Implication of machine learning techniques to forecast the electricity price and carbon emission: Evidence from a hot region

Abstract

The current study examines the significant determinants of electricity consumption and identifies an appropriate model to forecast the electricity price accurately. The main contribution is focused on eastern region of Saudi Arabia, a relatively hottest geographical area full of energy resources but with different electricity consumption patterns. The relative irrelevance of temperature as predicting factor of electricity consumption is quite surprising and contradicts the previous studies. In the eastern region, electricity price has negative association with electricity consumption. While comparing traditional and machine learning, it is found that machine learning techniques offer better predictability. Amongst the machine learning techniques, the support vector machine has the lowest errors in forecasting the electricity price. Additionally, the support vector machine approach is used to forecast the trend of carbon emissions caused by electricity consumption. The findings have policy implications and offer valuable suggestions to policymakers while addressing the determinants of electricity consumption and forecasting electricity prices.

Keywords

• Electricity consumption
• Carbon emission
• Artificial neural network
• Support vector machine
• Saudi Arabia