An Optimization Theory of Home Occupants’ Access Data for Determining Smart Grid Service

Je, Seung-Mo

doi:10.1007/978-981-13-5907-1_15

Cited by 2 publications

(5 citation statements)

References 23 publications

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“…The percentage of their mobile web news reached up to 52.5% as of the third week of July 2014, and today, they ahold a dominant position as an Internet news provider. Although their news consists of unstructured data taking different structures and forms that are generated, modified, and stored daily, they still rely on people using these different information structures [56][57][58][59][60][61][62][63][64].…”

Section: Photovoltaic Power Plant Big Data Collection Through Web Crawlingmentioning

confidence: 99%

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Accurate Demand Forecasting: A Flexible and Balanced Electric Power Production Big Data Virtualization Based on Photovoltaic Power Plant

2021

Energies

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This paper has tried to execute accurate demand forecasting by utilizing big data visualization and proposes a flexible and balanced electric power production big data virtualization based on a photovoltaic power plant. First of all, this paper has tried to align electricity demand and supply as much as possible using big data. Second, by using big data to predict the supply of new renewable energy, an attempt was made to incorporate new and renewable energy into the current power supply system and to recommend an efficient energy distribution method. The first presented problem that had to be solved was the improvement in the accuracy of the existing electricity demand for forecasting models. This was explained through the relationship between the power demand and the number of specific words in the paper that use crawling by utilizing big data. The next problem arose because the current electricity production and supply system stores the amount of new renewable energy by changing the form of energy that is produced through ESS or that is pumped through water power generation without taking the amount of new renewable energy that is generated from sources such as thermal power, nuclear power, and hydropower into consideration. This occurs due to the difficulty of predicting power production using new renewable energy and the absence of a prediction system, which is a problem due to the inefficiency of changing energy types. Therefore, using game theory, the theoretical foundation of a power demand forecasting model based on big data-based renewable energy production forecasting was prepared.

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Section: Photovoltaic Power Plant Big Data Collection Through Web Crawlingmentioning

confidence: 99%

“…71,773 MW (74,996 MW), which corresponds to approx. 39% (35%), depending on the calculation method [57,65,66].…”

Section: Photovoltaic Power Plant Big Data Collection Through Web Crawlingmentioning

confidence: 99%

Accurate Demand Forecasting: A Flexible and Balanced Electric Power Production Big Data Virtualization Based on Photovoltaic Power Plant

2021

Energies

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“…If both of them cooperate with the prosecutor, they will each be meted out a 6-year sentence. [27][28][29] In contrast, if they both refuse the offer and deny their collaboration, each of them will be meted out a 1-year sentence instead. Having such information but without knowing the opponent's choice, what would be the right choice for them?…”

Section: Game Theory: Prisoner's Dilemmamentioning

confidence: 99%

“…The methods involving selection between two types of algorithms, problem solving in a mesh network construction process, and head counting in a smart grid environment are discussed in this study and a Python-based big data (power usages) analysis (1) is described in Figure 18. 27 CA5000-AP1 represents a Wi-Fi AP, and Groundtruth indicates the number of people in a given space. Since some meaningful correlations can be found between these two elements, it is possible to estimate the present number of people on each floor using them.…”

Section: Performance Evaluation Using Python Big Data Platformmentioning

confidence: 99%

“…August 20-22, Jeju, 2018. 27 Also, second draft of this paper was presented in at The 2018 International Conference on Future Information Technology, Applications and Services (IFIT 2018 Seoul), November 23, 2018. 28 This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No.2017R1C1B5077157).…”

Section: Acknowledgmentsmentioning

confidence: 99%

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Estimation of future power consumption level in smart grid: Application of fuzzy logic and genetic algorithm on big data platform

2019

Int J Communication

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Summary In an environment where the importance of new and renewable energies is growing, the balance between energy production and consumption cannot be achieved easily. Although it is possible for the one with surplus power to supply it to the one experiencing a shortage, power demand can hardly be expected to exceed supply or both to stay at equal levels at all times. In such case, a network limited to an individual or a small group can be regarded as a small single node so that the larger network consisting of these nodes can be represented with a graph based on the topology of dispersed nodes. This situation is similar to the prisoner's dilemma wherein the most ideal situation for the nodes is to collaborate with each other; in a situation wherein betrayal takes place, however, the Nash equilibrium can hardly be expected. Such situation between the nodes is almost the same condition repeatedly laid down to the prisoners who consistently and competitively pursue maximum profit and can be considered a game. Thus, this study attempted to devise a method of gaining maximum profit and predicting future power demands by using a genetic algorithm based on the game theory‐based fuzzy logic that seeks maximum profit by making the best choice. A scheme that can avoid a possible “prisoner's dilemma” situation in a new and renewable energy transaction environment was devised based on the game theory. For such scheme, the fuzzy theory was adopted to reflect the power demand, supply, and values; by developing a greedy algorithm, the optimal values were reflected under each given environment to set a foundation on which the situation wherein the cooperative nodes could be placed at a greater disadvantage than the uncooperative nodes can be avoided with a tit‐for‐tat algorithm wherein the genetic algorithm was reflected as well.

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An Optimization Theory of Home Occupants’ Access Data for Determining Smart Grid Service

Cited by 2 publications

References 23 publications

Accurate Demand Forecasting: A Flexible and Balanced Electric Power Production Big Data Virtualization Based on Photovoltaic Power Plant

Accurate Demand Forecasting: A Flexible and Balanced Electric Power Production Big Data Virtualization Based on Photovoltaic Power Plant

Estimation of future power consumption level in smart grid: Application of fuzzy logic and genetic algorithm on big data platform

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