Related references
Note: Only part of the references are listed.Occupancy data at different spatial resolutions: Building energy performance and model calibration
Adrian Chong et al.
APPLIED ENERGY (2021)
Accuracy of different machine learning algorithms and added-value of predicting aggregated-level energy performance of commercial buildings
Shalika Walker et al.
ENERGY AND BUILDINGS (2020)
Building thermal load prediction through shallow machine learning and deep learning
Zhe Wang et al.
APPLIED ENERGY (2020)
Prediction of energy consumption in hotel buildings via support vector machines
Minglei Shao et al.
SUSTAINABLE CITIES AND SOCIETY (2020)
Using long short-term memory networks to predict energy consumption of air-conditioning systems
Chonggang Zhou et al.
SUSTAINABLE CITIES AND SOCIETY (2020)
Hybrid short-term forecasting of the electric demand of supply fans using machine learning
Jason Runge et al.
JOURNAL OF BUILDING ENGINEERING (2020)
An uncertainty-aware deep reinforcement learning framework for residential air conditioning energy management
Clement Lork et al.
APPLIED ENERGY (2020)
Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons
Zhihong Pang et al.
RENEWABLE ENERGY (2020)
Heat load prediction of residential buildings based on discrete wavelet transform and tree-based ensemble learning
Mingju Gong et al.
JOURNAL OF BUILDING ENGINEERING (2020)
Probabilistic individual load forecasting using pinball loss guided LSTM
Yi Wang et al.
APPLIED ENERGY (2019)
Probabilistic load forecasting for buildings considering weather forecasting uncertainty and uncertain peak load
Lei Xu et al.
APPLIED ENERGY (2019)
Assessment of deep recurrent neural network-based strategies for short-term building energy predictions
Cheng Fan et al.
APPLIED ENERGY (2019)
Improving prediction performance for indoor temperature in public buildings based on a novel deep learning method
Chengliang Xu et al.
BUILDING AND ENVIRONMENT (2019)
Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks
Aowabin Rahman et al.
APPLIED ENERGY (2018)
Probabilistic Load Forecasting Using an Improved Wavelet Neural Network Trained by Generalized Extreme Learning Machine
Mehdi Rafiei et al.
IEEE TRANSACTIONS ON SMART GRID (2018)
Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings
Young Tae Chae et al.
ENERGY AND BUILDINGS (2016)
Gray-box modeling and validation of residential HVAC system for control system design
Abdul Afram et al.
APPLIED ENERGY (2015)
Uncertainties in energy consumption introduced by building operations and weather for a medium-size office building
Liping Wang et al.
ENERGY AND BUILDINGS (2012)
Prediction of daily global solar irradiation data using Bayesian neural network: A comparative study
R. Yacef et al.
RENEWABLE ENERGY (2012)
Aleatory or epistemic? Does it matter?
Armen Der Kiureghian et al.
STRUCTURAL SAFETY (2009)
Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks
Geoffrey K. F. Tso et al.
ENERGY (2007)