Designing a Comprehensive and Flexible Architecture to Improve Energy Efficiency and Decision-Making in Managing Energy Consumption and Production in Panama

Nunez, Ivonne; Cano, Elia; Cruz, Edmanuel; Rovetto, Carlos

doi:10.3390/app13095707

Cited by 3 publications

(3 citation statements)

References 77 publications

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“…With the MoSCoW method [50], requirements were classified on a scale of 1 to 4, where 1 indicates critical elements, and 4 indicates features not included in the current version (see Table 6). This process allowed us to objectively categorize the requirements as essential, necessary, or optional, facilitating the definition of final technical requirements for developing a functional, accessible, and technically feasible interface [51].…”

Section: Requirementsmentioning

confidence: 99%

Improving Usability in Mobile Apps for Residential Energy Management: A Hybrid Approach Using Fuzzy Logic

Nuñez,

Cano,

Cruz

et al. 2024

Applied Sciences

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This paper presents a study that evaluates the usability and user experience of a mobile application interface for residential energy management, adopting a hybrid approach that integrates quantitative and qualitative methods within a user-centered design framework. For the evaluation, metrics and tools such as the User Experience Questionnaire Short (UEQ-S) and the System Usability Scale (SUS) were used, in addition to the implementation of a fuzzy logic model to interpret and contrast the data obtained through these metrics, allowing a more accurate assessment of usability and user experience, reflecting the variability and trends in the responses. Three aspects evaluated stand out: satisfaction with the interface, ease of use, and efficiency. These are fundamental to understanding how users perceive the system. The results indicate a high likelihood of user recommendation of the system and a high overall quality of user experience. This study significantly contributes to mobile application usability, especially in residential energy management, offering valuable insights for designing more intuitive and effective user interfaces on mobile devices.

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Section: Requirementsmentioning

confidence: 99%

Improving Usability in Mobile Apps for Residential Energy Management: A Hybrid Approach Using Fuzzy Logic

Nuñez,

Cano,

Cruz

et al. 2024

Applied Sciences

Self Cite

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“…This gives building blocks for madding suitable planning, implementation, evaluation and overall management to obtain a desired goal concerning with the problems and issues for a specific market and area conditions; and then this provides ways for its implementation increasingly in a large scale (area). For example, in electric market, demand side management started from California of the USA in 1970s with energy issues and at present, it is increasingly spreading over the world (11) .…”

Section: Fig 1 Geo-political Representation Of Dhar Districtmentioning

confidence: 99%

A Review on Demand Side Management in an Indian Village: Scope and Challenges

Sisodiya,

Tanwar,

Prajapat

2023

IJST

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Objectives: This study reviews the scopes and challenges of Integrated Resource Planning in power distribution system of an Indian tribal village Dedali-B. This study supports to the management of the demand of power, especially in rural place. Method: The optimal scheduling of the studied Demand Side Management has been observed using Binary Particle Swarm Optimization taking the management of the demand in mind. The BPSO is capable to handle binary variable hand has the property of parallelism, scalability and adaptability and converge to good solutions quickly when Demand Side Management is concerned. Findings: The Integrated resource planning in power distribution system has been conducted for minimizing of energy consumption in this paper through simulation. It is found that in village Dedali-B, through scheduling of the loads using BPSO, the bill of energy consumption reduces to ~15% when commercial loads are considered. Also, it was observed through the comparative study of residential and agricultural loads before and after demand response that the peak load is reduced to 9.12% while cost is reduced 6.57%. The peak to average ratio is reduced 21.95%. Novelty: A specific ideology suitable for implementation of integrated resource planning through optimization and DMS supports the demand management and reduces the costs. It has been examined through a test case of tribal village Dedali-B of Dhar district in India.

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“…Here, the physical meaning of the arrangement used for the electric circuit is not linked with the real working principles of the device but instead helps provide the relevant information needed as output. The second approach is derived from the already described approach but exploits the advantages of AI [11,[16][17][18]. Applying Machine Learning (ML) techniques enables the simultaneous achievement of two distinct objectives.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles

Miraftabzadeh,

Longo,

Di Martino

et al. 2024

Electronics

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The integration of Artificial Intelligence (AI) in Energy Storage Systems (ESS) for Electric Vehicles (EVs) has emerged as a pivotal solution to address the challenges of energy efficiency, battery degradation, and optimal power management. The capability of such systems to differ from theoretical modeling enhances their applicability across various domains. The vast amount of data available today has enabled AI to be trained and to predict the behavior of complex systems with a high degree of accuracy. As we move towards a more sustainable future, the electrification of vehicles and integrating electric systems for energy storage are becoming increasingly important and need to be addressed. The synergy of AI and ESS enhances the overall efficiency of electric vehicles and plays a crucial role in shaping a sustainable and intelligent energy ecosystem. To the best of the authors’ knowledge, AI applications in energy storage systems for the integration of electric vehicles have not been explicitly reviewed. The research investigates the importance of AI advancements in energy storage systems for electric vehicles, specifically focusing on Battery Management Systems (BMS), Power Quality (PQ) issues, predicting battery State-of-Charge (SOC) and State-of-Health (SOH), and exploring the potential for integrating Renewable Energy Sources with EV charging needs and optimizing charging cycles. This study examined all topics to identify the most commonly used methods, which were analyzed based on their characteristics and potential. Future trends were identified by exploring emerging techniques introduced in recent literature contributions published since 2017.

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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

Cited by 3 publications

References 77 publications

Improving Usability in Mobile Apps for Residential Energy Management: A Hybrid Approach Using Fuzzy Logic

Improving Usability in Mobile Apps for Residential Energy Management: A Hybrid Approach Using Fuzzy Logic

A Review on Demand Side Management in an Indian Village: Scope and Challenges

Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles

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