Probabilistic Load Forecasting via Quantile Regression Averaging on Sister Forecasts

Liu, Bidong; Nowotarski, Jakub; Hong, Tao; Weron, Rafał

doi:10.1109/tsg.2015.2437877

Cited by 192 publications

(152 citation statements)

References 29 publications

Supporting

Mentioning

152

Contrasting

Order By: Relevance

“…The analysis for determining the model which yields narrower PIW is done in this study using box and whisker plots, together with the probability density plots. A comparative analysis is done using the prediction intervals based on QRA [36].…”

Section: Prediction Intervalsmentioning

confidence: 99%

Probabilistic Hourly Load Forecasting Using Additive Quantile Regression Models

2018

View full text Add to dashboard Cite

Short-term hourly load forecasting in South Africa using additive quantile regression (AQR) models is discussed in this study. The modelling approach allows for easy interpretability and accounting for residual autocorrelation in the joint modelling of hourly electricity data. A comparative analysis is done using generalised additive models (GAMs). In both modelling frameworks, variable selection is done using least absolute shrinkage and selection operator (Lasso) via hierarchical interactions. Four models considered are GAMs and AQR models with and without interactions, respectively. The AQR model with pairwise interactions was found to be the best fitting model. The forecasts from the four models were then combined using an algorithm based on the pinball loss (convex combination model) and also using quantile regression averaging (QRA). The AQR model with interactions was then compared with the convex combination and QRA models and the QRA model gave the most accurate forecasts. Except for the AQR model with interactions, the other two models (convex combination model and QRA model) gave prediction interval coverage probabilities that were valid for the 90%, 95% and the 99% prediction intervals. The QRA model had the smallest prediction interval normalised average width and prediction interval normalised average deviation. The modelling framework discussed in this paper has established that going beyond summary performance statistics in forecasting has merit as it gives more insight into the developed forecasting models.

show abstract

Section: Prediction Intervalsmentioning

confidence: 99%

Probabilistic Hourly Load Forecasting Using Additive Quantile Regression Models

2018

View full text Add to dashboard Cite

show abstract

“…The first benchmark model is the multiple linear regression model (MLR) appeared as outliers detector. It is regarded as nave benchmark models in several probabilistic forecasting research [12,14,15]. The model is defined by:…”

Section: Benchmark Modelsmentioning

confidence: 99%

“…It has been utilized in modeling correlated stochastic variables [10] that can be used in generating probabilistic forecasts together with other statistic models. Statistical and other machine learning models were even more widely adopted in probabilistic forecasting like multiple linear regression [5,11], quantile regression [12], gradient boosting [13], general addictive model (GAM) [14], kernel density estimation (KDE) [15], etc.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Embedding based quantile regression neural network for probabilistic load forecasting

Gan

Wang

Yang

et al. 2018

J. Mod. Power Syst. Clean Energy

View full text Add to dashboard Cite

Compared to traditional point load forecasting, probabilistic load forecasting (PLF) has great significance in advanced system scheduling and planning with higher reliability. Medium term probabilistic load forecasting with a resolution to an hour has turned out to be practical especially in medium term energy trading and can enhance the performance of forecasting compared to those only utilizing daily information. Two main uncertainties exist when PLF is implemented: the first is the temperature fluctuation at the same time of each year; the second is the load variation which means that even if observed indicators are fixed since other observed external indicators can be responsible for the variation. Therefore, we propose a hybrid model considering both temperature uncertainty and load variation to generate medium term probabilistic forecasting with hourly resolution. An innovative quantile regression neural network with parameter embedding is established to capture the load variation, and a temperature scenario based technique is utilized to generate temperature forecasting in a probabilistic manner. It turns out that the proposed method overrides commonly used benchmark models in the case study.

show abstract

“…In literature, there are different methods for forecasting the future energy . In Li et al a method has been proposed for short‐term load forecasting based on wavelet transform and partial least squares regression.…”

Section: Introductionmentioning

confidence: 99%

A new combined method for future energy forecasting in electrical networks

Baesmat

Shiri

2018

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary Growing the industry and population in a region leads to the growth of required amount of electrical energy. Electrical companies should provide high quality energy according to the demand of customers. Load is an effective parameter of any system, and the network programmers should consider the annual load growth of the system and predict the required energy for each region by investigating previously recorded data of consumed electrical energy and stochastic analysis. In this paper, a new combined method for long‐term energy forecasting is proposed. This method, which combines the land‐consumption method and curve fitting based on generalization method, in addition to having simple calculations, takes into account the saturation. Moreover, loads from detailed formal program provided by the relevant institutes have been used, which results in better coordination between all organizations in charge of energy predictions and development of the countries. A suitable filtering method is also employed for input data to improve the method accuracy. To show the effectiveness of the proposed method, the results of different methods have been compared with those of the proposed method as well as real data. Then, real data of former 11 years of consumed energy gathered from Shiraz Electrical Distribution Company subscribers are employed and the energy for future 11 years is forecasted.

show abstract

Probabilistic Load Forecasting via Quantile Regression Averaging on Sister Forecasts

Cited by 192 publications

References 29 publications

Probabilistic Hourly Load Forecasting Using Additive Quantile Regression Models

Probabilistic Hourly Load Forecasting Using Additive Quantile Regression Models

Embedding based quantile regression neural network for probabilistic load forecasting

A new combined method for future energy forecasting in electrical networks

Contact Info

Product

Resources

About