A systematic review of multi-output prediction model for indoor environment and heating, ventilation, and air conditioning energy consumption in buildings
Bibliographic record
Abstract
Heating, ventilation and air conditioning (HVAC) systems could significantly impact indoor environmental quality, particularly in terms of thermal comfort and indoor air quality. Achieving a high-quality indoor environment poses challenges to the energy consumption of HVAC systems. Thus, balancing thermal comfort, indoor air quality (IAQ) and energy consumption becomes a challenging task. Currently, indoor environment prediction methods are considered effective solutions to address this issue. However, the published literature usually concentrates on single aspects like thermal comfort, air quality or energy consumption, with multi-aspect prediction methods being rare. The present work reviews research spanning the last decade that employs machine learning methods for predicting indoor environments and HVAC energy consumption through separate and multi-output predictive models. Separate predictive models focus on HVAC systems’ impact on the indoor environment, while multi-output models consider the interplay of various outputs. This article gives a thorough insight into machine learning prediction models’ workflow, detailing data collection, feature selection and model optimization for each research goal. A systematic assessment of methods for data collection of diverse prediction targets, machine learning algorithms and validation approaches for different prediction models is presented. This review highlights the complexities of data management, model development and validation, enriching the knowledge base in indoor environmental quality optimization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".