Gaussian process prior models for electrical load forecasting

This paper examines models based on Gaussian process (GP) priors for electrical load forecasting. This methodology is seen to encompass a number of popular forecasting methods, such as basic structural models (BSMs) and seasonal auto-regressive intergrated (SARI) as special cases. The GP forecasting models are shown to have some desirable properties and their performance is examined on weekly and yearly Irish load data

[1]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[2]  Pat Bodger,et al.  Spectral decomposition and extrapolation of variations in electricity loading , 1986 .

[3]  Chris Chatfield,et al.  Comparative Models for Electrical Load Forecasting. , 1986 .

[4]  C. Chatfield,et al.  Comparative Models for Electrical Load Forecasting. , 1986 .

[5]  M. Crawford,et al.  An Adaptive Nonlinear Predictor with Orthogonal Escalator Structure for Short-Term Load Forecasting , 1989, IEEE Power Engineering Review.

[6]  Andrew Harvey,et al.  Forecasting, Structural Time Series Models and the Kalman Filter , 1990 .

[7]  B. Silverman,et al.  Nonparametric regression and generalized linear models , 1994 .

[8]  John V. Ringwood,et al.  Improvement of electricity consumption forecasts using temperature inputs , 1994, Simul. Pract. Theory.

[9]  S. Muto,et al.  Regression based peak load forecasting using a transformation technique , 1994 .

[10]  H. Mori,et al.  Optimal fuzzy inference for short-term load forecasting , 1995 .

[11]  Geoffrey E. Hinton,et al.  Bayesian Learning for Neural Networks , 1995 .

[12]  John Ringwood,et al.  Forecasting of Weekly Electricity Consumption Using Neural Networks , 1996 .

[13]  David Mackay,et al.  Gaussian Processes - A Replacement for Supervised Neural Networks? , 1997 .

[14]  Athanasios Kehagias,et al.  Short term load forecasting using a Bayesian combination method , 1997 .

[15]  R. Ramanathan,et al.  Short-run forecasts of electricity loads and peaks , 1997 .

[16]  Christopher K. I. Williams.  Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond , 1999, Learning in Graphical Models.

[17]  R. Adapa,et al.  Risk due to load forecast uncertainty in short term power system planning , 1998 .

[18]  Yusuf Al-Turki,et al.  A comparative study of medium-weather-dependent load forecasting using enhanced artificial/fuzzy neural network and statistical techniques , 1998, Neurocomputing.

[19]  P. Young,et al.  Dynamic harmonic regression. , 1999 .

[20]  Thomas Kailath,et al.  Fast reliable algorithms for matrices with structure , 1999 .

[21]  W. Charytoniuk,et al.  Demand forecasting in power distribution systems using nonparametric probability density estimation , 1999 .

[22]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[23]  Georges A. Darbellay,et al.  Forecasting the short-term demand for electricity: Do neural networks stand a better chance? , 2000 .

[24]  John V. Ringwood,et al.  Forecasting Electricity Demand on Short, Medium and Long Time Scales Using Neural Networks , 2001, J. Intell. Robotic Syst..