IoT-Enabled Smart Home Energy Management Strategy for DR Actions in Smart Grid Paradigm

Iqbal, M. Tariq; Waseem, Muhammad; Manan, Abdul; Liaqat, Rehan; Muqeet, Abdul; Wasaya, Allah

doi:10.1109/ibcast51254.2021.9393205

Cited by 23 publications

(16 citation statements)

References 22 publications

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“…Gurobi optimizer is used to resolve the problems of MILP and MINLP [ 129 , 130 ]. The Gurobi Optimizer is commercialized for mixed-integer linear programming (MILP), quadratically constrained programming (QCP), quadratic programming (QP) [ 131 ], linear programming (LP) [ 132 ], mixed-integer quadratically constrained programming (MIQCP), and mixed-integer quadratic programming (MIQP).…”

Section: Multiple Approaches Used For Optimal Scheduling Of Campus Mi...mentioning

confidence: 99%

Sustainable Solutions for Advanced Energy Management System of Campus Microgrids: Model Opportunities and Future Challenges

Muqeet

Javed

Akhter

et al. 2022

Sensors

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Distributed generation connected with AC, DC, or hybrid loads and energy storage systems is known as a microgrid. Campus microgrids are an important load type. A university campus microgrids, usually, contains distributed generation resources, energy storage, and electric vehicles. The main aim of the microgrid is to provide sustainable, economical energy, and a reliable system. The advanced energy management system (AEMS) provides a smooth energy flow to the microgrid. Over the last few years, many studies were carried out to review various aspects such as energy sustainability, demand response strategies, control systems, energy management systems with different types of optimization techniques that are used to optimize the microgrid system. In this paper, a comprehensive review of the energy management system of campus microgrids is presented. In this survey, the existing literature review of different objective functions, renewable energy resources and solution tools are also reviewed. Furthermore, the research directions and related issues to be considered in future microgrid scheduling studies are also presented.

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Section: Multiple Approaches Used For Optimal Scheduling Of Campus Mi...mentioning

confidence: 99%

Sustainable Solutions for Advanced Energy Management System of Campus Microgrids: Model Opportunities and Future Challenges

Muqeet

Javed

Akhter

et al. 2022

Sensors

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“…Many previous research studies have focused on the energy storage and optimization for nearly-zero energy buildings. These studies try to develop an appropriate and accurate building simulation environment in order to achieve costefficient building energy management strategy without losing human comfort, and can be divided into three categories: 1) physics-based methods [12][13][14][15][16][17], 2) data-driven methods [18][19][20][21], and 3) model-free methods [22][23][24][25][26].…”

Section: B Background and Related Workmentioning

confidence: 99%

“…To solve the model-based shortages, learning-based, modelfree methods were proposed in many studies that could produce building energy management policy directly without requiring any system models [22]. A Q-value based reinforcement learning (RL) strategy considering end-users' priority is proposed in [23] for an optimal IoT-Enabled home appliances scheduling (HAS). [24] formulated an energy cost minimization problem as a Markov decision process and solved the problem based on a Deep Deterministic Policy Gradients method.…”

Section: B Background and Related Workmentioning

confidence: 99%

An Iterative Optimization and Learning-Based IoT System for Energy Management of Connected Buildings

Gao

Xiao

et al. 2022

IEEE Internet Things J.

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Buildings account for nearly 40% of primary energy and 36% of greenhouse emissions, which is one of the main factors driving climate change. Reducing energy consumption in buildings toward zero-energy buildings is a vital pillar to ensure that future climate and energy targets are reached. However, due to the high uncertainty of building loads and customer comfort demands, and extremely nonlinear building thermal characteristics, developing an effective zero-energy building energy management (BEM) technology is facing great challenges. This paper proposes a novel learning-based and iterative IoT system to address these challenges to achieve the zero-energy objective in BEM of connected buildings. Firstly, all buildings in the IoT-based BEM system share their operation data with an aggregator. Secondly, the aggregator uses these historical data to train a deep reinforcement learning model based on the Deep Deterministic Policy Gradient method. The learning model generates pre-cooling or pre-heating control actions to achieve zero-energy BEM for building heating ventilation and air conditioning (HVAC) systems. Thirdly, for solving the coupling problem between HVAC systems and building internal heat gain loads, an iterative optimization algorithm is developed to integrate physics-based and learning-based models to minimize the deviation between the on-site solar photovoltaic generated energy and the actual building energy consumption by properly scheduling building loads, electric vehicle charging cycles and the energy-storage system. Lastly, the optimal load operation scheduling is generated by considering customers' comfort requirements. All connected buildings then operate their loads based on the load operation schedule issued by the aggregator. The proposed learning-based and iterative IoT system is validated via simulation with real-world building data from the Pecan Street project.Index Terms-Internet of Things (IoT), building energy management (BEM), deep deterministic policy gradient (DDPG), deep reinforcement learning (DRL), zero energy building.

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“…The paper didn't apply the ToU pricing principle and consumer comfort was not considered. Finally, the benefits of energy management were explored in [31] where the electricity cost was investigated for IoT-enabled smart homes supplied by PV and energy storage systems combined with the electricity grid. In that study, the energy management problem was executed by considering both PV uncertainties and end-users comfort constraints.…”

Section: Literature Reviewmentioning

confidence: 99%

IoT-based Optimal Energy Management in Smart Homes using Harmony Search Optimization Technique

Saadawi

Abohamama

Alrahmawy

2022

Preprint

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The Internet of things (IoT) has a variety of application domains including smart homes. A smart home is an automated intelligent home where IoT technologies are used to remotely control home appliances, manage home energy, enhance home security, and increase the comfort of home residents. On the other hand, renewable energy resources are highly speared to cover huge electricity demand. To save more energy and maximize energy efficiency, smart homes are engaged with energy management systems. Energy management and optimization solutions can help in decreasing the overall cost of energy while optimizing all operational performance.This paper presents an IoT-based optimal energy management approach depending on the Harmony Search optimization technique to save energy usage in smart homes. PV and wind renewable energy systems are used to feed the home with electricity. These on-site energy sources help smart homes to decrease their dependence on power from the electricity grid. Energy storage systems are used to maintain energy system reliability due to the inherent randomness and intermittence of solar radiation and wind speed. The proposed algorithm depends on-demand response to the price variations in the electric energy over time and applies the Time-of-Use pricing principle for controlling household appliances. IoT technology is applied to exchange data between household appliances and the control center, in addition, to communicating both energy management systems and security systems with the control center. In this paper, the IoT system is based on ZigBee wireless technology which is described as the lowest power consumed wireless technology. The algorithm is applied to a proposed building consisting of five floors, each floor contains two apartments with a total area of 200 meters for one apartment. The obtained results prove the efficacy of the proposed Harmony Search-based optimization method in saving energy and reducing electricity bills in smart homes while satisfying the required constraints. Additionally, the performance of the proposed algorithm is validated against four AI algorithms including Genetic Algorithm, Artificial Immune System, Ant Lion Optimization, and Bat Algorithm. In addition to its simplicity in programming and formulation, the conducted comparison demonstrates a similarity in the results with an advantage in the proposed algorithm in improving both the electricity cost-saving and elapsed runtime

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IoT-Enabled Smart Home Energy Management Strategy for DR Actions in Smart Grid Paradigm

Cited by 23 publications

References 22 publications

Sustainable Solutions for Advanced Energy Management System of Campus Microgrids: Model Opportunities and Future Challenges

Sustainable Solutions for Advanced Energy Management System of Campus Microgrids: Model Opportunities and Future Challenges

An Iterative Optimization and Learning-Based IoT System for Energy Management of Connected Buildings

IoT-based Optimal Energy Management in Smart Homes using Harmony Search Optimization Technique

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