Related references
Note: Only part of the references are listed.A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting
Mohamed Massaoudi et al.
ENERGY (2021)
Comparison of time-frequency-analysis techniques applied in building energy data noise cancellation for building load forecasting: A real-building case study
Liang Zhang et al.
ENERGY AND BUILDINGS (2021)
Parallel computing and swarm intelligence based artificial intelligence model for multi-step-ahead hydrological time series prediction
Wen-jing Niu et al.
SUSTAINABLE CITIES AND SOCIETY (2021)
Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features
Lechen Li et al.
APPLIED ENERGY (2021)
Evolutionary artificial intelligence model via cooperation search algorithm and extreme learning machine for multiple scales nonstationary hydrological time series prediction
Zhong-kai Feng et al.
JOURNAL OF HYDROLOGY (2021)
A novel dynamic integration approach for multiple load forecasts based on Q-learning algorithm
Minhua Ma et al.
INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS (2020)
A gradient boosting decision tree based GPS signal reception classification algorithm
Rui Sun et al.
APPLIED SOFT COMPUTING (2020)
Load demand forecasting of residential buildings using a deep learning model
Lulu Wen et al.
ELECTRIC POWER SYSTEMS RESEARCH (2020)
Study on deep reinforcement learning techniques for building energy consumption forecasting
Tao Liu et al.
ENERGY AND BUILDINGS (2020)
Continuous control with Stacked Deep Dynamic Recurrent Reinforcement Learning for portfolio optimization
Amine Mohamed Aboussalah et al.
EXPERT SYSTEMS WITH APPLICATIONS (2020)
Reinforced Deterministic and Probabilistic Load Forecasting via Q-Learning Dynamic Model Selection
Cong Feng et al.
IEEE TRANSACTIONS ON SMART GRID (2020)
Load prediction in short-term implementing the multivariate quantile regression
Yazhou Xing et al.
ENERGY (2020)
Load forecasting based on grasshopper optimization and a multilayer feed-forward neural network using regressive approach
M. Talaat et al.
ENERGY (2020)
Using deep learning for short-term load forecasting
Nadjib Mohamed Mehdi Bendaoud et al.
NEURAL COMPUTING & APPLICATIONS (2020)
Modified deep learning and reinforcement learning for an incentive-based demand response model
Lulu Wen et al.
ENERGY (2020)
Detecting botnet by using particle swarm optimization algorithm based on voting system
Mehdi Asadi et al.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE (2020)
A Deep-Reinforcement-Learning-Based Recommender System for Occupant-Driven Energy Optimization in Commercial Buildings
Peter Wei et al.
IEEE INTERNET OF THINGS JOURNAL (2020)
A hybrid scheme-based one-vs-all decision trees for multi-class classification tasks
Jianjian Yan et al.
KNOWLEDGE-BASED SYSTEMS (2020)
Assessment of stacked unidirectional and bidirectional long short-term memory networks for electricity load forecasting
Sara Atef et al.
ELECTRIC POWER SYSTEMS RESEARCH (2020)
An intelligent financial portfolio trading strategy using deep Q-learning
Hyungjun Park et al.
EXPERT SYSTEMS WITH APPLICATIONS (2020)
Actor-critic learning for optimal building energy management with phase change materials
Zahra Rahimpour et al.
ELECTRIC POWER SYSTEMS RESEARCH (2020)
Forecasting Stock Prices Using a Hybrid Deep Learning Model Integrating Attention Mechanism, Multi-Layer Perceptron, and Bidirectional Long-Short Term Memory Neural Network
Qian Chen et al.
IEEE ACCESS (2020)
On-Line Building Energy Optimization Using Deep Reinforcement Learning
Elena Mocanu et al.
IEEE TRANSACTIONS ON SMART GRID (2019)
Short-term electricity load forecasting based on feature selection and Least Squares Support Vector Machines
Ailing Yang et al.
KNOWLEDGE-BASED SYSTEMS (2019)
A hybrid method based on neural network and improved environmental adaptation method using Controlled Gaussian Mutation with real parameter for short-term load forecasting
Priyanka Singh et al.
ENERGY (2019)
Multifactor spatio-temporal correlation model based on a combination of convolutional neural network and long short-term memory neural network for wind speed forecasting
Yong Chen et al.
ENERGY CONVERSION AND MANAGEMENT (2019)
Predicting residential energy consumption using CNN-LSTM neural networks
Tae-Young Kim et al.
ENERGY (2019)
PwAdaBoost: Possible world based AdaBoost algorithm for classifying uncertain data
Han Liu et al.
KNOWLEDGE-BASED SYSTEMS (2019)
Model-Free Real-Time EV Charging Scheduling Based on Deep Reinforcement Learning
Zhiqiang Wan et al.
IEEE TRANSACTIONS ON SMART GRID (2019)
Comparison of three short-term load forecast models in Southern California
Ning Zhang et al.
ENERGY (2019)
A Resampling Based Grid Search Method to Improve Reliability and Robustness of Mixture-Item Response Theory Models of Multimorbid High-Risk Patients
Adam J. Batten et al.
IEEE Journal of Biomedical and Health Informatics (2019)
EmoWare: A Context-Aware Framework for Personalized Video Recommendation Using Affective Video Sequences
Abhishek Tripathi et al.
IEEE ACCESS (2019)
Deep Learning for Household Load Forecasting-A Novel Pooling Deep RNN
Heng Shi et al.
IEEE TRANSACTIONS ON SMART GRID (2018)
Data-driven predictive control of molten iron quality in blast furnace ironmaking using multi-output LS-SVR based inverse system identification
Ping Zhou et al.
NEUROCOMPUTING (2018)
Review on probabilistic forecasting of photovoltaic power production and electricity consumption
D. W. van der Meer et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2018)
Deep Neural Network Based Demand Side Short Term Load Forecasting
Seunghyoung Ryu et al.
ENERGIES (2017)
Convolutional Neural Network approaches to granite tiles classification
Anselmo Ferreira et al.
EXPERT SYSTEMS WITH APPLICATIONS (2017)
A review and analysis of regression and machine learning models on commercial building electricity load forecasting
B. Yildiz et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2017)
Recognizing human activities from smartphone sensors using hierarchical continuous hidden Markov models
Charissa Ann Ronao et al.
INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS (2017)
Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings
Yongbao Chen et al.
APPLIED ENERGY (2017)
Reinforcement learning approach for train rescheduling on a single-track railway
D. Semrov et al.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL (2016)
Deep learning for estimating building energy consumption
Elena Mocanu et al.
SUSTAINABLE ENERGY GRIDS & NETWORKS (2016)
Hybrid methodologies for electricity load forecasting: Entropy-based feature selection with machine learning and soft computing techniques
Sergio Jurado et al.
ENERGY (2015)
Modeling and forecasting of cooling and electricity load demand
A. Vaghefi et al.
APPLIED ENERGY (2014)
Short-Term Load Forecasting: Similar Day-Based Wavelet Neural Networks
Ying Chen et al.
IEEE TRANSACTIONS ON POWER SYSTEMS (2010)
On forecasting daily stock volatility: The role of intraday information and market conditions
Ana-Maria Fuertes et al.
INTERNATIONAL JOURNAL OF FORECASTING (2009)
Expected value, reward outcome, and temporal difference error representations in a probabilistic decision task
Edmund T. Rolls et al.
CEREBRAL CORTEX (2008)
Share Paper
