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Final Energy Consumption Forecasting by Applying Artificial Intelligence Models

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Operational Research in the Digital Era – ICT Challenges

Abstract

The application of artificial neural networks has been increased in many scientific sectors the last years, with the development of new machine learning techniques and methodologies. In this research, neural networks are applied in order to build and compare neural network forecasting models for predicting the final energy consumption. Predicting the energy consumption can be very significant in public management at improving the energy management and also at designing the optimal energy planning strategies. The final energy consumption covers the energy consumption in sectors such as industry, households, transport, commerce and public management. Several architectures were examined in order to construct the optimal neural network forecasting model. The results have shown a very good prediction accuracy according to the mean squared error. The proposed methodology can provide more accurate energy consumption predictions in public and environmental decision making, and they can be used in order to help the authorities at adopting proactive measures in energy management.

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References

  • Alcántara, V., & Padilla, E. (2003). “Key” sectors in final energy consumption: An input–output application to the Spanish case. Energy Policy, 31(15), 1673–1678.

    Article  Google Scholar 

  • Azadeh, A., Babazadeh, R., & Asadzadeh, S. (2013). Optimum estimation and forecasting of renewable energy consumption by artificial neural networks. Renewable and Sustainable Energy Reviews, 27, 605–612.

    Article  Google Scholar 

  • Cortès, U., Sànchez-Marrè, M., Ceccaroni, L., R-Roda, I., & Poch, M. (2000). Artificial intelligence and environmental decision support systems. Applied Intelligence, 13(1), 77–91.

    Article  Google Scholar 

  • Dunleavy, P., Margetts, H., Bastow, S., & Tinkler, J. (2006). New public management is dead—long live digital-era governance. Journal of Public Administration Research and Theory, 16(3), 467–494.

    Article  Google Scholar 

  • Efendigil, T., Önüt, S., & Kahraman, C. (2009). A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis. Expert Systems with Applications, 36(3), 6697–6707.

    Article  Google Scholar 

  • Ekonomou, L. (2010). Greek long-term energy consumption prediction using artificial neural networks. Energy, 35(2), 512–517.

    Article  Google Scholar 

  • Khoshnevisan, B., Rafiee, S., Omid, M., Yousefi, M., & Movahedi, M. (2013). Modeling of energy consumption and GHG (greenhouse gas) emissions in wheat production in Esfahan province of Iran using artificial neural networks. Energy, 52, 333–338.

    Article  Google Scholar 

  • Khosravani, H. R., Castilla, M. D. M., Berenguel, M., Ruano, A. E., & Ferreira, P. M. (2016). A comparison of energy consumption prediction models based on neural networks of a bioclimatic building. Energies, 9(1), 57.

    Article  Google Scholar 

  • Koskela, T., Lehtokangas, M., Saarinen, J., & Kaski, K. (1996). Time series prediction with multilayer perceptron, FIR and Elman neural networks. In: Proceedings of the World Congress on Neural Networks, 1996 (pp. 491–496). Citeseer.

    Google Scholar 

  • Kouziokas, G. N. (2016a). Artificial intelligence and crime prediction in public management of transportation safety in urban environment. In: Proceedings of the 3rd Conference on Sustainable Urban Mobility, Volos, Greece, 2016 (pp. 534–539). University of Thessaly.

    Google Scholar 

  • Kouziokas, G. N. (2016b). Geospatial based information system development in public administration for sustainable development and planning in urban environment. European Journal of Sustainable Development, 5(4), 347–352. https://doi.org/10.14207/ejsd.2016.v5n4p347.

    Article  Google Scholar 

  • Kouziokas, G. N. (2016c). Technology-based management of environmental organizations using an Environmental Management Information System (EMIS): Design and development. Environmental Technology & Innovation, 5, 106–116. https://doi.org/10.1016/j.eti.2016.01.006.

    Article  Google Scholar 

  • Kouziokas, G. N. (2017a). An information system for judicial and public administration using artificial intelligence and geospatial data. In: Proceedings of the 21st Pan-Hellenic Conference on Informatics, Larissa, Greece (pp. 1–2). ACM, 3139402. doi:https://doi.org/10.1145/3139367.3139402

  • Kouziokas, G. N. (2017b). Machine learning technique in time series prediction of gross domestic product. In: Proceedings of the 21st Pan-Hellenic Conference on Informatics, Larissa, Greece (pp. 1–2). ACM, 3139443. doi:https://doi.org/10.1145/3139367.3139443

  • Kouziokas, G. N. (2017c). The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia, 24, 467–473. https://doi.org/10.1016/j.trpro.2017.05.083.

    Article  Google Scholar 

  • Kouziokas, G. N., Chatzigeorgiou, A., & Perakis, K. (2016). Predicting environmental data in public management by using artificial intelligence. In: Proceedings of the 11th International Scientific Conference eRA-11, Piraeus, Greece, September 2016 (pp. 39–46). Piraeus University of Applied Sciences.

    Google Scholar 

  • Kouziokas, G. N., Chatzigeorgiou, A., & Perakis, K. (2017). Artificial intelligence and regression in predicting ground water levels in public administration. European Water, (57), 361–366.

    Google Scholar 

  • Kouziokas, G. N., & Perakis, K. (2017). Decision support system based on artificial intelligence, GIS and remote sensing for sustainable public and judicial management. European Journal of Sustainable Development, 6(3), 397–404. https://doi.org/10.14207/ejsd.2017.v6n3p397.

    Article  Google Scholar 

  • Lourakis, M. I. A. (2005). A brief description of the Levenberg-Marquardt algorithm implemented by levmar. Foundation of Research and Technology, 4, 1–6.

    Google Scholar 

  • Marquardt, D. W. (1963). An algorithm for least-squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics, 11(2), 431–441.

    Article  Google Scholar 

  • Ramanathan, R. (2006). A multi-factor efficiency perspective to the relationships among world GDP, energy consumption and carbon dioxide emissions. Technological Forecasting and Social Change, 73(5), 483–494.

    Article  Google Scholar 

  • Svozil, D., Kvasnicka, V., & Pospichal, J. (1997). Introduction to multi-layer feed-forward neural networks. Chemometrics and Intelligent Laboratory Systems, 39(1), 43–62.

    Article  Google Scholar 

  • York, R. (2007). Demographic trends and energy consumption in European Union Nations, 1960–2025. Social Science Research, 36(3), 855–872.

    Article  Google Scholar 

  • Zeng, Y.-R., Zeng, Y., Choi, B., & Wang, L. (2017). Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network. Energy, 127, 381–396. https://doi.org/10.1016/j.energy.2017.03.094.

    Article  Google Scholar 

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Acknowledgments

The UK Office for National Statistics and the UK Department for Business, Energy and Industrial Strategy websites for retrieving the data.

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Correspondence to Georgios N. Kouziokas .

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Kouziokas, G.N., Chatzigeorgiou, A., Perakis, K. (2019). Final Energy Consumption Forecasting by Applying Artificial Intelligence Models. In: Sifaleras, A., Petridis, K. (eds) Operational Research in the Digital Era – ICT Challenges. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-95666-4_1

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