Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond

Hong, Tao; Pinson, Pierre; Fan, Shangchun; Zareipour, Hamidreza; Troccoli, Alberto; Hyndman, Rob J.

doi:10.1016/j.ijforecast.2016.02.001

Cited by 705 publications

(387 citation statements)

References 10 publications

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“…In [4] it is indicated that a usual feature of renewable energy is that the output of power plants largely depends on weather conditions. Thus, the integration of knowledge or data about weather could benefit the time series modelling process to provide an accurate forecast instead of a unique approach with one single value corresponding to the consumption data [17,33].…”

Section: Narx Modelmentioning

confidence: 99%

An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings

et al. 2016

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This paper addresses the problem of energy consumption prediction using neural networks over a set of public buildings. Since energy consumption in the public sector comprises a substantial share of overall consumption, the prediction of such consumption represents a decisive issue in the achievement of energy savings. In our experiments, we use the data provided by an energy consumption monitoring system in a compound of faculties and research centers at the University of Granada, and provide a methodology to predict future energy consumption using nonlinear autoregressive (NAR) and the nonlinear autoregressive neural network with exogenous inputs (NARX), respectively. Results reveal that NAR and NARX neural networks are both suitable for performing energy consumption prediction, but also that exogenous data may help to improve the accuracy of predictions.

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Section: Narx Modelmentioning

confidence: 99%

An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings

et al. 2016

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“…• nine variants of three parsimonious autoregressive model structures with exogenous variables (ARX): one originally proposed by Misiorek et al [19] and later used in a number of EPF studies [13,18,[20][21][22][23][24][25][26][27], one which evolved from it during the successful participation of TEAM POLAND in the Global Energy Forecasting Competition 2014 (GEFCom2014; see [28][29][30]) and an extension of the former, which creates a stronger link with yesterday's prices and additionally considers a second exogenous variable (zonal load or wind power), • three two-year long, hourly resolution test periods from three distinct power markets (GEFCom2014, Nord Pool and the U.K.), • nine variants of five classes of selection and shrinkage procedures: single-step elimination of insignificant predictors (without or with constraints), stepwise regression (with forward selection or backward elimination), ridge regression, lasso and three elastic nets (with α = 0.25, 0.5 or 0.75), • model validation in terms of the robust weekly-weighted mean absolute error (WMAE; see [1]) and the Diebold-Mariano (DM; see [31]) test and draw statistically-significant conclusions of high practical value. The remainder of the paper is structured as follows.…”

Section: Introductionmentioning

confidence: 99%

Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting

2016

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Abstract:In day-ahead electricity price forecasting (EPF) variable selection is a crucial issue. Conducting an empirical study involving state-of-the-art parsimonious expert models as benchmarks, datasets from three major power markets and five classes of automated selection and shrinkage procedures (single-step elimination, stepwise regression, ridge regression, lasso and elastic nets), we show that using the latter two classes can bring significant accuracy gains compared to commonly-used EPF models. In particular, one of the elastic nets, a class that has not been considered in EPF before, stands out as the best performing model overall.

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“…We will split the data using the following approach: 1) train from 2012-06-01 to 2013-05-31; 2) validation from 2013-06-01 to 2014-05-31; 3) test from 2014-06-01 to 2014-07-01. The complete dataset is accessible via [8].…”

Section: Datasetsmentioning

confidence: 99%

“…To test the usefulness of this new framework, experiments using datasets from the American Meteorological Society (AMS), solar radiation prediction contest [7] and from the 2014 Global Energy Forecasting Competition (GEFCom2014) [8] are carried out. The goal of these contests is to achieve the best short term predictions and the best probabilistic distribution, respectively.…”

Section: Introductionmentioning

confidence: 99%

General noise support vector regression with non-constant uncertainty intervals for solar radiation prediction

Prada

Dorronsoro

2018

J. Mod. Power Syst. Clean Energy

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General noise cost functions have been recently proposed for support vector regression (SVR). When applied to tasks whose underlying noise distribution is similar to the one assumed for the cost function, these models should perform better than classical -SVR. On the other hand, uncertainty estimates for SVR have received a somewhat limited attention in the literature until now and still have unaddressed problems. Keeping this in mind, three main goals are addressed here. First, we propose a framework that uses a combination of general noise SVR models with naive online R minimization algorithm (NORMA) as optimization method, and then gives nonconstant error intervals dependent upon input data aided by the use of clustering techniques. We give theoretical details required to implement this framework for Laplace, Gaussian, Beta, Weibull and Marshall-Olkin generalized exponential distributions. Second, we test the proposed framework in two real-world regression problems using data of two public competitions about solar energy. Results show the validity of our models and an improvement over classical -SVR. Finally, in accordance with the principle of reproducible research, we make sure that data and model implementations used for the experiments are easily and publicly accessible.

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Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond

Cited by 705 publications

References 10 publications

An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings

An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings

Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting

General noise support vector regression with non-constant uncertainty intervals for solar radiation prediction

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