A recent study has introduced a new machine learning model aimed at improving the accuracy of renewable energy forecasting. Published in March 2021 under the title An Efficient Supervised Machine Learning Model Approach for Forecasting of Renewable Energy to Tackle Climate Change, the research presents a model that achieves a Symmetric Mean Absolute Percentage Error (SMAPE) as low as 1-2%.
“Data science plays a crucial role in building a more sustainable and economically viable future,” Drumil Joshi, key researcher involved in the study, said. “This model aims to enable governments and corporations to make data-driven decisions that optimise energy consumption and investment. The research is among the emerging innovations in the field and has the potential to create a global impact,” he said.
Using advanced techniques such as Random Forest and Extra Trees regression, the model aims to improve energy scheduling while also enabling cost savings. With the global renewable energy market projected to exceed $1.5 trillion by 2025, innovations like this could help minimise energy wastage by 15-20%, translating into substantial economic benefits. Improved efficiency in forecasting could also open up new investment opportunities in the renewable sector.
The study emphasises the role of collaborative efforts in technological advancements. “Bringing together expertise in machine learning and renewable energy forecasting has helped create a tool that is both technically sound and financially viable,” Joshi explained. “The findings highlight the potential for sustainability and economic development to go hand in hand,” he added.
The lead researcher,Drumil Joshi, a recent graduate in Electronics and Telecommunication, has contributed to multiple international research papers and is furthering studies in Data Science. “Ongoing learning and research are essential to advancing technology and addressing key global challenges,” he remarked.
By integrating advanced computational methods with economic considerations, this research contributes to the broader efforts to enhance renewable energy forecasting. As the world continues transitioning toward sustainable energy solutions, innovations in predictive analytics may play a critical role in achieving both environmental and economic objectives.
