Related references
Note: Only part of the references are listed.A novel short-term load forecasting framework based on time-series clustering and early classification algorithm
Zhe Chen et al.
ENERGY AND BUILDINGS (2021)
Contribution of households' occupant profile in predictions of energy consumption in residential buildings: A statistical approach from Mediterranean survey data
Marta Braulio-Gonzalo et al.
ENERGY AND BUILDINGS (2021)
Day-ahead short-term load probability density forecasting method with a decomposition-based quantile regression forest
Feifei He et al.
APPLIED ENERGY (2020)
Short-term load forecast using ensemble neuro-fuzzy model
M. Malekizadeh 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)
Combination of short-term load forecasting models based on a stacking ensemble approach
Jihoon Moon et al.
ENERGY AND BUILDINGS (2020)
Prediction of load-displacement curve in a complex structure using artificial neural networks: A study on a long bone
Hadi Rahmanpanah et al.
INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE (2020)
PSO-GA based hybrid with Adam Optimization for ANN training with application in Medical Diagnosis
Rajesh K. Yadav et al.
COGNITIVE SYSTEMS RESEARCH (2020)
SVR-FFS: A novel forward feature selection approach for high-frequency time series forecasting using support vector regression
Jose Manuel Valente et al.
EXPERT SYSTEMS WITH APPLICATIONS (2020)
Short-Term Load Forecasting of Microgrid Based on Chaotic Particle Swarm Optimization
Han Ma et al.
PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON MECHATRONICS AND INTELLIGENT ROBOTICS (ICMIR-2019) (2020)
Short-term electricity load forecasting based on feature selection and Least Squares Support Vector Machines
Ailing Yang et al.
KNOWLEDGE-BASED SYSTEMS (2019)
Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm-A case study of papermaking process
Yusha Hu et al.
ENERGY (2019)
Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series
Hossein Javedani Sadaei et al.
ENERGY (2019)
A simplified HVAC energy prediction method based on degree-day
Huajing Sha et al.
SUSTAINABLE CITIES AND SOCIETY (2019)
Deep ensemble learning based probabilistic load forecasting in smart grids
Yandong Yang et al.
ENERGY (2019)
Short-Term Load Forecasts Using LSTM Networks
Shahzad Muzaffar et al.
INNOVATIVE SOLUTIONS FOR ENERGY TRANSITIONS (2019)
A Hybrid LSTM Neural Network for Energy Consumption Forecasting of Individual Households
Ke Yan et al.
IEEE ACCESS (2019)
Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system
Guoyin Fu
ENERGY (2018)
A switching delayed PSO optimized extreme learning machine for short-term load forecasting
Nianyin Zeng et al.
NEUROCOMPUTING (2017)
Hybrid evolutionary algorithms in a SVR-based electric load forecasting model
Wei-Chiang Hong
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2009)