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Smart Structures and Systems Volume 29, Number 5, May 2022 , pages 677-691 DOI: https://doi.org/10.12989/sss.2022.29.5.677 |
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Metaheuristic-designed systems for simultaneous simulation of thermal loads of building |
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Chang Lin and Junsong Wang
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| Abstract | ||
| Water cycle algorithm (WCA) has been a very effective optimization technique for complex engineering problems. This study employs the WCA for simultaneous prediction of heating load (LH) and cooling load (LC) in residential buildings. This algorithm is responsible for optimally tuning a neural network (NN). Utilizing 614 records, the behavior of the LH and LC is explored and the captured knowledge is then used to predict for 154 unanalyzed building conditions. Since the WCA is a population-based algorithm, different numbers of the searching agents were tested to find the most optimum configuration. It was observed that the best solution is discovered by 500 agents. A comparison with five newly-developed benchmark optimizers, namely equilibrium optimizer (EO), multi-tracker optimization algorithm (MTOA), slime mould algorithm (SMA), multi-verse optimizer (MVO), and electromagnetic field optimization (EFO) revealed that the WCANN predicts the desired parameters with considerably larger accuracy. Obtained root mean square errors (1.4866, 2.1296, 2.8279, 2.5727, 2.5337, and 2.3029 for the LH and 2.1767, 2.6459, 3.1821, 2.9732, 2.9616, and 2.6890 for the LC) indicated that the most reliable prediction was presented by the proposed model. The EFONN, however, provided a more time-effective solution. Lastly, an explicit predictive formula was elicited from the WCANN. | ||
| Key Words | ||
| cooling load; energy performance; heating load; neural computing; water cycle algorithm | ||
| Address | ||
| Chang Lin and Junsong Wang: School of Architecture, South China University of Technology, 381 Wushan Road, Tianhe District, Guangzhou, Guangdong 510640, China | ||