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Prediction of Personal Power Consumption Using the Moving Average Technique

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 78))

Abstract

In this paper we introduce a new prediction method of personal power consumption using the moving average technique. Unlike typical methods, our method considers the trend of consumer’s statistical power consumption changes for estimating the statistical future power consumption. In the simulation section, we verify that the performance of our method is better than that of typical method.

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References

  1. Bergey, P.K., Hoskote, M.: A decision support system for the electrical power districting problem. Decision Support Systems 36(1) (September 2003)

    Google Scholar 

  2. Alves da Silva, A.P., Ferreira, V.H., Velasquez, R.M.G.: Input space to neural network based load forecasting. International Journal of Forecasting 24, 616–629 (2008)

    Article  Google Scholar 

  3. Charytoniuk, W., Chen, M.S.: Very short-term load forecasting using artificial neural networks. IEEE Transactions on Power Systems 15, 263–268 (2000)

    Article  Google Scholar 

  4. Darbellay, G.A., Slama, M.: Forecasting the short-term demand for electricity – Do neural networks stand a better chance? International Journal of Forecasting 16, 71–83 (2000)

    Article  Google Scholar 

  5. Cottet, R., Smith, M.: Bayesian modeling and forecasting of intraday electricity load. Journal of the American Statistical Association 98, 839–849 (2003)

    Article  MathSciNet  Google Scholar 

  6. Zakeri, I., Adolph, A.L., Puyau, M.R., Vohra, F.A., Butte, N.F.: Application of cross-sectional time series modeling for the prediction of energy expenditure from heart rate and accelerometry. Journal of Applied Physiology 104, 1665–1673 (2008)

    Article  Google Scholar 

  7. Akaike, H.: A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716–723 (1974)

    Article  MathSciNet  MATH  Google Scholar 

  8. Buntine, W.: A guide to the literature on learning probabilistic networks from data. IEEE Transactions on Knowledge and Data Engineering 8, 195–210 (1996)

    Article  Google Scholar 

  9. Box, G.E.P., Jenkins, G.: Time Series Analysis, Forecasting and Control. Holden-Day (1990)

    Google Scholar 

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© 2010 Springer-Verlag Berlin Heidelberg

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Kim, J., Kang, S., Kim, HM. (2010). Prediction of Personal Power Consumption Using the Moving Average Technique. In: Kim, Th., Stoica, A., Chang, RS. (eds) Security-Enriched Urban Computing and Smart Grid. SUComS 2010. Communications in Computer and Information Science, vol 78. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16444-6_29

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  • DOI: https://doi.org/10.1007/978-3-642-16444-6_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16443-9

  • Online ISBN: 978-3-642-16444-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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