Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Developing Invasive Weed, Social Spider, Shuffled Frog Leaping, Biogeography-Based, and Harmony Search Optimization Algorithms for the Early Prediction of Residential Building’s Cooling Load Simulation

Version 1 : Received: 20 January 2021 / Approved: 21 January 2021 / Online: 21 January 2021 (09:23:04 CET)

How to cite: Moayedi, H.; Mosavi, A. Developing Invasive Weed, Social Spider, Shuffled Frog Leaping, Biogeography-Based, and Harmony Search Optimization Algorithms for the Early Prediction of Residential Building’s Cooling Load Simulation. Preprints 2021, 2021010411 (doi: 10.20944/preprints202101.0411.v1). Moayedi, H.; Mosavi, A. Developing Invasive Weed, Social Spider, Shuffled Frog Leaping, Biogeography-Based, and Harmony Search Optimization Algorithms for the Early Prediction of Residential Building’s Cooling Load Simulation. Preprints 2021, 2021010411 (doi: 10.20944/preprints202101.0411.v1).

Abstract

Regarding the high efficiency of metaheuristic techniques in energy performance analysis, this paper scrutinizes and compares five novel optimizers, namely biogeography-based optimization (BBO), invasive weed optimization (IWO), social spider algorithm (SOSA), shuffled frog leaping algorithm (SFLA), and harmony search algorithm (HSA) for the early prediction of cooling load in residential buildings. The algorithms are coupled with a multi-layer perceptron (MLP) to adjust the neural parameters that connect the CL with the influential factors. The complexity of the models is optimized by means of a trial-and-error effort, and it was shown that the BBO and IWO need more crowded spaces for fulfilling the optimization. The results revealed that the internal parameters (i.e., biases and weights) suggested by the BBO generate the most reliable MLP for both analyzing and generalizing the CL pattern (with nearly 93 and 92% correlations, respectively). Followed by this, the IWO emerged as the second powerful optimizer with mean absolute errors of 1.8632 and 1.9110 in the training and testing phases. Therefore, the BBO-MLP and IWO-MLP can be reliably used for accurate analysis of the CL in future projects.

Subject Areas

Energy performance; Cooling load prediction; Neural network, Metaheuristic optimization.

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