Green Building Energy Cost Optimization With Deep Belief Network and Firefly Algorithm

Yan Biao Liao, Yong Liu, Chao‐Yu Chen, Lili Zhang

Frontiers in Energy Research 2021 article cited by 11

Source Open-access PDF More in Engineering & Materials

importance 4/10 ai assisted Building Energy and Comfort Optimization building energy stage: research

Problem

Commercial building HVAC systems consume high energy costs while needing to maintain acceptable indoor air quality.

Why it matters

Energy is a major operating cost for commercial buildings, and poor optimization wastes money and energy.

What the AI did

Artificial neural networks predicted real-time energy use, then a firefly algorithm optimized HVAC operations to reduce cost while maintaining required indoor air quality.

Results

not stated in source (abstract claims reduced energy cost but gives no figures)

Impact

Framework demonstrated on data from one commercial building in Chengdu; no broader adoption stated.

Record

Venue	Frontiers in Energy Research
Published	2021
Work type	article
Authors	Yan Biao Liao, Yong Liu, Chao‐Yu Chen, Lili Zhang
Citations	11 (OpenAlex, at import time)
References	33
Model or method	Artificial neural networks (deep belief network per title); firefly algorithm
Organization	not stated in source
Primary source	https://doi.org/10.3389/fenrg.2021.805206
Open access	gold
Keywords	HVAC, energy cost, firefly algorithm, neural networks, indoor air quality

Importance rating

4 / 10

real but narrower advance

HVAC energy cost reduction matters for commercial building operations, but abstract demonstrates only one building in Chengdu with no quantified savings; narrow advance.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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