Paper Info
Title
Deep Learning For Household Load Forecasting-A Novel Pooling Deep Rnn
Abstract
The key challenge for household load forecasting lies in the high volatility and uncertainty of load profiles. Traditional methods tend to avoid such uncertainty by load aggregation (to offset uncertainties), customer classification (to cluster uncertainties) and spectral analysis (to filter out uncertainties). This paper, for the first time, aims to directly learn the uncertainty by applying a new breed of machine learning algorithms deep learning. However, simply adding layers in neural networks will cap the forecasting performance due to the occurrence of over-fitting. A novel pooling-based deep recurrent neural network is proposed in this paper which batches a group of customers' load profiles into a pool of inputs. Essentially the model could address the over-fitting issue by increasing data diversity and volume. This paper reports the first attempts to develop a bespoke deep learning application for household load forecasting and achieved preliminary success. The developed method is implemented on Tensorflow deep learning platform and tested on 920 smart metered customers from Ireland. Compared with the state-of-the-art techniques in household load tore casting, the proposed method outperforms ARIMA by 19.5%, SVR by 13.1% and classical deep RNN by 6.5% in terms of RMSE.
Year
DOI
Venue
2018
10.1109/TSG.2017.2686012
IEEE TRANSACTIONS ON SMART GRID
Keywords
Field
DocType
Big data, deep learning, load forecasting, long short-term memory, machine learning, neural network, smart meter
Data mining,Pooling,Mean squared error,Recurrent neural network,Autoregressive integrated moving average,Artificial intelligence,Deep learning,Engineering,Artificial neural network,Volatility (finance),Offset (computer science),Machine learning
Journal
Volume
Issue
ISSN
9
5
1949-3053
Citations 
PageRank 
References 
7
0.63
0
Authors
3
Name
Order
Citations
PageRank
Heng Shi171.30
Minghao Xu271.30
Ran Li3142.45