AI-Driven Load Forecasting for Smart Grids under High Renewable Penetration
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V3I2P114Keywords:
Smart Grids, Load Forecasting, Artificial Intelligence, Machine Learning, Renewable Energy Integration, Deep Learning, Hybrid Models, Ensemble Learning, Uncertainty ModelingAbstract
The introduction of renewable energy sources into the contemporary power system also brings a lot of variability, uncertainty, and nonlinearity in electricity demand, which makes the conventional load forecasting techniques challenging. Artificial intelligence (AI) and machine learning (ML) have become promising methods to capture complex temporal and spatial relationship of load data, which provide better prediction capabilities of renewable-integrated smart grids. This paper critically evaluates AI-based load predictive designs, which comprise artificial neural networks, support networks, decision tree ensembles, and deep learning devices, e.g., recurrent neural networks, long short-term memory networks, and gated recurrent units and convolutional neural networks. Hybrid and ensemble models, which combine statistical models and signal decomposition methods, are also presented. The main variables affecting the forecasting performance, such as the meteorological, socio-economic, and grid-level variables are mentioned, as well as the data sources, feature engineering approaches, and metrics. Issues of data quality, generalization of models, scalability, and uncertainty are discussed and future research possibilities, such as probabilistic forecasting, real-time adaptive learning, and integrated load-renewable modeling are mentioned. This review will offer a unified source of information on how AI-based load forecasting practices can be developed and implemented within a renewable-abundant smart grid setting to researchers and practitioners.
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