Short-Term Load Forecasting With Deep Residual Networks

@article{Chen2019ShortTermLF,
  title={Short-Term Load Forecasting With Deep Residual Networks},
  author={Kunjin Chen and Kunlong Chen and Qin Wang and Ziyu He and Jun Hu and Jinliang He},
  journal={IEEE Transactions on Smart Grid},
  year={2019},
  volume={10},
  pages={3943-3952}
}
We present in this paper a model for forecasting short-term electric load based on deep residual networks. The proposed model is able to integrate domain knowledge and researchers’ understanding of the task by virtue of different neural network building blocks. Specifically, a modified deep residual network is formulated to improve the forecast results. Further, a two-stage ensemble strategy is used to enhance the generalization capability of the proposed model. We also apply the proposed model… 
Ensemble Residual Networks for Short-Term Load Forecasting
TLDR
Numerical testing shows that the proposed model can get better forecasting results in comparison with other methods, and the ensemble method adopted effectively improves the generalization ability of the model.
Integration of Ensemble GoogLeNet and Modified Deep Residual Networks for Short-Term Load Forecasting
TLDR
A novel hybrid method based on ensemble GoogLeNet and modified deep residual networks for short-term load forecasting (STLF) that achieves accurate prediction results, strong generalization capability, and satisfactory coverages for different prediction intervals, along with reducing operation times.
Stacked Boosters Network Architecture for Short Term Load Forecasting in Buildings
TLDR
A novel deep learning architecture for short-term load forecasting of building energy loads based on a simple base learner and multiple boosting systems that are modelled as a single deep neural network that outperforms state-of-the-art load forecasting model in all the tasks.
Short-Term Power Load Forecasting Based on Empirical Mode Decomposition and Deep Neural Network
  • Li-Min Cheng, Yuqing Bao
  • Engineering, Computer Science
    Proceedings of PURPLE MOUNTAIN FORUM 2019-International Forum on Smart Grid Protection and Control
  • 2019
TLDR
A short-term load forecasting method based on empirical mode decomposition and deep neural network is proposed, which successfully decomposes the load into different timescales, based on which the deep-neural-network-based forecasting model is established.
Short-Term Load Forecasting with LSTM Based Ensemble Learning
  • Lingxiao Wang, S. Mao, B. Wilamowski
  • Computer Science
    2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)
  • 2019
TLDR
A Fully Connected Cascade Neural Network is incorporated for ensemble learning, which is solved by an enhanced Levenberg-Marquardt (LM) training algorithm, and its superior performance over several baseline schemes is demonstrated.
Ultra-Short-Term Industrial Power Demand Forecasting Using LSTM Based Hybrid Ensemble Learning
TLDR
Experimental results show that the proposed LSTM network based hybrid ensemble learning forecasting model outperforms several state-of-the-art time series forecasting models, and obtains higher accuracy and robustness to forecast peak demand.
Very‐short‐term load forecasting based on empirical mode decomposition and deep neural network
Very‐short‐term load forecasting (VSTLF) predicts the load from minutes to 1‐hour timescale. Effective forecasting is important for in‐day scheduling of the power systems. In this paper, a VSTLF
Deep Learning Based on Multi-Decomposition for Short-Term Load Forecasting
TLDR
Applications of deep learning using feature decomposition for improving the accuracy of load forecasting are presented, and the performance of the proposed approach is assessed by comparing the predicted results with those of conventional methods, nonlinear autoregressive networks with exogenous inputs, and long short-term memory network-based feature decompose.
Short-Term Load Forecasting Based on Deep Learning Bidirectional LSTM Neural Network
TLDR
A multi-layer stacked bidirectional long short-term memory (LSTM)-based short- term load forecasting framework that includes neural network architecture, model training, and bootstrapping is presented; the method can extract dynamic features from the data as well as make accurate predictions.
...
...

References

SHOWING 1-10 OF 51 REFERENCES
Deep neural network based demand side short term load forecasting
TLDR
This paper proposes deep neural network based load forecasting models, and applies them to demand side empirical load database and shows that DNNs exhibit accurate and robust forecasts compared to other forecasting models.
Deep Neural Network Regression for Short-Term Load Forecasting of Natural Gas
TLDR
It is determined that the proposed network outperforms traditional artificial neural networks and linear regression based forecasters.
Feature extraction via multiresolution analysis for short-term load forecasting
TLDR
Two strategies for embedding the discrete wavelet transform into neural network-based short-term load forecasting are described, which aim is to develop more robust load forecasters.
Short-term load forecasting: Similar day-based wavelet neural networks
In deregulated electricity markets, short term load forecasting is important for reliable power system operations, and significantly affects market participants. It is difficult and challenging in
A Novel Wavelet-Based Ensemble Method for Short-Term Load Forecasting with Hybrid Neural Networks and Feature Selection
TLDR
A new ensemble forecasting model for short-term load forecasting (STLF) is proposed based on extreme learning machine (ELM) and partial least squares regression is utilized as a combining approach to aggregate the individual forecasts.
A Strategy for Short-Term Load Forecasting by Support Vector Regression Machines
TLDR
Two important improvements to the SVR based load forecasting method are introduced, i.e., procedure for generation of model inputs and subsequent model input selection using feature selection algorithms and the use of the particle swarm global optimization based technique for the optimization of SVR hyper-parameters reduces the operator interaction.
A Novel RBF Training Algorithm for Short-Term Electric Load Forecasting and Comparative Studies
TLDR
This paper investigates the effectiveness of some of the newest designed algorithms in machine learning to train typical radial basis function (RBF) networks for 24-h electric load forecasting: support vector regression (SVR), extreme learning machines (ELMs), decay RBF neural networks (DRNNs), improves second order, and error correction, drawing some conclusions useful for practical implementations.
Nonparametric regression based short-term load forecasting
This paper presents a novel approach to short-time load forecasting by the application of nonparametric regression. The method is derived from a load model in the form of a probability density
...
...