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On statistical modeling and forecasting of energy usage in smart grid

Conference Paper


Abstract


  • Developing effective energy resource management strategies in the smart grid is challenging because the entities in both demand and supply sides experience numerous fluctuations. In this paper, we address the issue of quantifying uncertainties on the energy demand side. Specifically, we first develop approaches using statistical modeling analysis to derive a statistical distribution of energy usage. We then utilize machine learning based approaches such as the Support Vector Machines (SVM) and neural networks to conduct accurate forecasting on energy usage. We perform extensive experiments of our proposed approaches using a real-world meter reading data set. Our experimental data shows that the statistical distribution of meter reading data can be largely approximated with a Gaussian distribution and the two SVM-based machine learning approaches achieve a high accuracy of forecasting energy usage.

Publication Date


  • 2014

Citation


  • Yu, W., An, D., Griffith, D., Yang, Q., & Xu, G. (2014). On statistical modeling and forecasting of energy usage in smart grid. In Proceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014 (pp. 12-17). doi:10.1145/2663761.2663768

Scopus Eid


  • 2-s2.0-84910017088

Web Of Science Accession Number


Start Page


  • 12

End Page


  • 17

Abstract


  • Developing effective energy resource management strategies in the smart grid is challenging because the entities in both demand and supply sides experience numerous fluctuations. In this paper, we address the issue of quantifying uncertainties on the energy demand side. Specifically, we first develop approaches using statistical modeling analysis to derive a statistical distribution of energy usage. We then utilize machine learning based approaches such as the Support Vector Machines (SVM) and neural networks to conduct accurate forecasting on energy usage. We perform extensive experiments of our proposed approaches using a real-world meter reading data set. Our experimental data shows that the statistical distribution of meter reading data can be largely approximated with a Gaussian distribution and the two SVM-based machine learning approaches achieve a high accuracy of forecasting energy usage.

Publication Date


  • 2014

Citation


  • Yu, W., An, D., Griffith, D., Yang, Q., & Xu, G. (2014). On statistical modeling and forecasting of energy usage in smart grid. In Proceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014 (pp. 12-17). doi:10.1145/2663761.2663768

Scopus Eid


  • 2-s2.0-84910017088

Web Of Science Accession Number


Start Page


  • 12

End Page


  • 17