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Energy-efficient Manufacturing Practices In Las Vegas: Strategies For Profit
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Designing a Comprehensive and Flexible Architecture to Improve Energy Efficiency and Decision Making in Managing Energy Consumption and Production in Panama
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By Ivonne Nuñez Ivonne Nuñez Scilit Preprints.org Google Scholar 1 , Elia Esther Cano Elia Esther Cano Scilit Preprints.org Google Scholar 1 , Edmanuel Cruz Edmanuel Cruz Scilit Preprints.org Google Scholar 2 and Carlos Rovetto Carlos Rovetto Scilit Preprints.org Google Scholars. people, *
Received: April 4, 2023 / Revised: April 27, 2023 / Accepted: April 28, 2023 / Published: May 5, 2023
In recent years, the integration of new elements to the electric grid, such as electric vehicles and renewable energy, has required the evolution of the electric grid as we know it, making it necessary to optimize the process of energy production, distribution and storage. . This situation gave rise to the introduction of the Smart Grid (SG), which will enable a balance between energy supply and demand, thus enabling a system where the user will also be the producer of his excess energy. Under this scenario, this work proposes an architecture whose technological components, such as the internet of things (IoT), artificial intelligence (AI), cloud computing and mobile applications, allow users to address the problem of electricity consumption and production. In the experiments conducted, results were obtained from components that support the functionality of the proposed platform.
The electricity grid is evolving, where capabilities and functionality are added to existing infrastructure to improve efficiency and quality of service. For this purpose, the processing capabilities and increasing intelligence of the current technology are being used, which gives rise to the so-called SG or smart grid. It will be the basis of the balance between energy supply and demand and the first step towards smart energy, a system where consumers will also be producers of excess energy [1, 2, 3]. In this way, the digitization of the energy sector becomes an important factor for decarbonization by increasing energy efficiency and supporting the use of renewable energy sources [4, 5, 6] through the integration of variable production technologies that improve the quality and security of supply. [7, 8], thus creating an efficient, resilient and competitive energy market [9, 10].
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SG is defined as an electric grid that can integrate the actions of all related users to be efficient in providing sustainable, economic, and safe electricity supply [11, 12]. The transformation of the grid towards a smart, secure and reliable infrastructure will enable the challenges of complete economic electrification, integration of renewable resources, sustainable mobility, and more powerful and connected consumers to be met [13].
To achieve the full potential of the electricity market in a competitive and robust manner, investments in research and innovation must be made to develop the infrastructure by implementing the necessary technologies. This paper proposes a comprehensive and flexible architectural design based on different elements of information and communication technology (ICT) that enables the processing of data produced by energy sources in the generation phase and consumption data recorded by low-cost meters. The architecture uses components such as IoT, cloud computing, AI, and mobile technology to allow users to consult their consumption information, providing the possibility to improve the efficiency of their energy use. It increases the level of understanding of disruptions in energy production and consumption, such as fluctuations introduced by system participants dynamically, thus facilitating planning that not only considers technical aspects but also how participants respond to changes in economic terms.
This work aims to present an efficient model that enables the integration of data from various sources, such as consumption meters and energy production sensors. The objective is to facilitate the interpretation and use of information on energy consumption and production in Panama using data analysis and visualization tools. With this solution, it is expected to contribute to the improvement of energy efficiency and make decisions about the management of energy consumption and production.
The structure of this study is as follows: Part 2, background; Part 3, problem definition and motivation; Part 4, materials and methods; Section 5, results and discussion; and finally, in Section 6, the conclusion.
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A smart grid is an electricity network that integrates all relevant user actions to efficiently provide a sustainable, economic and safe supply of electricity. Smart grids use information and communication technologies to collect and analyze real-time data about energy consumption, energy production and grid capacity. Data is used to optimize energy flow and reduce energy loss [14, 15, 16, 17]. Smart grids use a variety of technologies to achieve optimization of the electric power system. Some of the key technologies used in smart grids include:
Thanks to IoT nodes, the power grid gains the flexibility it needs to face a more energetic future. Smart grids make it possible to know usage and demand and to use predictive maintenance strategies [18, 19, 20, 21, 22, 23]. IoT devices offer many connectivity options in the energy infrastructure, helping to monitor all important energy assets to achieve efficient energy use. Today, it is a technology that has become a benchmark for data collection and processing systems [24, 25, 26, 27].
One of the core elements of SG is the use of IoT to increase the visibility of the entire environment, providing greater dynamism and efficiency in the collection and distribution of data, which will be used to generate optimization notes for the system [28, 29] ]. IoT is one of the core elements of SG by providing greater dynamism and efficiency in data collection and distribution and more capacity for direct interaction and control over certain parts of the network [ 30 , 31 , 32 , 33 ].
In Shahinzadeh and M. Rana’s research [34, 35], IoT integration is used to achieve reliable data transmission in communication infrastructure at different SG levels:
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The application of AI techniques enables value extraction from generation, transmission, distribution and consumption data to support decision making in power grid management, which is changing and evolving towards SG [7, 36, 37, 38]. AI, combined with analytics and monitoring systems, provides clear and complete visibility of systems in the grid, allowing preventive maintenance actions, real-time incident resolution and decision-making for operational improvements and optimization [39, 40, 41].
Some practical cases of the use of AI applications in SG can be observed in the work of Atef y Eltawil [42], who proposed two smart techniques to deal with the problem of electricity price forecasting (EPF) using machine learning. First, a support vector regression (SVR) model is used to predict hourly prices. Second, the deep learning (DL) model is implemented and compared with the SVR model. The results show that the two proposed models are effective tools for the EPF.
In the work of Ahmad [43], compare tree-based ensemble machine learning models (random forest—RF and incremental tree—ET), decision tree (DT) and support vector regression (SVR) to predict useful hourly energy from solar. heat collector system. The developed models are compared in terms of generalizability (stability), accuracy, and computational cost.
In a well-known case implementing an AI platform used for the design and operation of energy-efficient buildings (EFBs) [44], Yang et al. present an adaptive ANN that can predict the unexpected behavior of incoming data and adjust accordingly. Two models, pooled training and sliding window training, were tested against simulated and measured data. The sliding window technique has better performance in the real measurement case. For simulated data, both techniques show similar performance [45].
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Cha et al. proposed a short-term building energy consumption prediction model based on an artificial neural network (ANN) model with a Bayesian regularization algorithm to investigate the effect of network design parameters, such as time delay, number
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