From White to Black-Box Models: A Review of Simulation Tools for Building Energy Management and Their Application in Consulting Practices

Energies, Journal Year: 2024, Volume and Issue: 17(2), P. 376 - 376

Published: Jan. 12, 2024

Buildings consume significant energy worldwide and account for a substantial proportion of greenhouse gas emissions. Therefore, building management has become critical with the increasing demand sustainable buildings energy-efficient systems. Simulation tools have crucial in assessing effectiveness their systems, they are widely used management. These simulation can be categorized into white-box black-box models based on level detail transparency model’s inputs outputs. This review publication comprehensively analyzes white-box, black-box, web tool tools. We also examine different scales, ranging from single-family homes to districts cities, various modelling approaches, such as steady-state, quasi-steady-state, dynamic. aims pinpoint advantages drawbacks tools, offering guidance upcoming research field aim help researchers, designers, engineers better understand available make informed decisions when selecting using them.

Language: Английский

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Probabilistic-based electricity demand forecasting with hybrid convolutional neural network-extreme learning machine model

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 132, P. 107918 - 107918

Published: Feb. 3, 2024

Language: Английский

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Statistical and machine learning approaches for energy efficient buildings

Energy and Buildings, Journal Year: 2025, Volume and Issue: unknown, P. 115309 - 115309

Published: Jan. 1, 2025

Language: Английский

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Technological innovation, trade openness, natural resources, clean energy on environmental sustainably: a competitive assessment between CO2 emission, ecological footprint, load capacity factor and inverted load capacity factor in BRICS+T

Frontiers in Environmental Science, Journal Year: 2025, Volume and Issue: 12

Published: Feb. 4, 2025

The study investigates the relationship between technological innovation, clean energy, trade openness, and natural resource rents on environmental sustainability within BRICS + T nations. Motivated by urgent need to address escalating CO2 emissions—reaching 36.4 billion metric tons in 2022—the research aims understand how these factors influence emissions, ecological footprint, load capacity factor, its inverse, contributing Sustainable Development Goals (SDGs). uses panel data from countries spanning period 1990 2022. Employing advanced econometric techniques such as Dynamic Seemingly Unrelated Regression (DSUR), Cross-Sectionally Augmented Panel Unit Root (CUP-FM, CUP-BC), nonlinear autoregressive distributed lag (ARDL) models, tests Environmental Kuznets Curve (EKC) hypothesis evaluates asymmetric effects of variables. Key findings indicate that innovation consistently reduces emissions footprints, reinforcing role promoting through cleaner technologies more efficient industrial processes. Clean energy adoption has also been shown be a significant driver reducing degradation, with consistent negative while improving factor. However, openness exhibits dual effect. While it enhances use efficiency, simultaneously increases likely due heightened activity. Natural display mixed results: some cases, they exacerbate others, contribute funding eco-friendly initiatives. recommends nations prioritize investments green technologies, strengthen regulations, enhance international collaboration accelerate transition renewable energy. Policymakers should balance benefits stricter standards mitigate adverse sustainability. These integrated strategies are essential for achieving targets outlined SDGs.

Language: Английский

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Digital Twin framework for automated fault source detection and prediction for comfort performance evaluation of existing non-residential Norwegian buildings

Energy and Buildings, Journal Year: 2022, Volume and Issue: 281, P. 112732 - 112732

Published: Dec. 28, 2022

Numerous buildings fall short of expectations regarding occupant satisfaction, sustainability, or energy efficiency. In this paper, the performance in terms comfort is evaluated using a probabilistic model based on Bayesian networks (BNs). The BN founded an in-depth analysis satisfaction survey responses and thorough study building parameters. This also presents user-friendly visualization compatible with BIM to simplify data collecting two case studies from Norway 2019 2022. paper proposes novel Digital Twin approach for incorporating information modeling (BIM) real-time sensor data, occupants' feedback, comfort, HVAC faults detection prediction that may affect comfort. New methods as platform, well predictive maintenance method detect anticipate problems system, are presented. These will help decision-makers improve conditions buildings. However, due intricate interaction between numerous equipment absence integration among FM systems, CMMS, BMS, integrated into framework utilizing ontology graphs generalize so it can be applied many results aid facility management sector by offering insight aspects influence speeding up process identifying malfunctions, pointing toward possible solutions.

Language: Английский

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Building Energy Prediction Models and Related Uncertainties: A Review

Buildings, Journal Year: 2022, Volume and Issue: 12(8), P. 1284 - 1284

Published: Aug. 21, 2022

Building energy usage has been an important issue in recent decades, and prediction models are tools for analysing this problem. This study provides a comprehensive review of building uncertainties the models. First, paper introduces three types methods: white-box models, black-box grey-box The principles, strengths, shortcomings, applications every model discussed systematically. Second, analyses terms human, building, weather factors. Finally, research gaps predicting consumption summarised order to guide optimisation methods.

Language: Английский

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Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends

Energies, Journal Year: 2022, Volume and Issue: 15(19), P. 7231 - 7231

Published: Oct. 1, 2022

Buildings use up to 40% of the global primary energy and 30% greenhouse gas emissions, which may significantly impact climate change. Heating, ventilation, air-conditioning (HVAC) systems are among most significant contributors consumption carbon emissions. Furthermore, HVAC demand is expected rise in future. Therefore, advancements systems’ performance design would be critical for mitigating worldwide environmental concerns. To make such advancements, modeling model predictive control (MPC) play an imperative role designing operating effectively. Building simulations analysis techniques effectively implement schemes building system operation phases, thus provide quantitative insights into behaviors flow architects engineers. Extensive research advanced modeling/control have emerged better solutions response issues. This study reviews state-of-the-art updates MPC applications based on recent articles (e.g., from MDPI’s Elsevier’s databases). For review process, investigation relevant keywords context-based collected data first carried out overview their frequency distribution comprehensively. Then, this narrows topic selection search scopes focus papers extract information outcomes. Finally, a systematic approach adopted technologies. reveals that crucial implementing MPC-based reduce cost. paper presents details major techniques, including white-box, grey-box, black-box approaches. also provides future researchers practical fields.

Language: Английский

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Experimental validation of multi-stage optimal energy management for a smart microgrid system under forecasting uncertainties

Energy Conversion and Management, Journal Year: 2023, Volume and Issue: 291, P. 117309 - 117309

Published: June 23, 2023

Language: Английский

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Investigating the application of a commercial and residential energy consumption prediction model for urban Planning scenarios with Machine Learning and Shapley Additive explanation methods

Energy and Buildings, Journal Year: 2023, Volume and Issue: 287, P. 112965 - 112965

Published: March 9, 2023

Building energy forecasting methodologies utilized by municipal governments tend to be geared heavily towards depicting broader qualitative representations of regional change and are in need complementary data-driven models that can produce quantitatively reliable depictions future consumption at the neighborhood-level. The current research demonstrates an application a Machine Learning (ML) model form Extreme Gradient Boosting (XGBoost) algorithm for use commercial residential buildings. methodology serves improve on scenario planning providing more spatially granular representation use. In this way, city government urban planners accurately set carbon emission benchmarks target specific locales sustainability initiatives. second major contribution study is demonstrate how approaches utilize existing techniques compensate gaps data. This work developed through case Philadelphia. begins with construction year 2015 corresponding 2045. forecast integrate socioeconomic trends from Delaware Valley Regional Planning Commission (DVRPC) Enduring Urbanism. DVRPC's open-source Geographic Information System (GIS) datasets, Commercial Buildings Energy Consumption Survey (CBECS), CoStar real estate applies Residential (RECS), Public Use Microdata Sample (PUMS), Census Bureau American Community (ACS) estimates. A SHAP (SHapley Additive exPlanations) analysis implemented pinpoint feature contributions model's By using PopGen software, estimates could analyzed household level, smallest possible scale. To provide useful resource key stakeholders, aggregates output Traffic Analysis Zone (TAZ) Area (PUMA) display detailed results indicate DVRPC Urbanism income employment do not significantly affect area. However, features related lower building intensity (e.g., square footage, fewer floors per building) were associated reduced both models. Additionally, found buildings under "single-family attached" zoning designation correspond higher

Language: Английский

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Domain knowledge decomposition of building energy consumption and a hybrid data-driven model for 24-h ahead predictions

Applied Energy, Journal Year: 2023, Volume and Issue: 344, P. 121244 - 121244

Published: May 20, 2023

Language: Английский

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