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.
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.
Every year, diverse types of safety accidents cause major damage to human life and property. In particular, failure to suppress safety accidents caused by fires during the early stages can lead to large-scale accidents, which in turn can cause more serious damage than other types of accident. Therefore, this paper presents an analysis of the prevailing research trends and future directions for research on preventing safety accidents due to fire. Since fire outbreaks can occur in many types of places, the study was conducted by selecting the places and causes involved in frequent fires, using fire data from Korea. As half of these fires were found to occur in buildings, this paper presents an analysis of the causes of building fires, and then focuses on three themes: fire prevention based on fire and gas detection; fire prevention in electrical appliances; and fire prevention for next-generation electricity. In the gas detection of the first theme, the gas referred to does not denote a specific gas, but rather to the gas used in each place. After an analysis of research trends for each issue related to fire prevention, future research directions are suggested on the basis of the findings. It is necessary to evaluate the risk, select a detection system, and improve its reliability in order to thoroughly prevent fires in the future. In addition, an active emergency response system should be developed by operating a fire prevention control system, and safety training should be developed after classifying the targets of the training targets appropriately.
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