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- W4283729449 abstract "Artificial intelligence methods have aided the advancement of several disciplines of science and technology. Furthermore, they have had a significant influence on smart grid management. One of the most significant information for optimal management in Smart Grid is the ability to predict its parameters: electricity consumption and meteorological factors. It is mostly utilized to develop improved control ways for building appliances (such as lighting and heating/cooling systems). Several methodologies for load and weather data characterization prediction have recently been proposed. The work discussed in this paper is aimed at the development and the comparison of three artificial intelligence forecasting approaches used to manage the Smart Grid by integrating load and climate data predictions. To achieve this objective, we primarily looked on the predicting accuracy of some artificial intelligence methodologies: a neural network, a neuro-fuzzy and a deep learning prediction algorithms are applied and compared to forecast the smart grid parameters (temperature, solar radiation, wind speed and the energy consumption). The simulation results are checked based on real database of wind speed, temperature, solar radiation, and consumption data. The findings of the simulation give us an idea about the most appropriate and performant algorithm to use in this aim." @default.
- W4283729449 created "2022-07-01" @default.
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- W4283729449 date "2022-05-17" @default.
- W4283729449 modified "2023-09-27" @default.
- W4283729449 title "A Comparative study of three AI prediction algorithms based on measured databases for an optimal Smart Grid" @default.
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- W4283729449 doi "https://doi.org/10.1109/codit55151.2022.9804155" @default.
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