PMID- 38404842
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240227
IS  - 2405-8440 (Print)
IS  - 2405-8440 (Electronic)
IS  - 2405-8440 (Linking)
VI  - 10
IP  - 4
DP  - 2024 Feb 29
TI  - Early energy performance analysis of smart buildings by consolidated artificial 
      neural network paradigms.
PG  - e25848
LID - 10.1016/j.heliyon.2024.e25848 [doi]
LID - e25848
AB  - The assessment of energy performance in smart buildings has emerged as a 
      prominent area of research driven by the increasing energy consumption trends 
      worldwide. Analyzing the attributes of buildings using optimized machine learning 
      models has been a highly effective approach for estimating the cooling load 
      (C(L)) and heating load (H(L)) of the buildings. In this study, an artificial 
      neural network (ANN) is used as the basic predictor that undergoes optimization 
      using five metaheuristic algorithms, namely coati optimization algorithm (COA), 
      gazelle optimization algorithm (GOA), incomprehensible but intelligible-in-time 
      logics (IbIL), osprey optimization algorithm (OOA), and sooty tern optimization 
      algorithm (STOA) to predict the C(L) and H(L) of a residential building. The 
      models are trained and tested via an Energy Efficiency dataset (downloaded from 
      UCI Repository). A score-based ranking system is built upon three accuracy 
      evaluators including mean absolute percentage error (MAPE), root mean square 
      error (RMSE), and percentage-Pearson correlation coefficient (PPCC) to compare 
      the prediction accuracy of the models. Referring to the results, all models 
      demonstrated high accuracy (e.g., PPCCs >89%) for predicting both C(L) and H(L). 
      However, the calculated final scores of the models (43, 20, 39, 38, and 10 in 
      H(L) prediction and 36, 20, 42, 42, and 10 in C(L) prediction for the STOA, OOA, 
      IbIL, GOA, and COA, respectively) indicated that the GOA, IbIL, and STOA perform 
      better than COA and OOA. Moreover, a comparison with various algorithms used in 
      earlier literature showed that the GOA, IbIL, and STOA provide a more accurate 
      solution. Therefore, the use of ANN optimized by these three algorithms is 
      recommended for practical early forecast of energy performance in buildings and 
      optimizing the design of energy systems.
CI  - (c) 2024 The Authors. Published by Elsevier Ltd.
FAU - Guo, Guoqing
AU  - Guo G
AD  - Xi'an Jiaotong-liverpool University, Xi'an, Shannxi, 215123, China.
FAU - Liu, Peng
AU  - Liu P
AD  - Xi'an Jiaotong-liverpool University, Xi'an, Shannxi, 215123, China.
FAU - Zheng, Yuchen
AU  - Zheng Y
AD  - Chenyu Technology (Wuhan) Co., LTD, Wuhan, Hubei, 430074, China.
LA  - eng
PT  - Journal Article
DEP - 20240207
PL  - England
TA  - Heliyon
JT  - Heliyon
JID - 101672560
PMC - PMC10884448
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Energy performance
OT  - Prediction
OT  - Smart buildings
OT  - Thermal loads
COIS- The authors declare that they have no known competing financial interests or 
      personal relationships that could have appeared to influence the work reported in 
      this paper.
EDAT- 2024/02/26 06:42
MHDA- 2024/02/26 06:43
PMCR- 2024/02/07
CRDT- 2024/02/26 04:33
PHST- 2023/08/24 00:00 [received]
PHST- 2024/02/02 00:00 [revised]
PHST- 2024/02/04 00:00 [accepted]
PHST- 2024/02/26 06:43 [medline]
PHST- 2024/02/26 06:42 [pubmed]
PHST- 2024/02/26 04:33 [entrez]
PHST- 2024/02/07 00:00 [pmc-release]
AID - S2405-8440(24)01879-6 [pii]
AID - e25848 [pii]
AID - 10.1016/j.heliyon.2024.e25848 [doi]
PST - epublish
SO  - Heliyon. 2024 Feb 7;10(4):e25848. doi: 10.1016/j.heliyon.2024.e25848. eCollection 
      2024 Feb 29.