Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting

Deep sequence to sequence Bi-LSTM neural networks for day-ahead peak load forecasting

(2021) Neelam Mughees et al.   EXPERT SYSTEMS WITH APPLICATIONS

Support vector regression with asymmetric loss for optimal electric load forecasting

(2021) Jinran Wu et al.   ENERGY

Equipping Seasonal Exponential Smoothing Models with Particle Swarm Optimization Algorithm for Electricity Consumption Forecasting

(2021) Changrui Deng et al.   Energies

Assessment of Model-Based peak electric consumption prediction for commercial buildings

(2021) Jessica Granderson et al.   ENERGY AND BUILDINGS

Research and application of a hybrid model for mid-term power demand forecasting based on secondary decomposition and interval optimization

(2021) Dongxiao Niu et al.   ENERGY

Designing a short-term load forecasting model in the urban smart grid system

(2020) Chen Li   APPLIED ENERGY

Load forecasting based on grasshopper optimization and a multilayer feed-forward neural network using regressive approach

(2020) M. Talaat et al.   ENERGY

A hybrid data mining driven algorithm for long term electric peak load and energy demand forecasting

(2020) Mohammad-Rasool Kazemzadeh et al.   ENERGY

Short-term load forecasting for microgrid energy management system using hybrid HHO-FNN model with best-basis stationary wavelet packet transform

(2020) Usman Bashir Tayab et al.   ENERGY

Novel deep supervised ML models with feature selection approach for large-scale utilities and buildings short and medium-term load requirement forecasts

(2020) Tanveer Ahmad et al.   ENERGY

A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting

(2020) Mohamed Massaoudi et al.   ENERGY

On the long-term density prediction of peak electricity load with demand side management in buildings

(2020) Youngchan Jang et al.   ENERGY AND BUILDINGS

Forecasting household electric appliances consumption and peak demand based on hybrid machine learning approach

(2020) Ejaz Ul Haq et al.   Energy Reports

Short-term electricity load and price forecasting by a new optimal LSTM-NN based prediction algorithm

(2020) Gholamreza Memarzadeh et al.   ELECTRIC POWER SYSTEMS RESEARCH

Feature-fusion-kernel-based Gaussian process model for probabilistic long-term load forecasting

(2020) Yaonan Guan et al.   NEUROCOMPUTING

A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm

(2019) Feifei He et al.   APPLIED ENERGY

Automatic time series analysis for electric load forecasting via support vector regression

(2019) Sebastián Maldonado et al.   APPLIED SOFT COMPUTING

Short-term load forecasting of urban gas using a hybrid model based on improved fruit fly optimization algorithm and support vector machine

(2019) Hongfang Lu et al.   Energy Reports

A novel fuzzy-based ensemble model for load forecasting using hybrid deep neural networks

(2019) George Sideratos et al.   ELECTRIC POWER SYSTEMS RESEARCH

Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia

(2018) Mohanad S. Al-Musaylh et al.   ADVANCED ENGINEERING INFORMATICS

Two-phase particle swarm optimized-support vector regression hybrid model integrated with improved empirical mode decomposition with adaptive noise for multiple-horizon electricity demand forecasting

(2018) Mohanad S. AL-Musaylh et al.   APPLIED ENERGY

Long-term load forecasting: models based on MARS, ANN and LR methods

(2018) Gamze Nalcaci et al.   Central European Journal of Operations Research

Short term electricity load forecasting using a hybrid model

(2018) Jinliang Zhang et al.   ENERGY

Short-Term Load Forecasting by Separating Daily Profiles and Using a Single Fuzzy Model Across the Entire Domain

(2018) Gregor Cerne et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Short-term electricity load forecasting based on feature selection and Least Squares Support Vector Machines

(2018) Ailing Yang et al.   KNOWLEDGE-BASED SYSTEMS

Integrating long-term economic scenarios into peak load forecasting: An application to Spain

(2017) Julián Moral-Carcedo et al.   ENERGY

A self-adaptive evolutionary fuzzy model for load forecasting problems on smart grid environment

(2016) Vitor N. Coelho et al.   APPLIED ENERGY

The comparison of Holt–Winters method and Multiple regression method: A case study

(2016) Liljana Ferbar Tratar et al.   ENERGY

Short-term load forecasting by wavelet transform and evolutionary extreme learning machine

(2015) Song Li et al.   ELECTRIC POWER SYSTEMS RESEARCH

Mid-term interval load forecasting using multi-output support vector regression with a memetic algorithm for feature selection

(2015) Zhongyi Hu et al.   ENERGY

Hybrid filter–wrapper feature selection for short-term load forecasting

(2015) Zhongyi Hu et al.   ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Comprehensive learning particle swarm optimization based memetic algorithm for model selection in short-term load forecasting using support vector regression

(2014) Zhongyi Hu et al.   APPLIED SOFT COMPUTING

A Survey on Electric Power Demand Forecasting: Future Trends in Smart Grids, Microgrids and Smart Buildings

(2014) Luis Hernandez et al.   IEEE Communications Surveys and Tutorials

Interval forecasting of electricity demand: A novel bivariate EMD-based support vector regression modeling framework

(2014) Tao Xiong et al.   INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS

Hybrid PSO–SVM method for short-term load forecasting during periods with significant temperature variations in city of Burbank

(2013) A. Selakov et al.   APPLIED SOFT COMPUTING

A new hybrid day-ahead peak load forecasting method for Iran’s National Grid

(2012) M. Moazzami et al.   APPLIED ENERGY

LIBSVM

(2012) Chih-Chung Chang et al.   ACM Transactions on Intelligent Systems and Technology

Empirical Mode Decomposition for Trivariate Signals

(2009) N. ur Rehman et al.   IEEE TRANSACTIONS ON SIGNAL PROCESSING