Abstract
Traditional energy sources are vital for energizing residences and industries. Nonetheless, they meet considerable obstacles, such as deteriorating infrastructure, ecological decline, resource exhaustion, price fluctuations, and a disparity between real-time demand and supply. Conversely, renewable energy sources offer cleaner and more sustainable energy sources. Renewable energy mitigates the pollution caused by carbon and greenhouse gas emissions in traditional power systems. Incorporating renewable energy into a power system needs an efficient management system to keep stability and improve power use. Modern energy management systems (EMS) face a significant challenge in controlling renewable and non-renewable energy sources. The challenges encompass intermittent and variable renewable energy, insufficient energy storage solutions, difficulty in integrating renewable sources into antiquated grid infrastructure, initial expenses, and regulatory impediments. This work is innovative in its synthesis of recent accomplishments and its identification of gaps in prior studies. The evaluation emphasizes discoveries and obstacles, accompanied by recommendations for improving EMS using machine learning (ML). This synthesis offers guidance to inform the development of the next generation of EMS. Adaptability in hybrid energy systems management is, therefore, essential for its successful transition to renewable energy, thus improving the overall energy system’s real-time resilience and sustainability.
Recommended Citation
Al-Fatllah, Mays Sattar; Ali, Zaipatimah; and Abidin, Aidil Azwin Zainul
(2025)
"A Review of Machine Learning Methods in Energy Management System,"
Almaaqal Journal of Sustainability and Emerging Technology: Vol. 1:
Iss.
1, Article 5.
Available at:
https://ajset.almaaqal.edu.iq/journal/vol1/iss1/5