Statistical Approaches to Forecasting Domestic Energy Consumption and Assessing Determinants: The Case of Nordic Countries

The residential sector accounts for large share of total annual energy use in the Nordic countries due to the extremely cold climates and high household heating demand. Most domestic energy consumption in the Nordic countries is for space heating and providing hot water. The purpose of our study was to forecast the annual energy consumption of the Nordic residential sectors by 2020 as a function of socio-economic and environmental factors, and to offer a framework for the predictors in each country.

Our research models the domestic energy use in Nordic countries based on social, economic and environmental factors. Applying the multiple linear regression (MLR), multivariate adaptive regression splines (MARS), and the artificial neural network (ANN) analysis methodologies, three models have been generated for each country in the Nordic region. Using these models, we forecasted the Nordic countries domestic energy use by 2020 and assessed the causal links between energy consumption and the investigated predictors. The results showed that the ANN models have a superior capability of forecasting the domestic energy use and specifying the importance of predictors compared to the regression models. The models revealed that changes in population, unemployment rate, work force, urban population, and the amount of CO₂ emissions from the residential sectors can cause significant variations in Nordic domestic sector energy use.