# BuildEvo: Designing Building Energy Consumption Forecasting Heuristics via LLM-driven Evolution

arXiv: https://arxiv.org/abs/2507.12207 · HTML: https://mlsanity.com/paper/2507.12207/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 16 Jul 2025.

## Builds on (most cited first, 4 of 4)

- [Automatic Prompt Optimization with "Gradient Descent" and Beam Search](https://mlsanity.com/paper/2305.03495/) (2023)
- [ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution](https://mlsanity.com/paper/2402.01145/) (2024)
- [Combining Physics-based and Data-driven Modeling for Building Energy Systems](https://mlsanity.com/paper/2411.01055/) (2024)
- [Runtime Analysis of Probabilistic Crowding and Restricted Tournament Selection for Bimodal Optimisation](https://mlsanity.com/paper/1803.09766/) (2018)

## Built on by (most cited first, 2 of 2)

- [EvoNav: Evolutionary Reward Function Design for Robot Navigation with Large Language Models](https://mlsanity.com/paper/2605.11859/) (2026)
- [Physics-Informed Large Language Models for HVAC Anomaly Detection with Autonomous Rule Generation](https://mlsanity.com/paper/2510.17146/) (2025)

## Abstract (arXiv)

Accurate building energy forecasting is essential, yet traditional heuristics often lack precision, while advanced models can be opaque and struggle with generalization by neglecting physical principles. This paper introduces BuildEvo, a novel framework that uses Large Language Models (LLMs) to automatically design effective and interpretable energy prediction heuristics. Within an evolutionary process, BuildEvo guides LLMs to construct and enhance heuristics by systematically incorporating physical insights from building characteristics and operational data (e.g., from the Building Data Genome Project 2). Evaluations show BuildEvo achieves state-of-the-art performance on benchmarks, offering improved generalization and transparent prediction logic. This work advances the automated design of robust, physically grounded heuristics, promoting trustworthy models for complex energy systems.