Subtopic Deep Dive
Building Energy Consumption Modeling
Research Guide
What is Building Energy Consumption Modeling?
Building Energy Consumption Modeling develops simulation and data-driven models to predict and optimize energy use in residential and commercial buildings, incorporating occupant behavior and demand-side management.
This subtopic integrates building information, user features, and environmental factors into predictive models. Key approaches include artificial neural networks (Kim et al., 2019, 35 citations) and analyses of sociodemographics with appliances (Lee and Song, 2021, 27 citations). Over 10 papers from 2008-2022 address residential energy patterns, with foundational work on specific consumption values (Evans et al., 2013, 106 citations).
Why It Matters
Precise models enable demand-side management in urban areas, reducing peak loads during crises (Park and Kim, 2017). They support retrofitting for energy efficiency across climates (Shandilya et al., 2020) and inform greenhouse gas reduction roadmaps (Ha et al., 2019). In Korea's new towns, such modeling cut residential demands by identifying usage factors (Park and Kim, 2017). These applications drive sustainability in smart cities (Choi and Song, 2022).
Key Research Challenges
Occupant Behavior Variability
Models struggle to capture diverse user habits affecting energy use, as seen in residential predictions requiring building and user data integration (Kim et al., 2019). Surveys reveal IAQ links to behavior but lack predictive scaling (Hattori et al., 2022). This variability complicates demand forecasting (Lee and Song, 2021).
Data Scarcity in Crises
Limited datasets hinder modeling during disasters, where normal patterns shift. Residential studies in specific regions like Korea highlight gaps in scalable data (Park and Kim, 2017). Integrating sociodemographics demands more granular inputs (Lee and Song, 2021).
Model Generalization Across Climates
Energy efficiency retrofits perform differently by climate, requiring optimized simulations (Shandilya et al., 2020). Cold store benchmarks exist but residential transferability is low (Evans et al., 2013). Neural networks trained on local data underperform elsewhere (Kim et al., 2019).
Essential Papers
TLD-100 post-irradiation fading characteristics according to IEC 62387:2020 standard
J. Stanković, Marko Krajinović, Nikola Kržanović et al. · 2021 · Book of Abstracts · 273 citations
The results of the post-irradiation fading of whole body dosemeters based on two TLD-100TM (Thermo Scientific™ Harshaw™, USA) detectors are presented. The dosemeters are regularly used by accredite...
Specific energy consumption values for various refrigerated food cold stores
Judith Evans, A Foster, J M Huet et al. · 2013 · Energy and Buildings · 106 citations
Artificial Neural Network-Based Residential Energy Consumption Prediction Models Considering Residential Building Information and User Features in South Korea
Mansu Kim, Sungwon Jung, Joo-won Kang · 2019 · Sustainability · 35 citations
When researching the energy consumption of residential buildings, it is becoming increasingly important to consider how residents use energy. With the advancement of computing power and data analys...
Direction for a Transition toward Smart Sustainable Cities based on the Diagnosis of Smart City Plans
Hee-Sun Choi, Seulki Song · 2022 · Smart Cities · 33 citations
Achieving urban sustainability through smart cities is necessary to manage urban environmental problems that threaten human survival. Smart city policy emphasizes the environmental aspects of urban...
Energy Demand Reduction in the Residential Building Sector: A Case Study of Korea
Da Yeon Park, Mi Jeong Kim · 2017 · Energies · 33 citations
This study sought to examine ways of reducing energy demands in the residential building sector by measuring energy usage and associated factors in Bundang District. This District represents the fi...
Optimization of Thermal Behavior and Energy Efficiency of a Residential House Using Energy Retrofitting in Different Climates
Apeksha Shandilya, Martin Hauer, Wolfgang Streicher · 2020 · Civil Engineering and Architecture · 33 citations
The poor performance of buildings does not only play a major role in energy consumption but is also responsible for thermal discomfort inside the building.Prior to planning the building retrofittin...
A Study on the Limitations of South Korea’s National Roadmap for Greenhouse Gas Reduction by 2030 and Suggestions for Improvement
Sungkyun Ha, Sungho Tae, Rakhyun Kim · 2019 · Sustainability · 30 citations
South Korea must submit its targets for greenhouse gas reduction by 2030 to comply with the Paris Agreement. While South Korea’s government has announced a roadmap for achieving greenhouse gas redu...
Reading Guide
Foundational Papers
Start with Evans et al. (2013, 106 citations) for specific energy benchmarks in buildings, then Mikola and Kõiv (2014) on heat pump efficiency analysis.
Recent Advances
Study Kim et al. (2019) for ANN-based predictions and Shandilya et al. (2020) for climate-adaptive retrofitting.
Core Methods
Core techniques: neural networks (Kim et al., 2019), sociodemographic regression (Lee and Song, 2021), simulation-based retrofitting (Shandilya et al., 2020).
How PapersFlow Helps You Research Building Energy Consumption Modeling
Discover & Search
Research Agent uses searchPapers on 'building energy consumption neural networks' to find Kim et al. (2019), then citationGraph reveals 35 citing works and findSimilarPapers uncovers Lee and Song (2021) for occupant factors.
Analyze & Verify
Analysis Agent applies readPaperContent to Kim et al. (2019) abstracts, runs verifyResponse (CoVe) on model accuracy claims, and uses runPythonAnalysis to replot energy prediction correlations with NumPy/pandas; GRADE scores evidence strength for residential ANN reliability.
Synthesize & Write
Synthesis Agent detects gaps in occupant-crisis modeling between Park and Kim (2017) and Shandilya et al. (2020), flags contradictions in efficiency metrics; Writing Agent uses latexEditText for model equations, latexSyncCitations for 10+ papers, latexCompile for reports, and exportMermaid for energy flow diagrams.
Use Cases
"Analyze energy data from Kim et al. 2019 with Python for prediction errors"
Research Agent → searchPapers → Analysis Agent → readPaperContent + runPythonAnalysis (pandas/matplotlib on user features dataset) → error metrics plot and statistical verification.
"Write LaTeX report on retrofitting models from Shandilya et al. 2020"
Synthesis Agent → gap detection → Writing Agent → latexEditText (add equations) → latexSyncCitations (Evans 2013 et al.) → latexCompile → PDF with climate-optimized diagrams.
"Find GitHub code for residential ANN energy models"
Research Agent → citationGraph (Kim et al. 2019) → Code Discovery workflow (paperExtractUrls → paperFindGithubRepo → githubRepoInspect) → executable ANN scripts for local retraining.
Automated Workflows
Deep Research workflow scans 50+ papers via searchPapers on 'residential energy modeling Korea', structures report with Evans et al. (2013) benchmarks and Kim et al. (2019) ANNs. DeepScan's 7-step chain verifies occupant data from Lee and Song (2021) with CoVe checkpoints and Python stats. Theorizer generates hypotheses linking IAQ (Hattori et al., 2022) to demand models.
Frequently Asked Questions
What is Building Energy Consumption Modeling?
It develops simulation and data-driven models to predict energy use in buildings, incorporating occupant behavior (Kim et al., 2019).
What are key methods used?
Artificial neural networks predict residential consumption using building and user data (Kim et al., 2019); retrofitting optimizes thermal behavior (Shandilya et al., 2020).
What are major papers?
Foundational: Evans et al. (2013, 106 citations) on cold store energy; recent: Kim et al. (2019, 35 citations) on ANNs, Lee and Song (2021, 27 citations) on end-use determinants.
What open problems exist?
Scaling occupant variability across crises and climates; generalizing local models like Korea's (Park and Kim, 2017) globally remains unsolved.
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Part of the Energy and Environmental Systems Research Guide