Effective long short-term memory with differential evolution algorithm for electricity price prediction
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[1] Mehdi Khashei,et al. A novel hybridization of artificial neural networks and ARIMA models for time series forecasting , 2011, Appl. Soft Comput..
[2] Jun Wang,et al. Exponent back propagation neural network forecasting for financial cross-correlation relationship , 2016, Expert Syst. Appl..
[3] R. Weron. Electricity price forecasting: A review of the state-of-the-art with a look into the future , 2014 .
[4] Shan Liu,et al. Optimizing the Joint Replenishment and Channel Coordination Problem under Supply Chain Environment Using a Simple and Effective Differential Evolution Algorithm , 2014 .
[5] Yi Zeng,et al. Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network , 2017 .
[6] Zhang Yang,et al. Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods , 2017 .
[7] Florentina Paraschiv,et al. Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks , 2016 .
[8] S. Surender Reddy,et al. Bat algorithm-based back propagation approach for short-term load forecasting considering weather factors , 2018 .
[9] Peter C. Y. Chen,et al. LSTM network: a deep learning approach for short-term traffic forecast , 2017 .
[10] Shan Liu,et al. Effects of process and outcome controls on business process outsourcing performance: Moderating roles of vendor and client capability risks , 2017, Eur. J. Oper. Res..
[11] Mohamed Cheriet,et al. Combination of context-dependent bidirectional long short-term memory classifiers for robust offline handwriting recognition , 2017, Pattern Recognit. Lett..
[12] Rui Liu,et al. An effective and efficient fruit fly optimization algorithm with level probability policy and its applications , 2016, Knowl. Based Syst..
[13] Ertuğrul Çam,et al. Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines , 2015 .
[14] Lin Wang,et al. Modeling and optimization for the joint replenishment and delivery problem with heterogeneous items , 2013, Knowl. Based Syst..
[15] Nitin Singh,et al. Short term electricity price forecast based on environmentally adapted generalized neuron , 2017 .
[16] A. Koch,et al. Composite forecasting approach, application for next-day electricity price forecasting , 2017 .
[17] Tao Chen,et al. Back propagation neural network with adaptive differential evolution algorithm for time series forecasting , 2015, Expert Syst. Appl..
[18] Bernhard Sick,et al. Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).
[19] Jana-Rebecca Rehse,et al. Predicting process behaviour using deep learning , 2016, Decis. Support Syst..
[20] Z. Tan,et al. Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models , 2010 .
[21] Yunpeng Wang,et al. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data , 2015 .
[22] Weiping Zhang,et al. Forecasting of turbine heat rate with online least squares support vector machine based on gravitational search algorithm , 2013, Knowl. Based Syst..
[23] Li Lin,et al. Remaining useful life estimation of engineered systems using vanilla LSTM neural networks , 2018, Neurocomputing.
[24] Kit Po Wong,et al. Electricity Price Forecasting With Extreme Learning Machine and Bootstrapping , 2012, IEEE Transactions on Power Systems.
[25] S. Surender Reddy,et al. Short term electrical load forecasting using back propagation neural networks , 2014, 2014 North American Power Symposium (NAPS).
[26] B. Eswara Reddy,et al. A moving-average filter based hybrid ARIMA-ANN model for forecasting time series data , 2014, Appl. Soft Comput..
[27] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[28] Rui Liu,et al. An improved differential harmony search algorithm for function optimization problems , 2019, Soft Comput..
[29] Haibin Ling,et al. Diagnosing deep learning models for high accuracy age estimation from a single image , 2017, Pattern Recognit..
[30] Christophe Garcia,et al. Contribution of recurrent connectionist language models in improving LSTM-based Arabic text recognition in videos , 2017, Pattern Recognit..
[31] Gang Qu,et al. Modified genetic algorithm-based feature selection combined with pre-trained deep neural network for demand forecasting in outpatient department , 2017, Expert Syst. Appl..
[32] Zhe George Zhang,et al. Forecasting stock indices with back propagation neural network , 2011, Expert Syst. Appl..
[33] Jakub Nowotarski,et al. An empirical comparison of alternate schemes for combining electricity spot price forecasts , 2013 .
[34] Lin Wang,et al. Effective electricity energy consumption forecasting using echo state network improved by differential evolution algorithm , 2018 .
[35] Rui Liu,et al. Optimizing the new coordinated replenishment and delivery model considering quantity discount and resource constraints , 2018, Comput. Ind. Eng..
[36] Guoqiang Han,et al. δ-agree AdaBoost stacked autoencoder for short-term traffic flow forecasting , 2017, Neurocomputing.
[37] Fei Gao,et al. Density prediction and dimensionality reduction of mid-term electricity demand in China: A new semiparametric-based additive model , 2014 .
[38] Qing Cao,et al. Forecasting wind speed with recurrent neural networks , 2012, Eur. J. Oper. Res..
[39] Olivier Grunder,et al. Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm , 2017 .
[40] Margaret Lech,et al. Evaluating deep learning architectures for Speech Emotion Recognition , 2017, Neural Networks.
[41] Marta R. Costa-jussà,et al. Introduction to the special issue on deep learning approaches for machine translation , 2017, Comput. Speech Lang..
[42] Kit Po Wong,et al. A Hybrid Approach for Probabilistic Forecasting of Electricity Price , 2014, IEEE Transactions on Smart Grid.
[43] Xing Yan,et al. Mid-term electricity market clearing price forecasting: A hybrid LSSVM and ARMAX approach , 2013 .
[44] Guoqiang Peter Zhang,et al. Time series forecasting using a hybrid ARIMA and neural network model , 2003, Neurocomputing.
[45] Mohammad Moradzadeh,et al. Simultaneous day-ahead forecasting of electricity price and load in smart grids , 2015 .
[46] Xiaobo Zhang,et al. Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by Cuckoo search algorithm , 2017 .
[47] Fan Zhang,et al. Time series forecasting for building energy consumption using weighted Support Vector Regression with differential evolution optimization technique , 2016 .
[48] Shan Liu,et al. Optimal Forecast Combination Based on Neural Networks for Time Series Forecasting , 2018, Appl. Soft Comput..
[49] Grzegorz Dudek,et al. Multilayer perceptron for GEFCom2014 probabilistic electricity price forecasting , 2016 .
[50] Jia Liu,et al. Maxout neurons for deep convolutional and LSTM neural networks in speech recognition , 2016, Speech Commun..
[51] Ioannis P. Panapakidis,et al. Day-ahead electricity price forecasting via the application of artificial neural network based models , 2016 .
[52] Truyen Tran,et al. Predicting healthcare trajectories from medical records: A deep learning approach , 2017, J. Biomed. Informatics.
[53] Guohua Cao,et al. Support vector regression with fruit fly optimization algorithm for seasonal electricity consumption forecasting , 2016 .
[54] Rosa Espínola,et al. The effect of wind generation and weekday on Spanish electricity spot price forecasting , 2011 .
[55] Rafał Weron,et al. On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks , 2019, International Journal of Forecasting.
[56] Kevin M. Smith,et al. Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy , 2014 .
[57] Jing Zhao,et al. Using a Grey model optimized by Differential Evolution algorithm to forecast the per capita annual net income of rural households in China , 2012 .
[58] Jingjing Xie,et al. Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions , 2016 .
[59] Jun Wang,et al. Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations , 2016 .
[60] Joao P. S. Catalao,et al. Electricity prices forecasting by a hybrid evolutionary-adaptive methodology , 2014 .
[61] Ali Deihimi,et al. Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction , 2013 .
[62] Rainer Storn,et al. Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..