Integrating Building Performance Simulation in Agent-based Models to Support Demand-side Management Strategies
Bibliographic record
Abstract
Abstract Demand-side management (DSM) strategies are essential for addressing critical energy challenges, and agent-based modeling (ABM) has emerged as a promising approach to model and inform DSM strategies. However, existing models often overlook the dynamic nature of building performance due to simplified building systems’ representation. This paper proposes a novel workflow that equips an agent-based model of a group of buildings with integrated building performance simulation (BPS) capabilities, coupled with supply-side energy modeling, to enable comprehensive testing of DSM strategies. A case study is conducted to compare decentralized and centralized renewable energy supply systems, alongside the evaluation of DSM strategies such as setpoint control during peak hours and tariff-based incentives. Key performance indicators – including solar energy self-consumption, self-sufficiency rate, surplus ratio, and peak load ratio – are used to assess the effectiveness of these strategies. Results indicate that (i) centralized renewable energy scenarios achieve 4–5% higher solar energy self-consumption rates compared to decentralized scenarios, (ii) setpoint control during peak hours reduces peak load ratio by 0.7%, and (iii) tariff incentives unexpectedly increase peak loads due to shifting demand to evening peaks. The proposed model serves as a proof of concept for the dynamic and integrated modeling of energy demand and renewable energy generation needed to test DSM strategies at the community scale.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".