Related references
Note: Only part of the references are listed.National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?
Juyong Lee et al.
ENERGY (2022)
Modeling and optimization of semi-continuous anaerobic co-digestion of activated sludge and wheat straw using Nonlinear Autoregressive Exogenous neural network and seagull algorithm
Mahmoud M. Abdel Daiem et al.
ENERGY (2022)
Prediction of biodiesel production from microalgal oil using Bayesian optimization algorithm-based machine learning approaches
Nahid Sultana et al.
FUEL (2022)
Hybrid intelligence modeling for estimating shear strength of FRP reinforced concrete members
Md Shah Alam et al.
NEURAL COMPUTING & APPLICATIONS (2022)
Soft-computing modeling and multiresponse optimization for nutrient removal process from municipal wastewater using microalgae
S. M. Zakir Hossain et al.
JOURNAL OF WATER PROCESS ENGINEERING (2022)
Predicting sun protection measures against skin diseases using machine learning approaches
Nahid Sultana
JOURNAL OF COSMETIC DERMATOLOGY (2022)
Application of an automated machine learning-genetic algorithm (AutoML-GA) coupled with computational fluid dynamics simulations for rapid engine design optimization
Opeoluwa Owoyele et al.
INTERNATIONAL JOURNAL OF ENGINE RESEARCH (2022)
Analysis of time series models for Brazilian electricity demand forecasting
Carlos E. Velasquez et al.
ENERGY (2022)
Modeling and multi-objective optimization of microalgae biomass production and CO2 biofixation using hybrid intelligence approaches
S. M. Zakir Hossain et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2022)
Knowledge Mapping in Electricity Demand Forecasting: A Scientometric Insight
Dongchuan Yang et al.
FRONTIERS IN ENERGY RESEARCH (2021)
Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis
Umar Javed et al.
ENERGIES (2021)
Deep learning in power systems research: A review
Mandi Khodayar et al.
CSEE JOURNAL OF POWER AND ENERGY SYSTEMS (2021)
Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX)
Andreas Wunsch et al.
HYDROLOGY AND EARTH SYSTEM SCIENCES (2021)
Electrical Energy Demand Forecasting Model Development and Evaluation with Maximum Overlap Discrete Wavelet Transform-Online Sequential Extreme Learning Machines Algorithms
Mohanad S. Al-Musaylh et al.
ENERGIES (2020)
Short-Term Electricity Demand Forecasting: Impact Analysis of Temperature for Thailand
Kamal Chapagain et al.
ENERGIES (2020)
A Nonlinear Autoregressive Modeling Approach for Forecasting Groundwater Level Fluctuation in Urban Aquifers
Abdullah A. Alsumaiei
WATER (2020)
Forecasting electricity consumption based on machine learning to improve performance: A case study for the organization of petroleum exporting countries (OPEC)
Abdullah Khan et al.
COMPUTERS & ELECTRICAL ENGINEERING (2020)
Time Series Forecasting with Multi-Headed Attention-Based Deep Learning for Residential Energy Consumption
Seok-Jun Bu et al.
ENERGIES (2020)
Hourly electricity demand forecasting using Fourier analysis with feedback
Ergun Yukseltan et al.
ENERGY STRATEGY REVIEWS (2020)
Modeling and Forecasting Medium-Term Electricity Consumption Using Component Estimation Technique
Ismail Shah et al.
FORECASTING (2020)
Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques
Mengmeng Cai et al.
APPLIED ENERGY (2019)
Short-Term Electricity Demand Forecasting Using Components Estimation Technique
Ismail Shah et al.
ENERGIES (2019)
Short-term electricity demand forecasting using machine learning methods enriched with ground-based climate and ECMWF Reanalysis atmospheric predictors in southeast Queensland, Australia
Mohanad S. AL-Musaylh et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2019)
Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia
Mohanad S. Al-Musaylh et al.
ADVANCED ENGINEERING INFORMATICS (2018)
Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches
Salah Bouktif et al.
ENERGIES (2018)
A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation
Zina Boussaada et al.
ENERGIES (2018)
Forecasting groundwater levels using nonlinear autoregressive networks with exogenous input (NARX)
Andreas Wunsch et al.
JOURNAL OF HYDROLOGY (2018)
Data-driven heating and cooling load predictions for non-residential buildings based on support vector machine regression and NARX Recurrent Neural Network: A comparative study on district scale
D. Koschwitz et al.
ENERGY (2018)
Empirical Mode Decomposition Based Deep Learning for Electricity Demand Forecasting
Jatin Bedi et al.
IEEE ACCESS (2018)
Neural network approach with teaching-learning-based optimization for modeling and forecasting long-term electric energy demand in Turkey
Murat Kankal et al.
NEURAL COMPUTING & APPLICATIONS (2017)
Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Vector Inputs
Jaime Buitrago et al.
ENERGIES (2017)
An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan
Syed Aziz Ur Rehman et al.
ENERGIES (2017)
Short term electricity price forecast based on environmentally adapted generalized neuron
Nitin Singh et al.
ENERGY (2017)
The Use of NARX Neural Networks to Forecast Daily Groundwater Levels
Sandra M. Guzman et al.
WATER RESOURCES MANAGEMENT (2017)
Electrical load forecasting models: A critical systematic review
Corentin Kuster et al.
SUSTAINABLE CITIES AND SOCIETY (2017)
Long-term electric energy consumption forecasting via artificial cooperative search algorithm
S. Hr. Aghay Kaboli et al.
ENERGY (2016)
Taking the Human Out of the Loop: A Review of Bayesian Optimization
Bobak Shahriari et al.
PROCEEDINGS OF THE IEEE (2016)
A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings
Muhammad Qamar Raza et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2015)
Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms
Zhongyi Hu et al.
SCIENTIFIC WORLD JOURNAL (2013)
Residential past and future energy consumption: Potential savings and environmental impact
A. Al-Ghandoor et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2009)
Electric load forecasting: literature survey and classification of methods
HK Alfares et al.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE (2002)