Contact and support
Need help, have a question, or want to contact the ResearchHub team?
© 2026 ResearchHub. Built for responsible scholarly connection.
Research graph
References from Prediction of office building electricity demand using artificial neural network by splitting the time horizon for different occupancy rates. Local targets link to admitted publications; unresolved targets remain external evidence.
Calibration of building energy models for retrofit analysis under uncertainty
10.1016/j.enbuild.2011.12.029 · 2012 · External reference
Design and analysis of electrical energy storage demonstration projects on UK distribution networks
10.1016/j.apenergy.2014.09.027 · 2015 · External reference
Operational supply and demand optimisation of a multi-vector district energy system using artificial neural networks and a genetic algorithm
10.1016/j.apenergy.2018.11.001 · 2019 · External reference
A review on the prediction of building energy consumption
10.1016/j.rser.2012.02.049 · 2012 · External reference
Comparative study of data driven methods in building electricity use prediction
10.1016/j.enbuild.2019.04.029 · 2019 · External reference
An occupancy-based model for building electricity consumption prediction: a case study of three campus buildings in Tianjin
10.1016/j.enbuild.2019.109412 · 2019 · External reference
Heating and cooling building energy demand evaluation; a simplified model and a modified degree days approach
10.1016/j.apenergy.2014.04.067 · 2014 · External reference
District heating network demand prediction using a physics-based energy model with a Bayesian approach for parameter calibration
10.3390/en12183408 · 2019 · External reference
Prediction of optimum heating timing based on artificial neural network by utilizing BEMS data
10.1016/j.jobe.2018.11.012 · 2019 · External reference
10.1016/j.egyai.2020.100028 · 2020 · Admitted local publication
Load prediction method for heat and electricity demand in buildings for the purpose of planning for mixed energy distribution systems
10.1016/j.enbuild.2007.10.014 · 2008 · External reference
Unresolved reference
2013 · External reference
A review on applications of ANN and SVM for building electrical energy consumption forecasting
10.1016/j.rser.2014.01.069 · 2014 · External reference
Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks
10.1016/j.apenergy.2019.02.056 · 2019 · External reference
A bottom-up stochastic model to predict building occupants’ time-dependent activities
10.1016/j.buildenv.2012.10.021 · 2013 · External reference
Building-level occupancy data to improve ARIMA-based electricity use forecasts
2010 · External reference
10.1016/j.egyai.2020.100015 · 2020 · Admitted local publication
Development of a consecutive occupancy estimation framework for improving the energy demand prediction performance of building energy modeling tools
10.3390/en12030433 · 2019 · External reference
Predictive artificial neural network models to forecast the seasonal hourly electricity consumption for a University campus
10.1016/j.scs.2018.06.019 · 2018 · External reference
Feasibility study on a novel methodology for short-term real-time energy demand prediction using weather forecasting data
10.1016/j.enbuild.2012.10.041 · 2013 · External reference
Prediction of building electricity usage using Gaussian process regression
2020 · External reference
Predicting the energy consumption of residential buildings for regional electricity supply-side and demand-side management
10.1109/access.2019.2901257 · 2019 · External reference
A review of data-driven building energy consumption prediction studies
10.1016/j.rser.2017.04.095 · 2018 · External reference
Forecasting electric energy consumption using neural networks
10.1016/0301-4215(95)00116-6 · 1995 · External reference
Short-term load forecasting for non-residential buildings contrasting artificial occupancy attributes
10.1016/j.enbuild.2016.08.081 · 2016 · External reference
Deep learning versus traditional machine learning methods for aggregated energy demand prediction
2017 · External reference
Energy demand prediction through novel random neural network predictor for large non-domestic buildings
2017 · External reference
Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks
10.1016/j.apenergy.2017.12.051 · 2018 · External reference