Electricity consumption forecasting with outliers handling based on clustering and deep learning with application to the Algerian market
暂无分享,去创建一个
[1] J. F. Torres,et al. Electricity consumption forecasting based on ensemble deep learning with application to the algerian market , 2021, Energy.
[2] Petr Musilek,et al. Federated Learning with Hyperparameter-based Clustering for Electrical Load Forecasting , 2021, Internet Things.
[3] Chao Liu,et al. Regression modeling for enterprise electricity consumption: A comparison of recurrent neural network and its variants , 2021 .
[4] Francisco Martínez-Álvarez,et al. Deep Learning for Time Series Forecasting: A Survey , 2020, Big Data.
[5] José Cristóbal Riquelme Santos,et al. An Experimental Review on Deep Learning Architectures for Time Series Forecasting , 2020, Int. J. Neural Syst..
[6] Federico Divina,et al. Hybridizing Deep Learning and Neuroevolution: Application to the Spanish Short-Term Electric Energy Consumption Forecasting , 2020, Applied Sciences.
[7] Yongjae Lee,et al. Basic Enhancement Strategies When Using Bayesian Optimization for Hyperparameter Tuning of Deep Neural Networks , 2020, IEEE Access.
[8] José C. Riquelme,et al. Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting , 2020, Applied Sciences.
[9] Jose A. Lozano,et al. A Review on Outlier/Anomaly Detection in Time Series Data , 2020, ACM Comput. Surv..
[10] Abhiram Mullapudi,et al. rrcf: Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams , 2019, J. Open Source Softw..
[11] Hang Lei,et al. Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimization , 2019 .
[12] Yuan Zhang,et al. Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network , 2019, IEEE Transactions on Smart Grid.
[13] Nakyoung Kim,et al. LSTM Based Short-term Electricity Consumption Forecast with Daily Load Profile Sequences , 2018, 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE).
[14] Alicia Troncoso Lora,et al. A scalable approach based on deep learning for big data time series forecasting , 2018, Integr. Comput. Aided Eng..
[15] Amir Mosavi,et al. A Hybrid clustering and classification technique for forecasting short‐term energy consumption , 2018, Environmental Progress & Sustainable Energy.
[16] Alicia Troncoso Lora,et al. SmartFD: A Real Big Data Application for Electrical Fraud Detection , 2018, HAIS.
[17] Francisco Martínez-Álvarez,et al. Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities , 2018 .
[18] Caston Sigauke,et al. Regression-SARIMA modelling of daily peak electricity demand in South Africa , 2017 .
[19] Yan Quan Liu,et al. Forecasting of Chinese Primary Energy Consumption in 2021 with GRU Artificial Neural Network , 2017 .
[20] Chandra Sekhar,et al. k-Shape clustering algorithm for building energy usage patterns analysis and forecasting model accuracy improvement , 2017 .
[21] Heeyoung Kim,et al. A new metric of absolute percentage error for intermittent demand forecasts , 2016 .
[22] Francisco Martínez-Álvarez,et al. A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting , 2015 .
[23] Yoshua Bengio,et al. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.
[24] Charu C. Aggarwal,et al. Outlier Detection for Temporal Data: A Survey , 2014, IEEE Transactions on Knowledge and Data Engineering.
[25] Alicia Troncoso Lora,et al. Discovery of motifs to forecast outlier occurrence in time series , 2011, Pattern Recognit. Lett..
[26] Anselmo Cardoso de Paiva,et al. Forecasting of individual electricity consumption using Optimized Gradient Boosting Regression with Modified Particle Swarm Optimization , 2021, Eng. Appl. Artif. Intell..
[27] Marc Rußwurm,et al. Tslearn, A Machine Learning Toolkit for Time Series Data , 2020, J. Mach. Learn. Res..
[28] Peter Laurinec. Improving Forecasting Accuracy Through the Influence of Time Series Representations and Clustering , 2018 .
[29] Su Wutyi Hnin,et al. Time Series Outlier Detection for Short-Term Electricity Load Demand Forecasting , 2018 .
[30] Marimuthu Palaniswami,et al. Improving load forecasting based on deep learning and K-shape clustering , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).
[31] Luis Gravano,et al. k-Shape: Efficient and Accurate Clustering of Time Series , 2016, SGMD.
[32] J. J. Montaño Moreno,et al. Using the R-MAPE index as a resistant measure of forecast accuracy. , 2013, Psicothema.
[33] Lee-Ing Tong,et al. Forecasting energy consumption using a grey model improved by incorporating genetic programming , 2011 .
[34] S. I. : EFFECTIVE AND EFFICIENT DEEP LEARNING A deep LSTM network for the Spanish electricity consumption forecasting , 2022 .