A systematic review of the smart energy conservation system: From smart homes to sustainable smart cities

Kim, Hakpyeong; Choi, Heeju; Kang, Hyuna; An, Jongbaek; Yeom, Seungkeun; Hong, Taehoon

doi:10.1016/j.rser.2021.110755

Cited by 253 publications

(102 citation statements)

References 130 publications

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“…Micro-grids, connecting from a few buildings to a whole neighborhood, represent a technical option to enable the sharing of information and energy among buildings, providing a fundamental structure to promote waste heat recovery [8,9], energy communities and demand-response programs, but also to implement a remote control and management of buildings to decrease their energy use while enhancing occupants' comfort. Intelligent buildings or smart buildings [10][11][12], have been studied for a long time, and currently building management systems (BMS) are commonly applied also in residential buildings [13][14][15][16], at least in the form of remote monitoring and management. These advanced control systems boost building management, enabling communication between users and building appliances, through remote monitoring and control [6,17,18].…”

Section: Introductionmentioning

confidence: 99%

“…The current energy transition [3,28], pushing toward an electrification of the major final energy uses, makes these examples very interesting for new neighborhoods and cities; however, most of the existing cities are characterised by a much more complex energy structure that requires a further level of management systems, i.e., multi-carrier EMS. Typically, the networks that need to be integrated are the power grid, district heating and cooling networks (both local and city-wide), natural gas networks and data networks [12,29,30]. This integration overtakes the traditional sectorial division of the design and operation of energy systems, arising the necessity to simulate the system complexity according to a holistic approach [31] and to perform advanced operational analyses through energy modeling tools [32].…”

Section: Introductionmentioning

confidence: 99%

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Neighborhood Energy Modeling and Monitoring: A Case Study

et al. 2021

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Cities and nations worldwide are pledging to energy and carbon neutral objectives that imply a huge contribution from buildings. High-performance targets, either zero energy or zero carbon, are typically difficult to be reached by single buildings, but groups of properly-managed buildings might reach these ambitious goals. For this purpose we need tools and experiences to model, monitor, manage and optimize buildings and their neighborhood-level systems. The paper describes the activities pursued for the deployment of an advanced energy management system for a multi-carrier energy grid of an existing neighborhood in the area of Milan. The activities included: (i) development of a detailed monitoring plan, (ii) deployment of the monitoring plan, (iii) development of a virtual model of the neighborhood and simulation of the energy performance. Comparisons against early-stage energy monitoring data proved promising and the generation system showed high efficiency (EER equal to 5.84), to be further exploited.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neighborhood Energy Modeling and Monitoring: A Case Study

et al. 2021

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“…Through the smart home energy management system, users can optimize their electricity consumption and improve their comfort levels [7]. Figure 1 shows five technical functional diagrams of energy efficiency in smart homes [8]. Smart consumers are the end user members of the future smart grid and play a key role in the supply-demand balance [9].…”

Section: Computer Science Computational Methods Algorithms and Artificial Intelligence Sessionmentioning

confidence: 99%

Cost Efficient Multi-Source Energy Mixing for Renewable Energy Microgrids Using Hybrid Abcpso Algorithm

Kömürgöz¹,

Keles²

2021

Proceedings of the International Scientific Conference - Sinteza 2021

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Energy management systems can provide a variety of features and services to prosumers. One of its most important functions is to determine cost-effective energy mixing rates by evaluating the unit price and power of energy resources. This study mainly proposes a hybrid optimization based on two heuristic algorithms: Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) algorithms. A hybrid ABC-PSO algorithm has been applied to solve the energy efficient multi-source energy mixing problem in Matlab. The proposed algorithm has been tested in the simulation of an energy management system including a grid, solar panel, wind turbine, and storage unit. The results show that the proposed algorithm responds appropriately to meet the hourly changing demand of the consumer in cases of energy production fluctuations in renewable energy sources and dynamic electricity price tariff implementation of the grid. This method can provide cost efficiency by maintaining the energy balance of consumers in smart grids. The algorithm has a simple structure, thus the method provides a solution for low-cost energy management applications in the microgrid.

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“…However, although the academic research on smart city shows an increasing trend, the empirical research in the existing literature is relatively scarce. At present, the research on smart city mainly focuses on the interpretation of the concept, construction planning, evaluation index system, strategic planning, and management measures, as well as the application of new science and technology in the construction of smart city [5,6]. And, some studies have pointed out that the development of smart city can improve energy efficiency [7], as well as promoting sustainable development [8,9].…”

Section: Introductionmentioning

confidence: 99%

Can Smart City Policy Facilitate the Low‐Carbon Economy in China? A Quasi‐Natural Experiment Based on Pilot City

2021

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This paper studies the impact of the implementation of smart city policy (SCP) on the development of low-carbon economy (LCE) in China. For this purpose, we developed a nonconvex meta-frontier data envelopment analysis (DEA) approach to measure LCE and used the differences-in-difference (DID) analysis method in the econometric model to empirically analyze the impact of SCP on LCE, using the dataset of 230 cities from 2005 to 2018. The results show that the implementation of SCP can significantly improve the LCE of cities, and the dynamic effect test presents that the promotion of smart cities to low-carbon economy increases with time. In addition, SCP promotes the development of LCE by optimizing government functions and improving the efficiency of governance and the degree of implementation openness. But there is heterogeneity between different cities as follows: the implementation of SCP has a more significant effect on the promotion of LCE in central and western regions in China and large-scale cities and cities without strict environmental protection planning. Finally, the robustness test verifies the reliability of the experimental data again and puts forward conclusions and policy recommendations.

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A systematic review of the smart energy conservation system: From smart homes to sustainable smart cities

Cited by 253 publications

References 130 publications

Neighborhood Energy Modeling and Monitoring: A Case Study

Neighborhood Energy Modeling and Monitoring: A Case Study

Cost Efficient Multi-Source Energy Mixing for Renewable Energy Microgrids Using Hybrid Abcpso Algorithm

Can Smart City Policy Facilitate the Low‐Carbon Economy in China? A Quasi‐Natural Experiment Based on Pilot City

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