On the public citation graph
Identifying key variables and interactions in statistical models of building energy consumption using regularization
0 signed · 154 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/0471667196openalex-snapshot
Encyclopedia of Statistical Sciences
inferredcites →https://doi.org/10.1007/978-0-387-84858-7openalex-snapshot
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
inferredcites →https://doi.org/10.1007/s12053-011-9116-8openalex-snapshot
Building commissioning: a golden opportunity for reducing energy costs and greenhouse gas emissions in the United States
inferredcites →https://doi.org/10.1016/0378-7788(94)90005-1openalex-snapshot
Two-to-one discrepancy between measured and predicted performance of a ‘low-energy’ office building: insights from a reconciliation based on the DOE-2 model
inferredcites →https://doi.org/10.1016/j.apenergy.2010.11.022openalex-snapshot
Review of building energy-use performance benchmarking methodologies
inferredcites →https://doi.org/10.1016/j.apenergy.2012.10.031openalex-snapshot
Calibration and uncertainty analysis for computer models – A meta-model based approach for integrated building energy simulation
inferredcites →https://doi.org/10.1016/j.apenergy.2013.10.062openalex-snapshot
Thermal comfort and building energy consumption implications – A review
inferredcites →https://doi.org/10.1016/j.apenergy.2014.02.057openalex-snapshot
Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy
inferredcites →https://doi.org/10.1016/j.apenergy.2014.04.016openalex-snapshot
Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques
inferredcites →https://doi.org/10.1016/j.autcon.2014.02.009openalex-snapshot
The gap between predicted and measured energy performance of buildings: A framework for investigation
inferredcites →https://doi.org/10.1016/j.buildenv.2006.10.027openalex-snapshot
Contrasting the capabilities of building energy performance simulation programs
inferredcites →https://doi.org/10.1016/j.buildenv.2010.01.021openalex-snapshot
A review of bottom-up building stock models for energy consumption in the residential sector
inferredcites →https://doi.org/10.1016/j.buildenv.2011.08.016openalex-snapshot
Handling uncertainty in housing stock models
inferredcites →https://doi.org/10.1016/j.buildenv.2011.09.024openalex-snapshot
A decoupled whole-building simulation engine for rapid exhaustive search of low-carbon and low-energy building refurbishment options
inferredcites →https://doi.org/10.1016/j.buildenv.2012.02.003openalex-snapshot
A framework for simulation-based real-time whole building performance assessment
inferredcites →https://doi.org/10.1016/j.compbiolchem.2010.07.002openalex-snapshot
Stable feature selection for biomarker discovery
inferredcites →https://doi.org/10.1016/j.enbuild.2007.03.007openalex-snapshot
A review on buildings energy consumption information
inferredcites →https://doi.org/10.1016/j.enbuild.2008.07.012openalex-snapshot
Building energy parameter investigations based on multivariate analysis
inferredcites →https://doi.org/10.1016/j.enbuild.2011.09.039openalex-snapshot
Calibrating whole building energy models: Detailed case study using hourly measured data
inferredcites →https://doi.org/10.1016/j.enbuild.2011.10.031openalex-snapshot
Identifying important variables of energy use in low energy office building by using multivariate analysis
inferredcites →https://doi.org/10.1016/j.enbuild.2011.12.020openalex-snapshot
Validation of building energy modeling tools under idealized and realistic conditions
inferredcites →https://doi.org/10.1016/j.enbuild.2011.12.029openalex-snapshot
Calibration of building energy models for retrofit analysis under uncertainty
inferredcites →https://doi.org/10.1016/j.enbuild.2012.09.005openalex-snapshot
Energy intelligent buildings based on user activity: A survey
inferredcites →https://doi.org/10.1016/j.enbuild.2012.10.027openalex-snapshot
Optimum building energy retrofits under technical and economic uncertainty
inferredcites →https://doi.org/10.1016/j.enbuild.2013.02.050openalex-snapshot
Development of an RDP neural network for building energy consumption fault detection and diagnosis
inferredcites →https://doi.org/10.1016/j.enbuild.2014.04.017openalex-snapshot
Big meter data analysis of the energy efficiency potential in Stockholm's building stock
inferredcites →https://doi.org/10.1016/j.enpol.2013.08.058openalex-snapshot
Building communities and social potential: Between and beyond organizations and individuals in commercial properties
inferredcites →https://doi.org/10.1016/j.enpol.2013.12.031openalex-snapshot
A comprehensive framework to quantify energy savings potential from improved operations of commercial building stocks
inferredcites →https://doi.org/10.1016/j.erss.2014.02.003openalex-snapshot
What are we doing here? Analyzing fifteen years of energy scholarship and proposing a social science research agenda
inferredcites →https://doi.org/10.1016/j.rser.2008.09.033openalex-snapshot
Modeling of end-use energy consumption in the residential sector: A review of modeling techniques
inferredcites →https://doi.org/10.1016/j.rser.2012.02.049openalex-snapshot
A review on the prediction of building energy consumption
inferredcites →https://doi.org/10.1016/j.rser.2013.03.004openalex-snapshot
State of the art in building modelling and energy performances prediction: A review
inferredcites →https://doi.org/10.1080/09613218.2012.675713openalex-snapshot
Beyond the technical: a snapshot of energy and buildings research
inferredcites →https://doi.org/10.1080/09613218.2014.887612openalex-snapshot
Improving energy benchmarking with self-reported data
inferredcites →https://doi.org/10.1080/09613218.2014.905395openalex-snapshot
Unequal access to energy efficiency in US multifamily rental housing: opportunities to improve
inferredcites →https://doi.org/10.1080/10618600.2014.938812openalex-snapshot
Learning Interactions via Hierarchical Group-Lasso Regularization
inferredcites →https://doi.org/10.1109/tit.2009.2016018openalex-snapshot
Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso)
inferredcites →https://doi.org/10.1111/j.1467-985x.2010.00646_6.xopenalex-snapshot
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
inferredcites →https://doi.org/10.1111/j.1467-9868.2005.00503.xopenalex-snapshot
Regularization and Variable Selection Via the Elastic Net
inferredcites →https://doi.org/10.1111/j.1467-9868.2005.00532.xopenalex-snapshot
Model Selection and Estimation in Regression with Grouped Variables
inferredcites →https://doi.org/10.1111/j.1467-9868.2011.00771.xopenalex-snapshot
Regression Shrinkage and Selection via The Lasso: A Retrospective
inferredcites →https://doi.org/10.1111/j.2517-6161.1996.tb02080.xopenalex-snapshot
Regression Shrinkage and Selection Via the Lasso
inferredcites →https://doi.org/10.1126/science.1100103openalex-snapshot
Stabilization Wedges: Solving the Climate Problem for the Next 50 Years with Current Technologies
inferredcites →https://doi.org/10.1214/07-aos584openalex-snapshot
The composite absolute penalties family for grouped and hierarchical variable selection
inferredcites →https://doi.org/10.1214/08-aos653openalex-snapshot
Near-ideal model selection by ℓ1 minimization
inferredcites →https://doi.org/10.1214/13-aos1096openalex-snapshot
A lasso for hierarchical interactions
inferredcites →https://doi.org/10.1260/1748-3018.6.1.59openalex-snapshot
Feature Selection for Predicting Building Energy Consumption Based on Statistical Learning Method
inferredcites →https://doi.org/10.18637/jss.v033.i01openalex-snapshot
Regularization Paths for Generalized Linear Models via Coordinate Descent
inferredcites →https://doi.org/10.32614/cran.package.glinternetopenalex-snapshot
glinternet: Learning Interactions via Hierarchical Group-Lasso Regularization
inferredcites →https://doi.org/10.32614/r.manualsopenalex-snapshot
R: A Language and Environment for Statistical Computing
inferredcites →https://doi.org/10.48550/arxiv.1001.0736openalex-snapshot
A note on the group lasso and a sparse group lasso
inferredcites →https://doi.org/10.48550/arxiv.1308.2719openalex-snapshot
Learning interactions through hierarchical group-lasso regularization
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4578449openalex-extracted
Open Data Model for Estimating Regional to Town Scale Residential Energy Consumption
inferred← citeshttps://doi.org/10.1002/9781119226444.ch3openalex-snapshot
Multi‐Scale Computing for a Sustainable Built Environment
inferred← citeshttps://doi.org/10.1002/9781394172092.ch2openalex-snapshot
Machine Learning in Urban Building Energy Modeling
inferred← citeshttps://doi.org/10.1002/asmb.2342openalex-snapshot
Integrative interaction analysis using threshold gradient directed regularization
inferred← citeshttps://doi.org/10.1002/bbb.2490openalex-snapshot
Comparative evaluation of bioethanol fermentation process parameters using RSM , ANN and ANFIS
inferred← citeshttps://doi.org/10.1007/978-3-030-02686-8_26openalex-snapshot
Connecting to Smart Cities: Analyzing Energy Times Series to Visualize Monthly Electricity Peak Load in Residential Buildings
inferred← citeshttps://doi.org/10.1007/978-3-030-71454-3_10openalex-snapshot
Impact of Charging Infrastructure Surroundings on Temporal Characteristics of Electric Vehicle Charging Sessions
inferred← citeshttps://doi.org/10.1007/s12053-019-09791-1openalex-snapshot
Daily life and demand: an analysis of intra-day variations in residential electricity consumption with time-use data
inferred← citeshttps://doi.org/10.1007/s12053-024-10264-3openalex-snapshot
Physical, socio-psychological, and behavioural determinants of household energy consumption in the UK
inferred← citeshttps://doi.org/10.1007/s12273-016-0345-9openalex-snapshot
Impacts of building operational schedules and occupants on the lighting energy consumption patterns of an office space
inferred← citeshttps://doi.org/10.1007/s42243-026-01856-7openalex-snapshot
Dimensionality reduction for steel rolling reheating furnace energy consumption prediction: feature importance, model performance, and industrial case studies
inferred← citeshttps://doi.org/10.1016/j.aei.2022.101627openalex-snapshot
A hybrid-model forecasting framework for reducing the building energy performance gap
inferred← citeshttps://doi.org/10.1016/j.apenergy.2014.04.016openalex-snapshot
Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques
inferred← citeshttps://doi.org/10.1016/j.apenergy.2015.08.126openalex-snapshot
Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data
inferred← citeshttps://doi.org/10.1016/j.apenergy.2016.07.087openalex-snapshot
A regression-based approach to estimating retrofit savings using the Building Performance Database
inferred← citeshttps://doi.org/10.1016/j.apenergy.2016.08.079openalex-snapshot
Estimation of the building energy use intensity in the urban scale by integrating GIS and big data technology
inferred← citeshttps://doi.org/10.1016/j.apenergy.2016.08.096openalex-snapshot
Identifying the influential features on the regional energy use intensity of residential buildings based on Random Forests
inferred← citeshttps://doi.org/10.1016/j.apenergy.2017.05.113openalex-snapshot
A spatially-explicit methodological framework based on neural networks to assess the effect of urban form on energy demand
inferred← citeshttps://doi.org/10.1016/j.apenergy.2017.06.105openalex-snapshot
Decomposing core energy factor structure of U.S. residential buildings through principal component analysis with variable clustering on high-dimensional mixed data
inferred← citeshttps://doi.org/10.1016/j.apenergy.2018.10.053openalex-snapshot
Grading buildings on energy performance using city benchmarking data
inferred← citeshttps://doi.org/10.1016/j.apenergy.2019.113391openalex-snapshot
Framework for estimation of the direct rebound effect for residential photovoltaic systems
inferred← citeshttps://doi.org/10.1016/j.apenergy.2019.113550openalex-snapshot
An integrated data-driven framework for urban energy use modeling (UEUM)
inferred← citeshttps://doi.org/10.1016/j.apenergy.2020.116114openalex-snapshot
Orthogonalization and machine learning methods for residential energy estimation with social and economic indicators
inferred← citeshttps://doi.org/10.1016/j.buildenv.2019.106270openalex-snapshot
Urban energy use modeling methods and tools: A review and an outlook
inferred← citeshttps://doi.org/10.1016/j.cities.2017.12.019openalex-snapshot
Understanding the complexities of domestic energy reductions in cities: Integrating data sets generally available in the United Kingdom's local authorities
inferred← citeshttps://doi.org/10.1016/j.cities.2018.12.007openalex-snapshot
A new clustering and visualization method to evaluate urban heat energy planning scenarios
inferred← citeshttps://doi.org/10.1016/j.enbuild.2016.07.018openalex-snapshot
Evaluating energy performance in non-domestic buildings: A review
inferred← citeshttps://doi.org/10.1016/j.enbuild.2016.12.016openalex-snapshot
Input variable selection for thermal load predictive models of commercial buildings
inferred← citeshttps://doi.org/10.1016/j.enbuild.2017.05.013openalex-snapshot
Probabilistic behavioral modeling in building performance simulation: A Monte Carlo approach
inferred← citeshttps://doi.org/10.1016/j.enbuild.2017.12.059openalex-snapshot
Statistical methodology to assess changes in the electrical consumption profile of buildings
inferred← citeshttps://doi.org/10.1016/j.enbuild.2020.110226openalex-snapshot
Comparison of linear and nonlinear statistical models for analyzing determinants of residential energy consumption
inferred← citeshttps://doi.org/10.1016/j.enbuild.2022.111909openalex-snapshot
GEIN: An interpretable benchmarking framework towards all building types based on machine learning
inferred← citeshttps://doi.org/10.1016/j.enbuild.2022.111933openalex-snapshot
Comparison of the applicability of city-level building energy consumption quota methods
inferred← citeshttps://doi.org/10.1016/j.enbuild.2023.112911openalex-snapshot
A review and reflection on open datasets of city-level building energy use and their applications
inferred← citeshttps://doi.org/10.1016/j.enbuild.2026.117033openalex-snapshot
Surrogate model evaluation and building energy benchmarking for commercial buildings
inferred← citeshttps://doi.org/10.1016/j.enbuild.2026.117351openalex-snapshot
Building robustness: measuring climate-responsive performance with information theory
inferred← citeshttps://doi.org/10.1016/j.enbuild.2026.117836openalex-snapshot
Data-driven discovery of sensor relationships for virtual sensing in predictive building operations
inferred← citeshttps://doi.org/10.1016/j.enbuild.2026.117957openalex-snapshot
Towards a data-driven profiles of household energy consumption in collective housing: Machine learning clustering with insights from Bab Ezzouar (Algiers)
inferred← citeshttps://doi.org/10.1016/j.enbuild.2026.118332openalex-snapshot
Architectural style as a rapid proxy for city-scale energy retrofit prioritization
inferred← citeshttps://doi.org/10.1016/j.enconman.2017.10.020openalex-snapshot
Decomposing core energy factor structure of U.S. commercial buildings through clustering around latent variables with Random Forest on large-scale mixed data
inferred← citeshttps://doi.org/10.1016/j.eneco.2020.104942openalex-snapshot
Prioritizing driving factors of household carbon emissions: An application of the LASSO model with survey data
inferred← citeshttps://doi.org/10.1016/j.energy.2016.10.068openalex-snapshot
Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization
inferred← citeshttps://doi.org/10.1016/j.energy.2017.02.131openalex-snapshot
Multi-criteria building energy performance benchmarking through variable clustering based compromise TOPSIS with objective entropy weighting
inferred← citeshttps://doi.org/10.1016/j.energy.2017.10.091openalex-snapshot
Probabilistic energy consumption analysis in buildings using point estimate method
inferred← citeshttps://doi.org/10.1016/j.energy.2019.01.157openalex-snapshot
Investigating structural and occupant drivers of annual residential electricity consumption using regularization in regression models
inferred← citeshttps://doi.org/10.1016/j.energy.2019.04.016openalex-snapshot
Development of Photovoltaic abnormal condition detection system using combined regression and Support Vector Machine
inferred← citeshttps://doi.org/10.1016/j.energy.2021.121634openalex-snapshot
Identification method of market power abuse of generators based on lasso-logit model in spot market
inferred← citeshttps://doi.org/10.1016/j.energy.2021.122720openalex-snapshot
Investigating the effects of key drivers on energy consumption of nonresidential buildings: A data-driven approach integrating regularization and quantile regression
inferred← citeshttps://doi.org/10.1016/j.energy.2024.133819openalex-snapshot
Household, sociodemographic, building and land cover factors affecting residential summer electricity consumption: A systematic statistical study in Phoenix, AZ
inferred← citeshttps://doi.org/10.1016/j.energy.2026.141056openalex-snapshot
A simplified design tool for zero-heating-energy buildings based on parametric modeling in high-solar climates
inferred← citeshttps://doi.org/10.1016/j.enpol.2020.111327openalex-snapshot
Examining the feasibility of using open data to benchmark building energy usage in cities: A data science and policy perspective
inferred← citeshttps://doi.org/10.1016/j.enpol.2024.114141openalex-snapshot
Toward sustainable heating: Assessment of the carbon mitigation potential from residential heating in northern rural China
inferred← citeshttps://doi.org/10.1016/j.eswa.2023.120109openalex-snapshot
Building energy performance prediction: A reliability analysis and evaluation of feature selection methods
inferred← citeshttps://doi.org/10.1016/j.future.2018.02.022openalex-snapshot
Research on anomaly detection algorithm based on generalization latency of telecommunication network
inferred← citeshttps://doi.org/10.1016/j.jclepro.2019.119461openalex-snapshot
Transdisciplinarity in energy retrofit. A Conceptual Framework
inferred← citeshttps://doi.org/10.1016/j.jobe.2020.101778openalex-snapshot
Developing data-driven models for energy-efficient heating design in office buildings
inferred← citeshttps://doi.org/10.1016/j.jobe.2021.102706openalex-snapshot
A review of data-driven building performance analysis and design on big on-site building performance data
inferred← citeshttps://doi.org/10.1016/j.jobe.2021.103018openalex-snapshot
Exploring data-driven building energy-efficient design of envelopes based on their quantified impacts
inferred← citeshttps://doi.org/10.1016/j.jobe.2022.104574openalex-snapshot
A parallel decomposition approach for building design optimization
inferred← citeshttps://doi.org/10.1016/j.jobe.2026.115222openalex-snapshot
Identification of essential variables for the estimation of energy demand in buildings at European scale
inferred← citeshttps://doi.org/10.1016/j.jretconser.2019.02.019openalex-snapshot
A data-driven framework for predicting weather impact on high-volume low-margin retail products
inferred← citeshttps://doi.org/10.1016/j.rser.2019.109542openalex-snapshot
A decision tree method for explaining household gas consumption: The role of building characteristics, socio-demographic variables, psychological factors and household behaviour
inferred← citeshttps://doi.org/10.1016/j.rser.2024.114400openalex-snapshot
Determinants of residential electricity consumption in South, East and South East Asia: A systematic review
inferred← citeshttps://doi.org/10.1016/j.scs.2017.10.002openalex-snapshot
A GIS-statistical approach for assessing built environment energy use at urban scale
inferred← citeshttps://doi.org/10.1016/j.scs.2019.101643openalex-snapshot
Coal consumption prediction model of space heating with feature selection for rural residences in severe cold area in China
inferred← citeshttps://doi.org/10.1016/j.scs.2021.103191openalex-snapshot
An energy consumption model for the Algerian residential building's stock, based on a triangular approach: Geographic Information System (GIS), regression analysis and hierarchical cluster analysis
inferred← citeshttps://doi.org/10.1016/j.scs.2023.104694openalex-snapshot
A combined workflow to generate citywide building energy demand profiles from low-level datasets
inferred← citeshttps://doi.org/10.1016/j.wse.2022.05.002openalex-snapshot
Modeling groundwater nitrate concentrations using spatial and non-spatial regression models in a semi-arid environment
inferred← citeshttps://doi.org/10.1038/ncomms14916openalex-snapshot
Energy efficiency to reduce residential electricity and natural gas use under climate change
inferred← citeshttps://doi.org/10.1061/9780784482865.095openalex-snapshot
Forecasting Chilled Water Consumption under Climate Change: Regression Analysis of University Campus Buildings
inferred← citeshttps://doi.org/10.1080/19401493.2020.1729862openalex-snapshot
Bayesian calibration at the urban scale: a case study on a large residential heating demand application in Amsterdam
inferred← citeshttps://doi.org/10.1080/23744731.2017.1415088openalex-snapshot
Methodology to determine the impact of simplified building models on model-predictive-control morning start optimization performance
inferred← citeshttps://doi.org/10.1093/jamiaopen/ooag035openalex-snapshot
Spectral clustering identifies patterns of chiropractic care in a national longitudinal cohort
inferred← citeshttps://doi.org/10.1109/access.2021.3071180openalex-snapshot
Analysis of Energy Consumption at Slow Charging Infrastructure for Electric Vehicles
inferred← citeshttps://doi.org/10.1109/csci46756.2018.00055openalex-snapshot
Dynamic Prediction of Building HVAC Energy Consumption by Ensemble Learning Approach
inferred← citeshttps://doi.org/10.1109/iedm19572.2017.9110625openalex-snapshot
Predicting World Energy Consumption: Comparison of ANN and Regression Analysis
inferred← citeshttps://doi.org/10.1109/tem.2024.3422821openalex-snapshot
Statistical and Artificial Intelligence-Based Tools for Building Energy Prediction: A Systematic Literature Review
inferred← citeshttps://doi.org/10.1109/tii.2019.2954098openalex-snapshot
Smart Grids Data Analysis: A Systematic Mapping Study
inferred← citeshttps://doi.org/10.1111/jiec.13097openalex-snapshot
Interrelationships between electricity, gas, and water consumption in large‐scale buildings
inferred← citeshttps://doi.org/10.1177/0739456x17739674openalex-snapshot
Further Opportunities to Reduce the Energy Use and Greenhouse Gas Emissions of Buildings
inferred← citeshttps://doi.org/10.1186/s12913-023-10423-9openalex-snapshot
Improving hospital quality risk-adjustment models using interactions identified by hierarchical group lasso regularisation
inferred← citeshttps://doi.org/10.17615/h09q-3v83openalex-snapshot
Prediction and Utility of a clinical fracture risk score in administrative claims
inferred← citeshttps://doi.org/10.21203/rs.3.rs-2026639/v1openalex-snapshot
Hierarchical Group Lasso Regression for Efficiently Identifying Interactions in Healthcare Risk-Adjustment Models
inferred← citeshttps://doi.org/10.21203/rs.3.rs-4095693/v1openalex-snapshot
Physical, social, and behavioural determinants of household energy consumption in the UK
inferred← citeshttps://doi.org/10.2139/ssrn.6837726openalex-snapshot
An Interpretable AI Framework for Dual-Outcome Energy Benchmarking of Multifunctional Public Schools
inferred← citeshttps://doi.org/10.31224/osf.io/ahn3eopenalex-snapshot
Interrelated Patterns of Electricity, Gas, and Water Consumption in Large-Scale Buildings
inferred← citeshttps://doi.org/10.3233/jifs-234798openalex-snapshot
RETRACTED: Hybrid optimization approach for energy minimization in wireless sensor networks leveraging XGBoost and random forest
inferred← citeshttps://doi.org/10.3389/fbuil.2019.00105openalex-snapshot
Exploratory Analysis of Energy Use Across Building Types and Geographic Regions in the United States
inferred← citeshttps://doi.org/10.3389/fenrg.2022.1073271openalex-snapshot
Shaping energy cost management in process industries through clustering and soft sensors
inferred← citeshttps://doi.org/10.3389/fenvs.2021.724095openalex-snapshot
A Counterfactual Baseline for Assessing Future Environmental Impact: A Case Study of the Belt and Road Initiative
inferred← citeshttps://doi.org/10.3390/buildings12081129openalex-snapshot
Daily Carbon Assessment Framework: Towards Near Real-Time Building Carbon Emission Benchmarking for Operative and Design Insights
inferred← citeshttps://doi.org/10.3390/buildings14082335openalex-snapshot
Clustering Open Data for Predictive Modeling of Residential Energy Consumption across Variable Scales: A Case Study in Andalusia, Spain
inferred← citeshttps://doi.org/10.3390/en11061477openalex-snapshot
A Dynamic Model for Indoor Temperature Prediction in Buildings
inferred← citeshttps://doi.org/10.3390/su10051515openalex-snapshot
Estimation and Forecasts for the Share of Renewable Energy Consumption in Final Energy Consumption by 2020 in the European Union
inferred← citeshttps://doi.org/10.3390/su15065211openalex-snapshot
Benchmarking Evaluation of Building Energy Consumption Based on Data Mining
inferred← citeshttps://doi.org/10.3390/su16188220openalex-snapshot
The Impact of Architects’ Reasoning on Early Design Decision-Making for Energy-Efficient Buildings
inferred← citeshttps://doi.org/10.4324/9781003178545-17openalex-snapshot
Further Opportunities to Reduce the Energy Use and Greenhouse Gas Emissions of Buildings
inferred← citeshttps://doi.org/10.48550/arxiv.2006.01672openalex-snapshot
Explaining the distribution of energy consumption at slow charging infrastructure for electric vehicles from socio-economic data
inferred← citeshttps://doi.org/10.54692/lgurjcsit.2020.0404106openalex-snapshot
Diagnostic Framework for Electricity Losses in Pakistan Using Data Visualization Techniques
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.