Saiful Islam
Amin Suaad
Michael Hartmann
Goran Rafajlovski
SRH University of Applied Sciences
Berliner Hochschule für Technik (BHT)
Ss. Cyril and Methodius University in Skopje
Germany
North Macedonia
e-mail: saiful.islam@srh.de
Abstract:
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.
Key words:
micro-grid
renewable energy
machine learning
cost reduction
excess energy reduction
Section:
The full text of the report is published in IJITS
and is available on the section “Archive” by URL https://ijits-bg.com/ijitsarchive
and is available on the section “Archive” by URL https://ijits-bg.com/ijitsarchive
Citation of this article:
Islam, S, Suaad, A., Hartmann, M., Rafajlovski, G. Analysing the electricity load and production by means of different machine learning methods: A case study of a MG system. International Journal on Information Technologies and Security, vol. 16, no. 3, 2024, pp. 101-110. DOI: https://doi.org/10.59035/LANO6489
Islam, S, Suaad, A., Hartmann, M., Rafajlovski, G. Analysing the electricity load and production by means of different machine learning methods: A case study of a MG system. International Journal on Information Technologies and Security, vol. 16, no. 3, 2024, pp. 101-110. DOI: https://doi.org/10.59035/LANO6489