Analysing the electricity load and production by means of different machine learning methods: A case study of a MG system

Renewable energy is a promising solution to combat the scarcity of electricity, particularly in isolated and rural areas. Microgrids (MG) can be employed for installing systems with different energy sources, such as renewable energy components and conventional energy sources like utility grids or grid-connected inverter systems. The amount of energy produced by renewable sources depends on their location, which has implications for energy production. This research aims to explore MG and their challenges for efficient operation. The study discusses various AI models used by researchers to mitigate problems associated with MG planning. Additionally, the paper presents a case study based on the most beneficial ML tool like clustering to gain insights into an existing MG system. The paper also delves into the issues related to PV, a connected distributed energy resource (DER), such as forecasting, and predictive management to reduce maintenance costs, and how AI tools can address them. Furthermore, forecasting methods such as LSTM and GRU models are discussed because of the stochastic nature of PV production.