Online Gaussian Process Regression for Short-term Probabilistic Interval Load Prediction

We propose a hybrid probabilistic interval prediction method for short-term load forecasting. The method combines K-means clustering based feature selection approaches and online Gaussian processes regression(OGPR) to generate better prediction results. The K-means clustering algorithm based feature selection are used to select the most relevant features during a dynamical process to better capture the load characters along with time. OGRP, includes dynamically updating the hyper-parameters and training sample sets as two key features, is served as a forecasting engine to carry out load probability interval prediction. The load data from Queensland market, Australia is used to validate the model proposed. The comparative results show that the proposed approach can obtain higher quality prediction interval.

[1]  Chuen-Sheng Cheng,et al.  Identification of seasonal short-term load forecasting models using statistical decision functions , 1990 .

[2]  Rui Araújo,et al.  An adaptive ensemble of on-line Extreme Learning Machines with variable forgetting factor for dynamic system prediction , 2016, Neurocomputing.

[3]  Z. Dong,et al.  A Statistical Approach for Interval Forecasting of the Electricity Price , 2008, IEEE Transactions on Power Systems.

[4]  Peng Kou,et al.  Probabilistic wind power forecasting with online model selection and warped gaussian process , 2014 .

[5]  Georgios Giasemidis,et al.  A hybrid model of kernel density estimation and quantile regression for GEFCom2014 probabilistic load forecasting , 2016, 1610.05183.

[6]  Kit Po Wong,et al.  Optimal Prediction Intervals of Wind Power Generation , 2014, IEEE Transactions on Power Systems.

[7]  Abbas Khosravi,et al.  Uncertainty handling using neural network-based prediction intervals for electrical load forecasting , 2014 .

[8]  Tao Hong,et al.  Probabilistic electric load forecasting: A tutorial review , 2016 .

[9]  Dahua GAN,et al.  Embedding based quantile regression neural network for probabilistic load forecasting , 2018 .

[10]  Tao Hong,et al.  A Naïve multiple linear regression benchmark for short term load forecasting , 2011, 2011 IEEE Power and Energy Society General Meeting.

[11]  Suhartono Suhartono,et al.  Short term load forecasting using double SARIMA Model , 2010 .

[12]  Abbas Khosravi,et al.  A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings , 2015 .

[13]  Tao Hong,et al.  Temperature Scenario Generation for Probabilistic Load Forecasting , 2018, IEEE Transactions on Smart Grid.

[14]  Saguna Saguna,et al.  Applied machine learning: Forecasting heat load in district heating system , 2016 .

[15]  Ahmet Teke,et al.  A state-of-the-art review of artificial intelligence techniques for short-term electric load forecasting , 2017, 2017 6th International Youth Conference on Energy (IYCE).

[16]  Andreas Svensson,et al.  Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes , 2018 .

[17]  Jing Wang,et al.  A survey on online feature selection with streaming features , 2018, Frontiers of Computer Science.

[18]  Chongqing Kang,et al.  On Normality Assumption in Residual Simulation for Probabilistic Load Forecasting , 2017, IEEE Transactions on Smart Grid.

[19]  Tao Hong,et al.  Probabilistic Load Forecasting via Quantile Regression Averaging on Sister Forecasts , 2017, IEEE Transactions on Smart Grid.

[20]  Devendra K. Chaturvedi,et al.  Short term load forecast using fuzzy logic and wavelet transform integrated generalized neural network , 2015 .

[21]  Xindong Wu,et al.  Online feature selection for high-dimensional class-imbalanced data , 2017, Knowl. Based Syst..

[22]  Souhaib Ben Taieb,et al.  A gradient boosting approach to the Kaggle load forecasting competition , 2014 .

[23]  P Pinson,et al.  Conditional Prediction Intervals of Wind Power Generation , 2010, IEEE Transactions on Power Systems.

[24]  Rui Zhang,et al.  Short-term load forecasting of Australian National Electricity Market by an ensemble model of extreme learning machine , 2013 .