Assessing the performance of deep learning models for multivariate probabilistic energy forecasting
暂无分享,去创建一个
Arto Kaarna | Lasse Lensu | Samuli Honkapuro | Toni Kuronen | Aleksei Mashlakov | S. Honkapuro | L. Lensu | A. Kaarna | Aleksei Mashlakov | Toni Kuronen
[1] Bryan Lim,et al. Time-series forecasting with deep learning: a survey , 2020, Philosophical Transactions of the Royal Society A.
[2] Li Sun,et al. Short-term global horizontal irradiance forecasting based on a hybrid CNN-LSTM model with spatiotemporal correlations , 2020, Renewable Energy.
[3] Rob J Hyndman,et al. Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality , 2020, International Journal of Forecasting.
[4] Li Li,et al. Improved Deep Mixture Density Network for Regional Wind Power Probabilistic Forecasting , 2020, IEEE Transactions on Power Systems.
[5] Jianhui Wang,et al. Convolutional Graph Autoencoder: A Generative Deep Neural Network for Probabilistic Spatio-Temporal Solar Irradiance Forecasting , 2020, IEEE Transactions on Sustainable Energy.
[6] Jianzhong Zhou,et al. Probabilistic spatiotemporal wind speed forecasting based on a variational Bayesian deep learning model , 2020 .
[7] Ashfaqur Rahman,et al. ForecastNet: A Time-Variant Deep Feed-Forward Neural Network Architecture for Multi-Step-Ahead Time-Series Forecasting , 2020, ICONIP.
[8] Arto Kaarna,et al. Probabilistic Forecasting of Battery Energy Storage State-of-Charge under Primary Frequency Control , 2020, IEEE Journal on Selected Areas in Communications.
[9] Yi Wang,et al. Using Bayesian Deep Learning to Capture Uncertainty for Residential Net Load Forecasting , 2020, IEEE Transactions on Power Systems.
[10] Jinfu Chen,et al. Learning Temporal and Spatial Correlations Jointly: A Unified Framework for Wind Speed Prediction , 2020, IEEE Transactions on Sustainable Energy.
[11] Zizhuo Wang,et al. Probabilistic Forecasting with Temporal Convolutional Neural Network , 2019, Neurocomputing.
[12] F. Diebold,et al. Comparing Predictive Accuracy , 1994, Business Cycles.
[13] Syama Sundar Rangapuram,et al. GluonTS: Probabilistic and Neural Time Series Modeling in Python , 2020, J. Mach. Learn. Res..
[14] Fotios Petropoulos,et al. The M4 competition: Conclusions , 2020 .
[15] Weijun Hong,et al. Deep ensemble learning based probabilistic load forecasting in smart grids , 2019 .
[16] Ao Tang,et al. DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting , 2019, CIKM.
[17] DSANet , 2019, Proceedings of the 28th ACM International Conference on Information and Knowledge Management.
[18] Jianchun Peng,et al. A review of deep learning for renewable energy forecasting , 2019, Energy Conversion and Management.
[19] Xiaoxia Qi,et al. A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network , 2019, Applied Energy.
[20] Pierre Pinson,et al. The future of forecasting for renewable energy , 2019, WIREs Energy and Environment.
[21] Aleksei Romanenko,et al. Hyper-parameter Optimization of Multi-attention Recurrent Neural Network for Battery State-of-Charge Forecasting , 2019, EPIA.
[22] Andrea Vitali,et al. Bayesian deep learning based method for probabilistic forecast of day-ahead electricity prices , 2019, Applied Energy.
[23] Roy Assaf,et al. Explainable Deep Neural Networks for Multivariate Time Series Predictions , 2019, IJCAI.
[24] Carsten Croonenbroeck,et al. Renewable generation forecast studies – Review and good practice guidance , 2019, Renewable and Sustainable Energy Reviews.
[25] Tim Januschowski,et al. Deep Factors for Forecasting , 2019, ICML.
[26] Hsiang-Fu Yu,et al. Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting , 2019, NeurIPS.
[27] Yong Chen,et al. Multifactor spatio-temporal correlation model based on a combination of convolutional neural network and long short-term memory neural network for wind speed forecasting , 2019, Energy Conversion and Management.
[28] Jianhui Wang,et al. Spatio-Temporal Graph Deep Neural Network for Short-Term Wind Speed Forecasting , 2019, IEEE Transactions on Sustainable Energy.
[29] Jean-François Toubeau,et al. Deep Learning-Based Multivariate Probabilistic Forecasting for Short-Term Scheduling in Power Markets , 2019, IEEE Transactions on Power Systems.
[30] Martin Jahn,et al. Open Power System Data - Frictionless data for electricity system modelling , 2018, ArXiv.
[31] Hung-yi Lee,et al. Temporal pattern attention for multivariate time series forecasting , 2018, Machine Learning.
[32] Honkapuro Samuli,et al. Hyper-parameter Optimization of Multi-attention Recurrent Neural Network for Battery State-of-Charge Forecasting , 2019 .
[33] Shou-De Lin,et al. A Memory-Network Based Solution for Multivariate Time-Series Forecasting , 2018, ArXiv.
[34] Guokun Lai,et al. Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks , 2017, SIGIR.
[35] Joakim Widén,et al. Review on probabilistic forecasting of photovoltaic power production and electricity consumption , 2018 .
[36] Matthias W. Seeger,et al. Deep State Space Models for Time Series Forecasting , 2018, NeurIPS.
[37] Bri-Mathias Hodge,et al. Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry , 2017 .
[38] Pierre Pinson,et al. Benefits of spatiotemporal modeling for short‐term wind power forecasting at both individual and aggregated levels , 2017, 1704.07606.
[39] Valentin Flunkert,et al. DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks , 2017, International Journal of Forecasting.
[40] Yitao Liu,et al. Deep learning based ensemble approach for probabilistic wind power forecasting , 2017 .
[41] Inderjit S. Dhillon,et al. Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction , 2016, NIPS.
[42] Tao Hong,et al. Probabilistic electric load forecasting: A tutorial review , 2016 .
[43] P. Pinson,et al. Generation and evaluation of space–time trajectories of photovoltaic power , 2016, 1603.06649.
[44] Zoubin Ghahramani,et al. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.
[45] R. Weron,et al. Recent advances in electricity price forecasting: A review of probabilistic forecasting , 2016 .
[46] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[47] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[48] Nicolas Chapados,et al. Effective Bayesian Modeling of Groups of Related Count Time Series , 2014, ICML.
[49] Carlos Moreira,et al. Handling renewable energy variability and uncertainty in power systems operation , 2014 .
[50] S Roberts,et al. Gaussian processes for time-series modelling , 2013, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[51] Yoshua Bengio,et al. Algorithms for Hyper-Parameter Optimization , 2011, NIPS.
[52] C. Alexander,et al. Market risk analysis IV: value-at-risk models , 2008 .
[53] A. Raftery,et al. Probabilistic forecasts, calibration and sharpness , 2007 .
[54] Rob J Hyndman,et al. 25 years of time series forecasting , 2006 .
[55] Francis Eng Hock Tay,et al. Support vector machine with adaptive parameters in financial time series forecasting , 2003, IEEE Trans. Neural Networks.
[56] L. Bauwens,et al. Multivariate GARCH Models: A Survey , 2003 .
[57] Arthur E. Hoerl,et al. Ridge Regression: Biased Estimation for Nonorthogonal Problems , 2000, Technometrics.
[58] Peter F. Christoffersen. Evaluating Interval Forecasts , 1998 .
[59] Yoshua Bengio,et al. Convolutional networks for images, speech, and time series , 1998 .
[60] Paul H. Kupiec,et al. Techniques for Verifying the Accuracy of Risk Measurement Models , 1995 .
[61] Kishan G. Mehrotra,et al. Forecasting the behavior of multivariate time series using neural networks , 1992, Neural Networks.
[62] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.