Short Term Electricity Forecasting Using Individual Smart Meter Data

Abstract Smart metering is a quite new topic that has grown in importance all over the world and it appears to be a remedy for rising prices of electricity. Forecasting electricity usage is an important task to provide intelligence to the smart gird. Accurate forecasting will enable a utility provider to plan the resources and also to take control actions to balance the electricity supply and demand. 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, our contribution is the proposal for accurate short term electricity load forecasting for 24 hours ahead, not on the aggregate but on the individual household level.

[1]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[2]  Uwe Aickelin,et al.  The Application of a Data Mining Framework to Energy Usage Profiling in Domestic Residences Using UK Data , 2011, ArXiv.

[3]  Ryszard Szupiluk,et al.  Combining Forecasts with Blind Signal Separation Methods in Electric Load Prediction Framework , 2006, Artificial Intelligence and Applications.

[4]  P. McSharry,et al.  A comparison of univariate methods for forecasting electricity demand up to a day ahead , 2006 .

[5]  Ryszard Szupiluk,et al.  Multistage Covariance Approach to Measure the Randomness in Financial Time Series Analysis , 2011, KES-AMSTA.

[6]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .

[7]  Ryszard Szupiluk,et al.  Noise Detection for Latent Component Classification in Ensemble Method , 2010 .

[8]  Fredrik Wallin,et al.  Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting , 2012 .

[9]  James D. Hamilton Time Series Analysis , 1994 .

[10]  R. Weron Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach , 2006 .

[11]  Kevin J. Lomas,et al.  Identifying trends in the use of domestic appliances from household electricity consumption measurements , 2008 .

[12]  Alireza Khotanzad,et al.  A Neuro-Fuzzy Approach to Short-Term Load Forecasting in a Price-Sensitive Environment , 2002, IEEE Power Engineering Review.

[13]  Enrique Castillo,et al.  Electricity Load Forecast using Functional Networks , 2002 .

[14]  Brian Norton,et al.  Real-life energy use in the UK: How occupancy and dwelling characteristics affect domestic electricity use , 2008 .

[15]  Xiaodong Wang,et al.  Short-Term Load Forecasting in Power System Using Least Squares Support Vector Machine , 2006 .

[16]  Richard A. Davis,et al.  Introduction to time series and forecasting , 1998 .

[17]  P. W. Strike,et al.  Forecasting and control , 1991 .

[18]  Hesham K. Alfares,et al.  Electric load forecasting: Literature survey and classification of methods , 2002, Int. J. Syst. Sci..

[19]  Farshid Keynia,et al.  Short-Term Load Forecast of Microgrids by a New Bilevel Prediction Strategy , 2010, IEEE Transactions on Smart Grid.

[20]  John R. Williams,et al.  Towards Accurate Electricity Load Forecasting in Smart Grids , 2012, DBKDA 2012.

[21]  Ryszard Szupiluk,et al.  Noise Detection for Ensemble Methods , 2010, ICAISC.

[22]  Dug Hun Hong,et al.  Short-term load forecasting for the holidays using fuzzy linear regression method , 2005 .

[23]  S. Osowski,et al.  Blind source separation for improved load forecasting in the power system , 2005, Proceedings of the 2005 European Conference on Circuit Theory and Design, 2005..

[24]  Kathryn B. Janda,et al.  Buildings don't use energy: people do , 2011 .

[25]  V. Lo Brano,et al.  Forecasting daily urban electric load profiles using artificial neural networks , 2004 .