An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting

Abstract

The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series. Indeed, both service interruptions and resource waste can be reduced with the implementation of an effective forecasting system. Significant research has thus been devoted to the design and development of methodologies for short term load forecasting over the past decades. A class of mathematical models, called Recurrent Neural Networks, are nowadays gaining renewed interest among researchers and they are replacing many practical implementation of the forecasting systems, previously based on static methods. Despite the undeniable expressive power of these architectures, their recurrent nature complicates their understanding and poses challenges in the training procedures. Recently, new important families of recurrent architectures have emerged and their applicability in the context of load forecasting has not been investigated completely yet. In this paper we perform a comparative study on the problem of Short-Term Load Forecast, by using different classes of state-of-the-art Recurrent Neural Networks. We test the reviewed models first on controlled synthetic tasks and then on different real datasets, covering important practical cases of study. We provide a general overview of the most important architectures and we define guidelines for configuring the recurrent networks to predict real-valued time series.

Springer Briefs in Computer Science (ISBN 978-3-319-70338-1), 2017

References in corpus (20)

• Sequence to Sequence Learning with Neural Networks
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• LSTM: A Search Space Odyssey
• On the difficulty of training Recurrent Neural Networks
• WaveNet: A Generative Model for Raw Audio
• A Critical Review of Recurrent Neural Networks for Sequence Learning
• Training Very Deep Networks
• Sequence Transduction with Recurrent Neural Networks
• Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge
• A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
• Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
• Group Sparse Regularization for Deep Neural Networks
• Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
• A Comprehensive Study of Deep Bidirectional LSTM RNNs for Acoustic Modeling in Speech Recognition
• Architectural Complexity Measures of Recurrent Neural Networks
• Investigating echo state networks dynamics by means of recurrence analysis
• Determination of the edge of criticality in echo state networks through Fisher information maximization
• Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification
• Identifying user habits through data mining on call data records
• Data-driven detrending of nonstationary fractal time series with echo state networks

Cited by in corpus (9)

• Graph Neural Networks with convolutional ARMA filters
• Stability of discrete-time feed-forward neural networks in NARX configuration
• The Echo Index and multistability in input-driven recurrent neural networks
• Bullwhip effect attenuation in supply chain management via control-theoretic tools and short-term forecasts: A preliminary study with an application to perishable inventories
• An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX models
• Towards lifelong learning of Recurrent Neural Networks for control design
• Accident Impact Prediction based on a deep convolutional and recurrent neural network model
• Outlier classification using Autoencoders: application for fluctuation driven flows in fusion plasmas
• Modelling stochastic time delay for regression analysis