AI-Driven Forecasting Models for Solar and Wind Power Generation in Smart Grids

Abstract

The integration of distributed renewable energy sources into smart grids poses complex challenges for the stability, reliability, and economic operation of the grid. Accurate forecasting of the generation of these energy sources is essential for effective energy management, dispatching, and market operations. In this paper, a detailed review and conceptual framework of AI-driven forecasting models for solar and wind power generation in the context of smart grids is presented. We study various artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) algorithms, and discuss their features and applications in short-term, medium-term, and long-term forecasting. Key issues such as data acquisition, preprocessing, feature engineering, model selection, and performance evaluation are discussed in depth. The impact of meteorological data, sensor networks and advanced data analytics on forecasting accuracy is also discussed in this paper. In addition, the problems associated with variability and uncertainty in renewable energy generation are also studied, and hybrid AI models and uncertainty quantification methods are suggested as solutions. This paper provides a comprehensive overview of the state-of-the-art development of AI-based renewable energy forecasting and suggests future research directions, so that easy and efficient inclusion of renewable energy in smart grids will be possible in the future.