Global Energy Forecasting Competition 2012

By Tao Hong, Pierre Pinson, Shu Fan. 2014

555 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/9b1b1ee2c8761b5ac6c77ca3c7a34c184f154088

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

In Ocean Engineering

Citations in the corpus

Deep Learning for Household Load Forecasting—A Novel Pooling Deep RNN (2018, background): “load forecasting at aggregated level already exist [14]–[26], [40]–[42].”
Of quantiles and expectiles: consistent scoring functions, Choquet representations and forecast rankings (2015, background): “See, for example, Hong et al. (2014) for a description of the global energy forecasting competition 2012.”
Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges (2018, background): “The case study was based on the data from the load forecasting track of GEFCom2012.”
Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities (2015, methodology): “Although this aspect is emphasized in [1], nobody did grouping in the GEFCom2012 competition.”
Probabilistic Load Forecasting via Quantile Regression Averaging on Sister Forecasts (2017, methodology): “For instance, the so-called recency effect models in [26] are sister models, because they are all built based on the same model, Tao’s Vanilla benchmark model [6], [8]”
Deep Learning for Time Series Forecasting: The Electric Load Case (2019, background): “19 A PREPRINT Reference Predictive Family of Models Time Horizon Exogenous Variables Dataset (Location) [18] LSTM, GRU, ERNN, NARX, ESN D - Rome, Italy [18] LSTM, GRU, ERNN, NARX, ESN D T New England [27] [28] ERNN H T, H, P, other Palermo, Italy [17] ERNN H T, W, H Hubli, India [20] ESN 15min to 1Y”
On-line quantile regression in the RKHS (Reproducing Kernel Hilbert Space) for operational probabilistic forecasting of wind power (2016, methodology): “The case-study considered in this work consists in one real wind farm 8 from the Global Energy Forecasting Competition dataset (GEFCOM 2012 9 - third wind farm), which is freely available in [31].”
A new high-dimensional time series approach for wind speed, wind direction and air pressure forecasting (2017, methodology): “The GBM is used for the Global Energy Forecasting Competition GEFCom2012 and 2014 (see Hong et al., 2014, Landry et al., 2016).”