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

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.

Assessing the impact of artificial intelligence and machine learning on forecasting medication demand and supply in public pharmaceutical systems: A systematic review

“…Failure to adequately address privacy concerns may result in the inadvertent disclosure of sensitive personal information, with potentially negative consequences for individuals in terms of their personal lives and employment prospects [35]. Moreover, the use of artificial intelligence (AI) models in the context of inventory projections necessitates institutions to securely conserve and analyze substantial volumes of personal data [36]. The possibility of security breaches or illegal access to highly confidential data has raised concerns [37].…”

Potential for the use of large unstructured data resources by public innovation support institutions

“…A similar problem of time delays is much wider and concerns many areas of life, and building forecasting models and improving data accuracy are a challenge that we deal with in many cases when demand forecasting is necessary [6].…”