A Review of Time Series forecasting Techniques: Predicting Solar energy using AI and Statistics Techniques
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A Review of Time Series forecasting Techniques: Predicting Solar energy using AI and Statistics Techniques
Ajay Kumar1, Sushil Kumar2
Research Scholar, JCDM College of Engineering, Sirsa, India1
Assistant Professor, JCDM College of Engineering, Sirsa, India2
Abstract
The overuse of fossil fuels and their detrimental effects on the environment must be considered in the current electrical energy crisis scenario. This promotes sustainable development by encouraging the use of renewable resources. The overall capacity of the energy systems was enhanced by adding such unpredictable renewable energy sources; however, the design of these hybrid systems is extremely important, which is why many academics have come to trust this area. Normally, in order to advance sustainable growth in the current electrical market, renewable resources like solar energy have been integrated into existing grid systems, but such systems have faced numerous challenges in terms of energy management. Energy management in solar-integrated electric grid systems is challenging because of a number of factors, such as temperature, wind speed, air pressure, and precipitation, that have erratic and volatile properties, and all such factors impact solar energy generation. Further, due to this, the power grid may become unstable if all of these unstable variables are disregarded, as voltage fluctuations may arise. In order to handle all of this, solar energy prediction is necessary so that the required actions can be taken based on the data available for renewable energy. Hence this paper reviewed the articles on statistical and AI-based solar irradiance forecasting in order to provide classifications for employees based on the situation.
Keywords: Solar Energy, Statistical and AI-based Solar Irradiance Forecasting.
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