FORECASTING OF INDIVIDUAL ELECTRICITY USAGE USING SMART METER DATA
Languages of publication
Forecasting electricity usage is an important task to provide intelligence to the smart gird. The customers will benefit from metering solutions through greater understanding of their own energy consumption and future projections, allowing them to better manage costs of their usage. In this proof of concept paper, we show the approach for short term electricity load forecasting for 24 hours ahead, calculated on the individual household level. In this context authors will develop an approach to the analysis and prediction using Multivariate Adaptive Regression Splines (MARSplines).
- Alfares H.K., Nazeeruddin M. (2002) Electric load forecasting: literature survey and classification of method, International Journal of Systems Science, vol. 33(1), 3–34.
- Beccali M., Cellura M., Brano V.L., Marvuglia A. (2004) Forecasting daily urban electric load profiles using artificial neural networks, Energy Conversion and Management, vol. 45, 2879–2900.
- Brockwell P.J., Davis R.A. (2002) Introduction to Time Series and Forecasting, Springer.
- Castillo E., Guijarro B., Alonso M. (2001) Electricity Load Forecast using Functional Networks, Report for EUNITE 2001 Competition, available at http://neuron.tuke.sk/competition/ on 2013-07-10.
- Friedman J.H. (1991) Multivariate Adaptive Regression Splines, The Annals of Statistics, vol. 19, 1–141.
- Hippert H.S, Pedreira C.E., Souza R.C. (2001) Neural networks for short term load forecasting: a review and evaluation, IEEE Transactions on Power Systems, vol. 16, 44–55.
- Javed F., Arshad N., Wallin F., Vassileva I., Dahlquist E. (2012) Forecasting for demand response in smart grids: an analysis on use of anthropologic and structural data and short term multiple loads forecasting, Applied Energy, vol. 69, 150–160.
- Khotanzad A., Zhou E., Elragal H. (2002) A neuro-fuzzy approach to short-term load forecasting in a price-sensitive environment, IEEE Transactions on Power Systems, vol. 17, 1273–1282.
- Song K.B., Baek Y.S., Hong D.H., Jang G. (2005) Short-term load forecasting for the holidays using fuzzy linear regression method, IEEE Transactions on Power Systems, vol. 20, 96–101.
- Weron R. (2006) Modeling and forecasting electricity loads and prices: A statistical approach, Wiley, Chichester.
Publication order reference