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.
Energy Policies of IEA Countries - Denmark (2011). Review. https://www.iea.org.
Energy Policies of IEA Countries - Finland (2013). Review. https://www.iea.org.
Energy Policies of IEA Countries - Norway (2011). Review. https://www.iea.org.
Energy Policies of IEA Countries - Sweden (2013). Review. https://www.iea.org.
Karlsson, K., Münster, M., Skytte, K., Pérez, C., Venturini, G. and Salvucci, R.
(2016). Nordic energy technology perspectives.
International Energy Agency (2014). Statistics IE. Balances, Denmark. https://
Railio, J. (2005). Energy Performance of Buildings Directive. Influences on European
standardization and on ventilation and air-conditioning industry, update
and follow-up. Page 3.
Kitzing, L., Katz, J., Schröder, S., Morthorst, P. and Andersen, F. The residential
electricity sector in Denmark: a description of current conditions. Working paper,
Technical Univrsity of Denmark, Kgs. Lyngby. http://orbit.dtu.
The Danish Energy Agency. Energy efficiency trends and policies in Denmark.
Barriers for flexibility in the district heating-electricity interface (2016). www.lsta.
Whitehead, F. (2014). Lessons from Denmark: how district heating could improve
energy security. The guardian.
International Energy Agency (2014). Statistics IE. Balances, Finland. https://www.
Paiho, S. and Reda, F. (2016). Towards next generation district heating in Finland.
Renewable and Sustainable Energy Reviews, 65, pages 915-24.
Znouda, E., Ghrab-Morcos, N. and Hadj-Alouane A. (2007). Optimization of Mediterranean
building design using genetic algorithms. Energy and Buildings, 39(2),
International Energy Agency (2014). Statistics IE. Balances. https://www.iea.org.
National Energy Authority of Iceland (2016). Geothermal. http://www.nea.is/
Mäntysaari, P. (2015). E.U. electricity trade law: the legal tools of electricity producers
in the internal electricity market. Springer.
Pool, N. (2007). Annual Report. www. nordpoolspot. com/about.
International Energy Agency (2014). Statistics IE. Balances. Norway. https://
Gebremedhin, A. (2012). Introducing district heating in a Norwegian town-potential
for reduced local and global emissions. Applied Energy, 95, pages 300-4.
Statistics Norway (2012). Energy consumption in households. https://www.ssb.
Eva Rosenberg Institute for Energy Technology (2015). Energy efficiency trends
and policies in Norway. www.odyssee-mure.eu.
International Energy Agency (2014). Statistics IE. Balances. Sweden. https://
International Energy Agency (2016). The IEA CHP and DHC Collaborative CHP/
DHC Scorecard: Sweden. http://www.iea.org.
Swan, L. and Ugursal, VI. (2009). Modeling of end-use energy consumption in the
residential sector: a review of modeling techniques. Renewable and Sustainable Energy
Reviews, 13(8), pages 1,819-35.
Friedman, J. (1991). Multivariate adaptive regression splines. The annals of statistics.
Lomas, K. (2010). Carbon reduction in existing buildings: a transdisciplinary approach.
Taylor and Francis.
Oreszczyn, T. and Lowe, R. (2010). Challenges for energy and buildings research:
objectives, methods and funding mechanisms. Building Research and Information,
(1), pages 107-22.
Jones, R., Fuertes, A. and Lomas, K. (2015). The socio-economic, dwelling and
appliance related factors affecting electricity consumption in domestic buildings.
Renewable and Sustainable Energy Reviews, 43, pages 901-17.
Bedir, M., Hasselaar, E. and Itard, L. (2013). Determinants of electricity consumption
in Dutch dwellings. Energy and Buildings, 58, pages 94-207.
Tso, G. and Yau, K. (2007). Predicting electricity energy consumption: a comparison
of regression analysis, decision tree and neural networks. Energy, 32(9), pages
Wiesmann, D., Azevedo, I., Ferrão, P. and Fernández, J. (2011). Residential electricity
consumption in Portugal: findings from top-down and bottom-up models.
Energy Policy, 39(5), pages 2,772-9.
Druckman, A. and Jackson, T. (2008). Household energy consumption in the UK: a
highly geographically and socio-economically disaggregated model. Energy Policy,
(8), pages 3,177-92.
Kavousian, A., Rajagopal, R. and Fischer, M. (2013). Determinants of residential
electricity consumption: using smart meter data to examine the effect of climate,
building characteristics, appliance stock, and occupants’ behavior. Energy, 55,
Brounen, D., Kok, N. and Quigley, J. (2012). Residential energy use and conservation:
economics and demographics. European Economic Review, 56(5), pages 31-45.
Leahy, E. and Lyons, S. (2010). Energy use and appliance ownership in Ireland.
Energy Policy, 38(8), pages 4,265-79.
Tso, G. and Yau, K. (2003). A study of domestic energy usage patterns in Hong
Kong. Energy, 28(15), pages 1,671-82.
Zhou, S. and Teng, F. (2013). Estimation of urban residential electricity demand in
China using household survey data. Energy Policy, 61, pages 394-402.
The Nordic Council of Ministers. The population Nordic cooperation. http://
O’neill, B. and Chen, S. (2002). Demographic determinants of household energy
use in the United States. Population and Development Review, 28, pages 53-88.
Liddle, B. (2004). Demographic dynamics and per capita environmental impact:
using panel regressions and household decompositions to examine population
and transport. Population and Environment, 26(1), pages 23-39.
Prskawetz, A., Leiwen, J. and O’Neill, B. (2004). Demographic composition and
projections of car use in Austria. Vienna Yearbook of Population Research, pages 175-
Liddle, B. (2004). Impact of population, age structure, and urbanization on carbon
emissions/energy consumption: evidence from macro-level, cross-country analyses.
Population and Environment, 35(3), pages 286-304.
Jorgenson, A., Rice, J. and Clark, B. (2010). Cities, slums, and energy consumption
in less developed countries, 1990 to 2005. Organization and Environment, 23(2),
York, R. (2007). Demographic trends and energy consumption in European Union
Nations, 1960-2025. Social Science Research, 36(3), pages 855-72.
York, R. editor (2007). Structural influences on energy production in south and
east Asia, 1971-2002. Sociological Forum: Wiley Online Library.
Okada, A. (2012). Is an increased elderly population related to decreased CO2
emissions from road transportation? Energy Policy, 45, pages 286-92.
Menz, T. and Welsch, H. (2012). Population aging and carbon emissions in OECD
countries: accounting for life-cycle and cohort effects. Energy Economics, 34(3),
Martínez-Zarzoso, I. and Maruotti, A. (2011). The impact of urbanization on CO2
emissions: evidence from developing countries. Ecological Economics, 70(7), pages
Liddle, B. and Lung, S. (2010). Age-structure, urbanization, and climate change in
developed countries: revisiting STIRPAT for disaggregated population and consumption-
related environmental impacts. Population and Environment, 31(5), pages
York, R. (2008). De-carbonization in former Soviet republics, 1992-2000: the ecological
consequences of de-modernization. Social Problems, 55(3), pages 70-90.
Blázquez, L., Boogen, N. and Filippini, M. (2013). Residential electricity demand
in Spain: new empirical evidence using aggregate data. Energy Economics, 36,
Halvorsen, B. and Larsen, B. (2001). Norwegian residential electricity demand—a
microeconomic assessment of the growth from 1976 to 1993. Energy Policy, 29(3),
Fan, J., Zhang, Y. and Wang, B. (2016). The impact of urbanization on residential
energy consumption in China: an aggregated and disaggregated analysis. Renewable
and Sustainable Energy Reviews.
Sun, C., Ouyang, X., Cai, H., Luo, Z. and Li, A. (2014). Household pathway selection
of energy consumption during urbanization process in China. Energy Conversion
and Management, 84, pages 295-304.
Ali, H., Law, S. and Zannah, T. (2016). Dynamic impact of urbanization, economic
growth, energy consumption, and trade openness on CO2 emissions in Nigeria.
Environmental Science and Pollution Research, 23(12), pages 12,435-43.
Yuan, B., Ren, S. and Chen, X. (2015). The effects of urbanization, consumption
ratio and consumption structure on residential indirect CO2 emissions in China: a
regional comparative analysis. Applied Energy, 140, pages 94-106.
Wang, Q., Zeng, Y. and Wu, B. (2016). Exploring the relationship between urbanization,
energy consumption, and CO2 emissions in different provinces of China.
Renewable and Sustainable Energy Reviews, 54, pages 1,563-79.
Ru, M., Tao, S., Smith, K., Shen, G., Shen, H. and Huang, Y. (2015). Direct energy
consumption associated emissions by rural-to-urban migrants in Beijing. Environmental
Science and Technology, 49(22), pages 13,708-15.
Aixiang, T. (2011). Research on relationship between energy consumption quality
and education, science and technology based on grey relation theory. Energy Procedia,
The Nordic Council of Ministers. Total research and development expenditure
Nordic cooperation. http://www.norden.org.
The Nordic Council of Ministers. Educational attainment at upper- and postsecondary
level Nordic cooperation. http://www.norden.org.
Yohanis, Y., Mondol, J., Wright, A. and Norton, B. (2008). Real-life energy use in
the UK: how occupancy and dwelling characteristics affect domestic electricity
use. Energy and Buildings, 40(6), pages 1,053-9.
Cramer, J., Miller, N., Craig, P., Hacket, B., Dietz, T. and Vine, E. (1985). Social and
engineering determinant and their equity implications in residential electricity
use. Energy, 10(12), pages 1,283-91.
Frederiks, E., Stenner, K. and Hobman, E. (2015). The socio-demographic and psychological
predictors of residential energy consumption: a comprehensive review.
Energies, 8(1), pages 573-609.
The Nordic Council of Ministers. Denmark—per cent employed of the population
between 15-64 Nordic cooperation. http://www.norden.org.
Feng, Y., Chen, S. and Zhang, L. (2013). System dynamics modeling for urban
energy consumption and CO2 emissions: a case study of Beijing, China. Ecological
Modelling, 252, pages 44-52.
Hossain, M., Li, B., Chakraborty, S., Hossain, M. and Rahman, M. (2015). A comparative
analysis on China’s energy issues and CO2 emissions in global perspectives.
Sustainable Energy, 3(1), pages 1-8.
Fumo, N. and Biswas, M. (2015). Regression analysis for prediction of residential
energy consumption. Renewable and Sustainable Energy Reviews, 47, pages 332-43.
Aiken, L., West, S. and Pitts, S. (2003). Multiple linear regression. Handbook of
Wang, S. (2003). Artificial neural network. Interdisciplinary computing in java
programming. Springer, pages 81-100.
Bertolini, M., Bevilacqua, M. and Ciarapica, F., editors (2010). Re-engineering the
forecasting phase using traditional and soft computing methods. Industrial Engineering
and Engineering Management. 2010 IEEE International Conference. IEEE.
Holt-Winters seasonal method. https://www.otexts.org.
Williams, K. and Gomez, J. (2016). Predicting future monthly residential energy
consumption using building characteristics and climate data: a statistical learning approach. Energy and Buildings, 128, pages 1-11.
Fridedman, J. (1991). Multivariate adaptive regression splines (with discussion).
Annual Statistics, 19(1), pages 79-141.
Jung, S. and Lee, S. (2006). In situ monitoring of cell concentration in a photobioreactor
using image analysis: comparison of uniform light distribution model and
artificial neural networks. Biotechnology Progress, 22(5), pages 1,443-50.
Garson, D. (1991). Interpreting neural network connection weights.
Montano, J., and Palmer, A. (2003). Numeric sensitivity analysis applied to feedforward
neural networks. Neural Computing and Applications, 12(2), pages 119-25.
Bartiaux, F. and Gram-Hanssen, K., editors (2005). Socio-political factors influencing
household electricity consumption: a comparison between Denmark and Belgium.
ECEEE summer study proceedings.
Wyatt, P. (2013). A dwelling-level investigation into the physical and socioeconomic
drivers of domestic energy consumption in England. Energy Policy, 60,