Souhaib BenTaieb, Raphael Huser, Rob J. Hyndman and Marc G. Genton

IEEE Transactions on Smart Grid (2016), 7(5), 2448-2455.

Abstract: Smart electricity meters are currently deployed in millions of households to collect detailed individual electricity consumption data. Compared to traditional electricity data based on aggregated consumption, smart meter data are much more volatile and less predictable. There is a need within the energy industry for probabilistic forecasts of household electricity consumption to quantify the uncertainty of future electricity demand, in order to undertake appropriate planning of generation and distribution. We propose a probabilistic forecasting method where a different quantile regression model is estimated for each quantile of the future distribution. Each model is estimated by boosting additive quantile regression which enjoys many useful properties including flexibility, interpretability and automatic variable selection. We compare our approach with three benchmark methods on both aggregated and disaggregated scales using a smart meter dataset collected from 3639 households in Ireland at 30-minute intervals over a period of 1.5 years. The empirical results demonstrate that our approach based on quantile regression provides better forecast accuracy for disaggregated demand while the traditional approach based on a normality assumption provides better forecasts for aggregated demand. These results are particularly useful since more energy data will become available at the disaggregated level in the future.

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  Tag: electricity

14 posts
February 4th, 2016

Forecasting uncertainty in electricity smart meter data by boosting additive quantile regression

Souhaib BenTaieb, Raphael Huser, Rob J. Hyndman and Marc G. Genton IEEE Transactions on Smart Grid (2016), 7(5), 2448-2455. Abstract: […]

January 25th, 2016

Probabilistic Energy Forecasting: Global Energy Forecasting Competition 2014 and Beyond

Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J Hyndman International Journal of Forecasting (2016), 32(3), 896–913. […]

June 23rd, 2015

MEFM: An R package for long-term probabilistic forecasting of electricity demand

International Symposium on Forecasting Riverside, California   I will describe and demonstrate a new open-source R package that implements the […]

June 19th, 2015

Probabilistic forecasting of peak electricity demand

Southern California Edison Rosemead, California   Electricity demand forecasting plays an important role in short-term load allocation and long-term planning […]

June 8th, 2015

STR: A Seasonal-Trend Decomposition Procedure Based on Regression

By Alex Dokumentov and Rob J Hyndman

June 4th, 2015

Probabilistic time series forecasting with boosted additive models: an application to smart meter data

By Souhaib Ben Taieb, Raphael Huser, Rob J Hyndman and Marc G Genton

May 22nd, 2015

Probabilistic forecasting of long-term peak electricity demand

The latest version of my talk on electricity demand forecasting.
Given to the “Monash Energy Materials and Systems Institute”

December 17th, 2014

MEFM package for R

The MEFM package for R includes a set of tools for implementing the Monash Electricity Forecasting Model.

June 17th, 2014

Challenges in forecasting peak electricity demand

A two-part seminar given at the Energy Forum, Valais/Wallis, Switzerland.

May 22nd, 2014

Monash Electricity Forecasting Model

By Rob J Hyndman and Shu Fan

April 1st, 2014

A gradient boosting approach to the Kaggle load forecasting competition

By Souhaib Ben Taieb and Rob J Hyndman

International Journal of Forecasting (2014), 30(2), 382–394.

February 1st, 2012

Short-term load forecasting based on a semi-parametric additive model

Shu Fan and Rob J Hyndman Revised 10 January 2011 IEEE Transactions on Power Systems (2012), 27(1), 134-141. Abstract Short-term […]

October 3rd, 2011

Forecasting electricity demand distributions using a semiparametric additive model

Talk given at University of Melbourne, 11 October 2011. University of Adelaide, 16 March 2012 Monash University, 16 May 2012 […]

June 15th, 2011

Evaluating extreme quantile forecasts

Talk to be given at the International Symposium on Forecasting, Prague, 26–29 June 2011. Slides

March 31st, 2011

The price elasticity of electricity demand in South Australia

Shu Fan and Rob J Hyndman Business and Economic Forecasting Unit, Monash University, Clayton, Victoria 3800, Australia Energy policy (2011), […]

July 21st, 2010

Short-term load forecasting based on a semi-parametric additive model

Shu Fan and Rob J Hyndman 20th Australasian Universities Power Engineering Conference 5-8 December 2010, University of Canterbury, Christchurch, New […]

January 3rd, 2010

Density forecasting for long-term peak electricity demand

Rob J Hyndman and Shu Fan IEEE Transactions on Power Systems, 2010, 25(2), 1142-1153 Abstract: Long-term electricity demand forecasting plays […]

June 23rd, 2009

Extreme forecasting

Keynote address, International Symposium on Forecasting, June 2009. Abstract Extremely bad data, extremely poor methods and extremely difficult problems will […]

June 25th, 2007

Forecasting medium- and long-term peak electricity demand

When: 25 June 2007 Where: International Symposium on Forecasting, New York Abstract: Peak electricity demand forecasting is important in medium […]