Grid Insight A Prediction Model of Energy Insights for a Sustainable Sri Lanka

Abstract

"ri Lanka is currently facing an acute challenge in meeting its rapidly growing electricity demand amid an economic crisis and constrained capacity for energy import and export. The conventional methods for forecasting energy supply and demand, as well as for identifying and evaluating potential energy sources, fall short in addressing the complex, multi-dimensional energy landscape of Sri Lanka. This project aims to develop a predictive model that offers a more accurate, efficient, and sustainable solution for energy management in Sri Lanka, thereby addressing the country's urgent need for reliable and cost-effective energy solutions.To tackle this problem, a Python-based predictive model was developed using advanced computational techniques, including machine learning algorithms. The project involved comprehensive data pre-processing to ensure highquality inputs, followed by the selection and tuning of appropriate machine learning models such as regression analysis and ensemble methods. The model was designed to integrate various economic, environmental, and consumption variables to forecast future electricity demand and identify the most efficient and cost-effective energy sources for generation. This approach also incorporated renewable energy potentials, aiming to provide a holistic view of energy management for the country. The developed model was evaluated using historical data and various data science metrics, such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared values, to assess its accuracy and reliability. The results demonstrated a high level of predictive accuracy, with the model successfully forecasting future energy demands and highlighting the optimal mix of renewable and conventional energy sources. These findings offer actionable insights for policymakers and contribute significantly to the discourse on sustainable energy management in Sri Lanka"