Prediction of Thermal Energy Demand Using Fuzzy-Based Models Synthesized with Metaheuristic Algorithms
Bibliographic record
Abstract
Increasing consumption of energy calls for proper approximation of demand towards a sustainable and cost-effective development. In this work, novel hybrid methodologies aim to predict the annual thermal energy demand (ATED) by analyzing the characteristics of the building, such as transmission coefficients of the elements, glazing, and air-change conditions. For this objective, an adaptive neuro-fuzzy-inference system (ANFIS) was optimized with equilibrium optimization (EO) and Harris hawks optimization (HHO) to provide a globally optimum training. Moreover, these algorithms were compared to two benchmark techniques, namely grey wolf optimizer (GWO) and slap swarm algorithm (SSA). The performance of the designed hybrids was evaluated using different accuracy indicators, and based on the results, ANFIS-EO and ANFIS-HHO (with respective RMSEs equal to 6.43 and 6.90 kWh·m−2·year−1 versus 9.01 kWh·m−2·year−1 for ANFIS-GWO and 11.80 kWh·m−2·year−1 for ANFIS-SSA) presented the most accurate analysis of the ATED. Hence, these models are recommended for practical usages, i.e., the early estimations of ATED, leading to a more efficient design of buildings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".