Ensuring power quality and demand-side management through IoT-based smart meters in a developing country

Ahammed, Md. Tanvir; Khan, Imran

doi:10.1016/j.energy.2022.123747

Cited by 46 publications

(19 citation statements)

References 31 publications

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“…More than 60% of people use smart devices to support their daily activities. The phenomenon above seems consistent with the previous studies [14,15,16,17,18]. Besides the IoT applications could also be complicated and, in some cases, challenging for the users [18,19], the use of digital technology in a developing country may also relate to the digital divide issues [14,15,16,17].…”

Section: Introductionsupporting

confidence: 88%

Network Externality Effects on Behavioral Intention to Use Consumer Internet of Things Among Urban Citizens in Indonesia

Subiyakto,

Nurrachman,

Nuryasin

et al. 2023

Management Systems in Production Engineering

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One of the most popular technologies is the internet of things (IoT). It refers to the number of users and penetration in the industry (I-IoT) and consumer (C-IoT) sectors. The previous stud-ies indicated that the usage rate of the C-IoT is outperforming the I-IoT worldwide. However, the contrary indication occurred in Indonesia. Among developing countries, the spending level of IoT in Indonesia is significant, but the use level of the technology is less developed. This survey study purposed to predict what factors influence the behavior intention to use C-IoT. The researchers extended the unified theory of acceptance and use of technology (UTAUT) model by adopting the network externality aspects. Around 400 valid data were collected from urban communities in the six most populous provinces in the country. The scholars used the partial least squares structural equation modeling (PLS-SEM) method using SmartPLS 3.3 in the data analysis stage. The findings expressed that the number of users and social influence factors are not influential factors influencing behavior intention to use IoT. Besides that, the UTAUT model extension may also be one of the theoretical references for future similar studies. Practically, the findings may also be one of the considerations for the stakeholders of C-IoT implementation in Indonesia.

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Section: Introductionsupporting

confidence: 88%

Network Externality Effects on Behavioral Intention to Use Consumer Internet of Things Among Urban Citizens in Indonesia

Subiyakto,

Nurrachman,

Nuryasin

et al. 2023

Management Systems in Production Engineering

View full text Add to dashboard Cite

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“…Node-defined sub-PDEs are polynomial-converted into the Laplace form using the adapted functional derivative transformation of Operator Calculus (OC) (1).…”

Section: Ai Methods Used In Equipment Load Pq Predictionmentioning

confidence: 99%

“…Optimal consumer load (re)scheduling according to the peak RE power supply, using a time-shifting plan, can be practiced in real operation modes using the estimated PQ and RE data. Real-time load planning or PQ monitoring, considering predicted RE supply and backup state storage, is necessary to compensate for power production instabilities in user demands and secure failure-free operation of autonomous grids 1 .…”

Section: Introductionmentioning

confidence: 99%

Power quality approximation for household equipment load combinations using a stepwise growth in input parameters of AI models

Zjavka

2022

Sci Rep

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Detached off-grids, subject to the generated renewable energy (RE), need to balance and compensate the unstable power supply dependent on local source potential. Power quality (PQ) is a set of EU standards that state acceptable deviations in the parameters of electrical power systems to guarantee their operability without dropout. Optimization of the estimated PQ parameters in a day-horizon is essential in the operational planning of autonomous smart grids, which accommodate the norms for the specific equipment and user demands to avoid malfunctions. PQ data for all system states are not available for dozens of connected / switched on household appliances, defined by their binary load series only, as the number of combinations grows exponentially. The load characteristics and eventual RE contingent supply can result in system instability and unacceptable PQ events. Models, evolved by Artificial Intelligence (AI) methods using self-optimization algorithms, can estimate unknown cases and states in autonomous systems contingent on self-supply of RE power related to chaotic and intermitted local weather sources. A new multilevel extension procedure designed to incrementally improve the applicability and adaptability to training data. The initial AI model starts with binary load series only, which are insufficient to represent complex data patterns. The input vector is progressively extended with correlated PQ parameters at the next estimation level to better represent the active demand of the power consumer. Historical data sets comprise training samples for all PQ parameters, but only the load sequences of the switch-on appliances are available in the next estimation states. The most valuable PQ parameters are selected and estimated in the previous algorithm stages to be used as supplementary series in the next more precise computing. More complex models, using the previous PQ-data approximates, are formed at the secondary processing levels to estimate the target PQ-output in better quality. The new added input parameters allow us to evolve a more convenient model form. The proposed multilevel refinement algorithm can be generally applied in modelling of unknown sequence states of dynamical systems, initially described by binary series or other insufficient limited-data variables, which are inadequate in a problem representation. Most AI computing techniques can adapt this strategy to improve their adaptive learning and model performance.

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“…The index reflects the low-carbon level of power transportation, including 2 secondary indicators: ‘Energy consumption at the supply and demand side (PG1)’ and ‘Power grid construction (PG2)’, [ 51 , 52 ]. ‘Energy consumption at the supply and demand side (PG1)’ includes 2 tertiary indicators: ‘The coal consumption rate of power generation (PG11)’ and ‘The coal consumption rate of power generation (PG12)’, [ 53 , 54 ], which reflects whether the store transportation can achieve high efficiency, reduction, and electrification.…”

Section: Methodsmentioning

confidence: 99%

Influencing Factors and Realization Path of Power Decarbonization—Based on Panel Data Analysis of 30 Provinces in China from 2011 to 2019

Ren

Zhang

Fan

2022

IJERPH

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2011–2019 was the critical period of the low-carbon transformation of the power industry, reflecting the deepening influence of market mechanisms. Decarbonization of the new power system is a systematic project that needs to strengthen the top-level design and overall planning. Therefore, the paper first evaluates the decarbonization of the existing power system and controls the grid architecture, power structure, energy utilization, supply chain, and trading market to further optimize the system by strengthening the basic theoretical research of the new power system, exploring the key elements of the low-carbon development of the power system, promoting the breakthrough of the key subjects, and formulating the new power system decarbonization path. In the international push for carbon neutrality goals, identifying key factors in the decarbonization of the power system is critical to achieving low-carbon development in the power sector. Combined with the characteristics and development trends of the power industry, the five dimensions of “Power generation decarbonization (SP)”, “Energy utilization efficiency (EU)”, “Supply chain decarbonization (SC)”, and “Power grid decarbonization (PG)”, and “the Trading system (TS)” are selected to construct an evaluation index system for the power decarbonization and identify the key factors. The Analytic Network Process (ANP) Method is used to calculate the index weight and measure the decarbonization level of the power industry in 30 provinces in China from 2011 to 2019. The evaluation results reveal that the overall decarbonization level of the power industry is on the rise and has stabilized after peaking in 2016. The regression results of the systematic GMM estimation show that “the intensity of cross-regional transmission”, “the degree of carbon market participation”, “technology innovation”, and “policy support” can significantly promote power decarbonization, and different regions have heterogeneity. Therefore, we propose to achieve technological innovation and upgrading in the eastern region, strengthen the construction of smart grids in the central region, optimize the power structure in the western region, and improve the market mechanism as a whole, to form a low-carbon development path for the power industry.

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Ensuring power quality and demand-side management through IoT-based smart meters in a developing country

Cited by 46 publications

References 31 publications

Network Externality Effects on Behavioral Intention to Use Consumer Internet of Things Among Urban Citizens in Indonesia

Network Externality Effects on Behavioral Intention to Use Consumer Internet of Things Among Urban Citizens in Indonesia

Power quality approximation for household equipment load combinations using a stepwise growth in input parameters of AI models

Influencing Factors and Realization Path of Power Decarbonization—Based on Panel Data Analysis of 30 Provinces in China from 2011 to 2019

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