Forecasting Electricity Consumption In Kyrgyzstan Using Machine Learning

Abstract

The goal of this research is to analyze model of electric power consumption of Kyrgyzstan and to build a data-driven model to predict the consumption for the future. Using historical electricity consumption data from World Bank, in the present study, modern machine learning approaches are used in order to provide accurate and interpretable predictions. The primary aim is to help policy makers and energy planners to identify models which are able to capture underlying consumption drivers. These results validate the use of machine learning models over traditional statistical models for better predictive performance and importance in national energy planning. This thesis adds to the emerging literature on energy forecasting in developing countries by offering a case study based on local data and policy.

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