DocumentCode :
3696410
Title :
Combining load forecasts from independent experts
Author :
Jingrui Xie;Bidong Liu; Xiaoqian Lyu;Tao Hong;David Basterfield
Author_Institution :
Energy Production and Infrastructure Center, University of North Carolina at Charlotte, USA
fYear :
2015
Firstpage :
1
Lastpage :
5
Abstract :
The NPower Forecasting Challenge 2015 invited students and professionals worldwide to predict daily energy usage of a group of customers. The BigDEAL team from the Big Data Energy Analytics Laboratory landed a top 3 place in the final leaderboard. This paper presents a refined methodology based on the implementation during the competition. We first build the individual forecasts using several forecast techniques, such as Multiple Linear Regression (MLR), Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) and Random Forests (RF). We then select a subset of the individual forecasts based on their performance on a validation period, a.k.a. post-sample. Finally we obtain the final forecast by averaging the selected individual forecasts. The forecast combination on average yields a better result than the forecast from a single technique.
Keywords :
"Forecasting","Load forecasting","Predictive models","Market research","Artificial neural networks","Energy consumption","Weather forecasting"
Publisher :
ieee
Conference_Titel :
North American Power Symposium (NAPS), 2015
Type :
conf
DOI :
10.1109/NAPS.2015.7335138
Filename :
7335138
Link To Document :
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