Machine Learning for Optimising Renewable Energy and Grid Efficiency

• Published in: Atmosphere 2024-10, Vol.15 (10), p.1250
• Main Authors: Oladapo, Bankole I., Olawumi, Mattew A., Omigbodun, Francis T.
• Format: Article
• Language: English
• Subjects: Alternative energy sources; Artificial intelligence; Carbon dioxide; Carbon dioxide emissions; Climate change; CO2 emissions reduction; Comparative analysis; Efficiency; Emission measurements; Emissions; Emissions control; energy forecasting; Energy industry; Energy resources; Environmental aspects; Error analysis; Error reduction; Greenhouse gases; grid optimization; Hydroelectric power; Learning algorithms; Long short-term memory; Machine learning; Management; Net zero; Neural networks; Optimization; Paris Agreement; Renewable energy; Renewable energy sources; Root-mean-square errors; Support vector machines; Technology application; Wind power
• ISSN: 2073-4433; 2073-4433
• DOI: 10.3390/atmos15101250

This research investigates the application of machine learning models to optimise renewable energy systems and contribute to achieving Net Zero emissions targets. The primary objective is to evaluate how machine learning can improve energy forecasting, grid management, and storage optimisation, thereby enhancing the reliability and efficiency of renewable energy sources. The methodology involved the application of various machine learning models, including Long Short-Term Memory (LSTM), Random Forest, Support Vector Machines (SVMs), and ARIMA, to predict energy generation and demand patterns. These models were evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Key findings include a 15% improvement in grid efficiency after optimisation and a 10–20% increase in battery storage efficiency. Random Forest achieved the lowest MAE, reducing prediction error by approximately 8.5%. The study quantified CO2 emission reductions by energy source, with wind power accounting for a 15,000-ton annual reduction, followed by hydropower and solar reducing emissions by 10,000 and 7500 tons, respectively. The research concludes that machine learning can significantly enhance renewable energy system performance, with measurable reductions in errors and emissions. These improvements could help close the “ambition gap” by 20%, supporting global efforts to meet the 1.5 °C Paris Agreement targets.