape of energy scheduling and optimization within microgrid and residential contexts, addressing pivotal aspects such as real-time scheduling systems, challenges in dynamic pricing, and an array of optimization strategies. This paper introduces a cutting-edge scheduling algorithm, harnessing the power of artificial neural networks driven by Long Short-Term Memory Networks, and highlights its exceptional performance, boasting a significantly lower Mean Absolute Error of 5.32 compared to conventional models. This heightened predictive accuracy translates into tangible improvements in both energy efficiency and cost savings. This study underscores the delicate balance between user satisfaction, cost reduction, and efficient scheduling for sustainable energy consumption, showcasing a remarkable 38% enhancement in optimized schedules. Further granularity revealed substantial gains in energy efficiency and cost reduction across different scheduling intensities: 11.11% in light schedules, 20.09% in medium schedules, and an impressive 38.85% in heavy schedules. However, this research does not shy away from highlighting challenges related to data quality, computational demands, and generalizability. Future research trajectories encompass the development of adaptive models tailored to diverse data qualities, enhancements in scalability for and adaptability to various microgrid configurations, the integration of real-time data, the accommodation of user preferences, the exploration of energy storage and renewables, and an imperative focus on enhancing algorithm transparency.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A Scheduling Algorithm for Appliance Energy Consumption Optimization in a Dynamic Pricing Environment

Touhs,

Temouden,

Khallaayoun

et al. 2023

WEVJ

View full text Add to dashboard Cite

show abstract

“…Also, the relationship of WTA with electricity cost was demonstrated as well. Iqbal et al (2016) proposed an efficient ECG optimisation model that include energy consumption minimisation, energy consumption cost minimisation and energy generation cost optimisation for smart home energy management based on demand-side management (DSM). The ECG model was used for balancing the total energy generation cost and two main types of energy sources: Iceg and Icd which are named macro-grid and micro-grid for smart home appliances.…”

Section: Conventional/hardware and Infrastructural Based Approachesmentioning

confidence: 99%

A Survey of Smart Grid Systems on Electric Power Distribution Network and Its Impact on Reliability

2019

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This paper presents an excerpt of a more comprehensive survey of smart grid systems on electric power distribution networks and its impact on reliability. The survey was carried out as part of the feasibility study in Nigeria to determine its enhance-ability on the smartness of a conventional (traditional) distribution network. A smart grid is not a single technology but multiplex technologies in which the combination of different areas of engineering, communication and energy management systems are done. Consequently, a comprehensive review of various approaches and their impact on reliability of the network is presented. Furthermore, this paper introduces the smart grid technology and its features, reliability impacts and emerging issues and challenges that arise from the smart grid system applications. The benefit of this comprehensive survey is to provide a reference point for educational advancement on the recently published articles in the areas of smart grid systems on electric power distribution network as well as to stimulate further research interest.

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“…We also proposed three hybrid optimization algorithms: wind-driven GA (WDGA), wind-driven GWO (WDGWO) and wind-driven BPSO (WBPSO). Part of this work was already published in [15]. The main contributions of this work are as follows:…”

Section: Introductionmentioning

confidence: 99%

A Domestic Microgrid with Optimized Home Energy Management System

et al. 2018

Self Cite

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Microgrid is a community-based power generation and distribution system that interconnects smart homes with renewable energy sources (RESs). Microgrid efficiently and economically generates power for electricity consumers and operates in both islanded and grid-connected modes. In this study, we proposed optimization schemes for reducing electricity cost and minimizing peak to average ratio (PAR) with maximum user comfort (UC) in a smart home. We considered a grid-connected microgrid for electricity generation which consists of wind turbine and photovoltaic (PV) panel. First, the problem was mathematically formulated through multiple knapsack problem (MKP) then solved by existing heuristic techniques: grey wolf optimization (GWO), binary particle swarm optimization (BPSO), genetic algorithm (GA) and wind-driven optimization (WDO). Furthermore, we also proposed three hybrid schemes for electric cost and PAR reduction: (1) hybrid of GA and WDO named WDGA; (2) hybrid of WDO and GWO named WDGWO; and (3) WBPSO, which is the hybrid of BPSO and WDO. In addition, a battery bank system (BBS) was also integrated to make our proposed schemes more cost-efficient and reliable, and to ensure stable grid operation. Finally, simulations were performed to verify our proposed schemes. Results show that our proposed scheme efficiently minimizes the electricity cost and PAR. Moreover, our proposed techniques, WDGA, WDGWO and WBPSO, outperform the existing heuristic techniques.

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A Smart Home Energy Management Strategy Based on Demand Side Management

Cited by 8 publications

References 7 publications

A Scheduling Algorithm for Appliance Energy Consumption Optimization in a Dynamic Pricing Environment

A Scheduling Algorithm for Appliance Energy Consumption Optimization in a Dynamic Pricing Environment

A Survey of Smart Grid Systems on Electric Power Distribution Network and Its Impact on Reliability

A Domestic Microgrid with Optimized Home Energy Management System

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