Deep Learning for Household Load Forecasting—A Novel Pooling Deep RNN

By Heng Shi, Minghao Xu, Ran Li. IEEE Transactions on Smart Grid, 2018

983 citations recorded by the source corpus.

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 forecasting, the proposed method outperforms ARIMA by 19.5%, SVR by 13.1% and classical deep RNN by 6.5% in terms of RMSE.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/d140fb7f9537c309e50845dd6cd06a4482ac57c5

How researchers see this paper

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

In Computer Vision and Pattern Recognition

In Electrical and Electronic Engineering

Citations in the corpus

A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network (2018, methodology): “In [23], A novel pooling-based deep RNN is applied for household load forecast and achieves preliminary success.”
Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review (2020, neutral): “Image Caption [86]; Data Forecasting [87]; Natural Language Processing [88]”
Big data analytics in smart grids: a review (2018, methodology): “To overcome the volatility and uncertainty of load profiles, the recurrent neural network is adopted with a novel pooling layer to avoid overfitting problems in (Shi et al., 2017).”
Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges (2018, background): “A pooling-based deep recurrent neural network (RNN) was proposed in [28] to learn spatial information shared between interconnected customers and to address the over-fitting challenges.”
Deep Learning-Based Multivariate Probabilistic Forecasting for Short-Term Scheduling in Power Markets (2019, background): “Such models have proved their superiority in many tasks such as load forecasting [21], [22], and are extensively used by major technology companies for products such as Google Translate [23] or speech recognition applications in smartphones [24].”
Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques (2019, background): “Authors in [32] proposed a pooling deep recurrent neural network (PDRNN) model to forecast household load.”
Review of low voltage load forecasting: Methods, applications, and recommendations (2021, methodology): “Individual Residential Very Statistical and time series Approaches [80] Short-term Machine Learning and other Artificial Intelligence Approaches [61,63,128,144,156,195,216,217] Comparing Methods [39,48,106,166] Probabilistic Forecasting [222,222,229] Hybrid, Combination and Ensemble Approaches [7, 5”
Energy Forecasting: A Review and Outlook (2020, background): “Various advanced AI/ML techniques such as deep learning [27], [28], reinforcement learning [29], and transfer”