Abstract: This article presents a review of current advances and future prospects in the field of fore- casting renewable energy generation using machine learning (ML techniques. With the increasing penetration of renewable energy sources (RES) into the electricity grid, accurate forecasting of their generation becomes crucial for efficient grid operation and energy man- agreement. Traditional forecasting methods have limitations, and thus ML. This paper reviews the different approaches and models that have been used for re- new able energy forecasting and discusses their strengths and limitations. It also highlights the challenges and future research directions in the field, such as dealing with uncertainty and variability in renewable energy generation, data availability, and model interpretability.
Improved irradiance forecasting ensures precise solar power generation forecasts, resulting in smoother operation of the distribution grid. Empirical models are used to estimate irradiation using a wide range of data and specific national or regional parameters. In contrast, algorithms based on Artificial Intelligence (AI) are becoming increasingly popular and effective for estimating solar irradiance. Although there has been significant development in this area elsewhere, employing an AI model to investigate irradiance in Bangladesh is limited. This research forecasts solar radiation in Bangladesh using ensemble machine-learning models. The meteorological data collected from 32 stations contain maximum temperature, minimum temperature, total rain, humidity, sunshine, wind speed, cloud coverage, and irradiance.
Finally, this paper emphasizes the importance of developing robust and accurate renewable energy forecasting models to enable the integration of RES into the electricity grid and facilitate the transition towards a sustainable energy future.
Keywords: Accurate predictions; Energy management; Machine Learning; Renew- able Energy Forecasting, solar irradiance; machine-learning; ensemble models; performance matrices; prediction error.
Cite:
Mr.Meghraj Chougule, Solwat Kimyanand Bharat, "Forecasting Renewable Energy Generation with Machine learning: Latest Advances and Future Possibility", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 12, pp.31-39, 2023, Crossref https://doi.org/10.17148/IJARCCE.2023.121205.
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DOI:
10.17148/IJARCCE.2023.121205
