Abstract
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.
Highlights
In recent years, energy production systems based on photovoltaic power generation have pursued a more efficient structure to provide better reliability and enhanced energy efficiency
This paper suggests a direction to solve the problem of connection between the existing power grid and the new renewable energy grid, which can be said to be an inefficient part of the current power generation/supply system, by improving the prediction accuracy of new renewable energy production and by proposing a method to improve the accuracy of electricity demand forecasting using big data
Forecasting the power demand/production in a solar power plant is similar to performing a calculation with a function that can be affected by numerous inputs, so even though using big data may help to improve the accuracy, it is not easy to find an optimal virtualization solution just by identifying a number of correlations
Summary
Energy production systems based on photovoltaic power generation have pursued a more efficient structure to provide better reliability and enhanced energy efficiency. The currently available alternatives include existing thermal power generation that uses energy sources that are expected to deplete in the near future or new and renewable energy-based power generation, which still offers quite a low level of efficiency but that is able to provide power indefinitely [2,3,4] Another method that could increase the total amount of energy is to reduce the use of energy itself, which can be accomplished in many ways: either by suspending the supply, such as in the case of what North Korea often does, encouraging people to use types of anachronistic lighting sources such as candles, or persuading citizens to save energy according to their consciences by emphasizing the necessity of saving energy. It will be possible to minimize the amount of reserve power produced for the safety of the power supply, to reduce unnecessary energy waste, and to minimize the production losses of new renewable energy that occurs through a storage process such as ESS
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