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 AI-Assisted approach for building energy and carbon footprint modeling. Local targets link to admitted publications; unresolved targets remain external evidence.
Energy efficiency in buildings business realities and opportunities
2008 · 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
UK office buildings archetypal model as methodological approach in development of regression models for predicting building energy consumption from heating and cooling demands
10.1016/j.enbuild.2012.12.032 · 2013 · External reference
Pattern recognition in building energy performance over time using energy benchmarking data
10.1016/j.apenergy.2018.03.079 · 2018 · External reference
A review on buildings energy consumption information
10.1016/j.enbuild.2007.03.007 · 2008 · External reference
Dynamic occupant density models of commercial buildings for urban energy simulation
10.1016/j.buildenv.2019.106549 · 2020 · External reference
Occupancy-based zone-level VAV system control implications on thermal comfort, ventilation, indoor air quality and building energy efficiency
10.1016/j.enbuild.2019.109473 · 2019 · External reference
Methodology to estimate building energy consumption using energyplus Benchmark Models
10.1016/j.enbuild.2010.07.027 · 2010 · External reference
EnergyStar++: towards more accurate and explanatory building energy benchmarking
10.1016/j.apenergy.2020.115413 · 2020 · External reference
Development of a new energy benchmark for improving the operational rating system of office buildings using various data-mining techniques
10.1016/j.apenergy.2016.04.035 · 2016 · External reference
Methodology of comprehensive building energy performance diagnosis for large commercial buildings at multiple levels
10.1016/j.apenergy.2016.01.054 · 2016 · External reference
An energy performance evaluation methodology for individual office building with dynamic energy benchmarks using limited information
10.1016/j.apenergy.2017.08.153 · 2017 · External reference
Solar radiation forecasting using artificial neural network and random forest methods: application to normal beam, horizontal diffuse and global components
10.1016/j.renene.2018.08.044 · 2019 · External reference
Artificial intelligence and machine learning approaches to energy demand-side response: a systematic review
10.1016/j.rser.2020.109899 · 2020 · External reference
Managing electric flexibility from distributed energy resources: a review of incentives for market design
10.1016/j.rser.2016.06.008 · 2016 · External reference
Short-term load forecasts using LSTM networks
10.1016/j.egypro.2019.01.952 · 2019 · External reference
Machine learning approaches for estimating commercial building energy consumption
10.1016/j.apenergy.2017.09.060 · 2017 · External reference
Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment
10.1016/j.energy.2018.05.169 · 2018 · External reference
Long-term demand forecasting in a scenario of energy transition
10.3390/en12163095 · 2019 · External reference
R, Neural network based optimization approach for energy demand prediction in smart grid
10.1016/j.neucom.2017.08.017 · 2018 · External reference
A high precision artificial neural networks model for short-term energy load forecasting
10.3390/en11010213 · 2018 · External reference
Deployment of data-mining short and medium-term horizon cooling load forecasting models for building energy optimization and management
10.1016/j.ijrefrig.2018.10.017 · 2019 · External reference
A novel multi-time-scale modeling for electric power demand forecasting: from short-term to medium-term horizon
10.1016/j.epsr.2016.08.031 · 2017 · External reference
Unresolved reference
External reference
Unresolved reference
External reference
Energyplus: creating a new-generation building energy simulation program
10.1016/s0378-7788(00)00114-6 · 2001 · External reference
Prediction of building's thermal performance using LSTM and MLP neural networks
10.3390/app10217439 · 2020 · External reference
Long short-term memory
10.1162/neco.1997.9.8.1735 · 1997 · External reference
LSTM based long-term energy consumption prediction with periodicity
10.1016/j.energy.2020.117197 · 2020 · External reference
Unresolved reference
2018 · External reference
A long short-term memory artificial neural network to predict daily HVAC consumption in buildings
10.1016/j.enbuild.2020.109952 · 2020 · External reference
Spatial granularity analysis on electricity consumption prediction using LSTM recurrent neural network
10.1016/j.egypro.2019.02.027 · 2019 · External reference
Optimal deep learning LSTM model for electric load forecasting using feature selection and genetic algorithm: comparison with machine learning approaches
10.3390/en11071636 · 2018 · External reference
Comparative study of machine learning-based multi-objective prediction framework for multiple building energy loads
10.1016/j.scs.2020.102283 · 2020 · External reference
Early predicting cooling loads for energy-efficient design in office buildings by machine learning
10.1016/j.enbuild.2018.10.004 · 2020 · External reference
A fresh look at weather impact on peak electricity demand and energy use of buildings using 30-year actual weather data
10.1016/j.apenergy.2013.05.019 · 2013 · External reference
Optimization of carbon emissions in smart grids
2014 · External reference
Measuring the progress and impacts of decarbonising British electricity
10.1016/j.enpol.2016.12.037 · 2017 · External reference
Prediction and optimization of energy consumption in an office building using artificial neural network and a genetic algorithm
10.1016/j.scs.2020.102325 · 2020 · External reference
Reliability analysis of an energy-based form optimization of office buildings under uncertainties in envelope and occupant parameters
10.1016/j.enbuild.2019.109707 · 2020 · External reference
Using the R-MAPE index as a resistant measure of forecast accuracy
10.7334/psicothema2013.23 · 2013 · External reference