  This paper presents a comprehensive survey of AI-driven mini-grid solutions
aimed at enhancing sustainable energy access. It emphasises the potential of
mini-grids, which can operate independently or in conjunction with national
power grids, to provide reliable and affordable electricity to remote
communities. Given the inherent unpredictability of renewable energy sources
such as solar and wind, the necessity for accurate energy forecasting and
management is discussed, highlighting the role of advanced AI techniques in
forecasting energy supply and demand, optimising grid operations, and ensuring
sustainable energy distribution. This paper reviews various forecasting models,
including statistical methods, machine learning algorithms, and hybrid
approaches, evaluating their effectiveness for both short-term and long-term
predictions. Additionally, it explores public datasets and tools such as
Prophet, NeuralProphet, and N-BEATS for model implementation and validation.
The survey concludes with recommendations for future research, addressing
challenges in model adaptation and optimisation for real-world applications.
