Forecasting Monthly Electricity Consumption for Energy Planning and Policy Development in Sri Lanka

Abstract

For effective energy planning, grid stability, and policy development especially in emerging nations like Sri Lanka accurate electricity consumption projections is essential. The goal of this project is to create a reliable model that can forecast the Ceylon Electricity Board's (CEB) monthly electricity consumption using a large dataset that includes macroeconomic variables, market indicators, peak demand, energy generation sources, and weather data. Autoregressive Distributed Lag (ARDL), Random Forest, and eXtreme Gradient Boosting (XGBoost) are the three models whose redicting performance is compared in this study. The most pertinent predictors were chosen using Recursive Feature Elimination with Cross-Validation (RFECV). Although XGBoost performed well throughout training, overfitting was a problem. ARDL was interpretable, however it was unable to detect longterm cointegration and could not represent non-linear connections. With the best accuracy and dependability on the test dataset without overfitting, Random Forest turned out to be the best model whereas Monthly Sales by Tariff in LKR, Fuel Cost by Power Stations in LKR, Electricity Generation from Thermal Coal in CEB (Gwh), Electricity Generation from Mannar Wind in CEB (Gwh), Day Peak Demand (MW), Night Peak Demand (MW), Average Monthly Rainfall (mm), and Gross Domestic Product (GDP) in LKR were the eight most important factors that were found to be involved in forecasting electricity consumption. On the test dataset, Random Forest, the best model chosen, had an accuracy of 77.34%, a Mean Absolute Percentage Error (MAPE) of 22.67%, a Root Mean Square Error (RMSE) of 12.62, and a Mean Absolute Error (MAE) of 11.11. However, the models might not be able to reflect long-term structural changes like the switch to electric vehicles or widespread adoption of renewable energy sources, and the study did not account for new elements like government policy reforms or energy efficiency initiatives. Nevertheless, the results show that machine learning, in particular Random Forest, can improve Sri Lankan electricity consumption predictions to aid in sustainable energy planning and policy choices.