Working Paper Article Version 1 This version is not peer-reviewed

Double-Target Based Network in Predicting Energy Consumption in Residential Buildings

Version 1 : Received: 5 January 2021 / Approved: 6 January 2021 / Online: 6 January 2021 (11:00:33 CET)

A peer-reviewed article of this Preprint also exists.

Moayedi, H.; Mosavi, A. Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings. Energies 2021, 14, 1331. Moayedi, H.; Mosavi, A. Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings. Energies 2021, 14, 1331.

Journal reference: Energies 2021, 14, 1331
DOI: 10.3390/en14051331

Abstract

Reliable prediction of sustainable energy consumption is the key to designing environmental friend buildings. In this study, two novel hybrid intelligent methods, namely grasshopper optimization algorithm (GOA), wind-driven optimization (WDO), and biogeography-based optimization (BBO) is employed to optimize the multitarget prediction of heating loads (HLs) and cooling loads (CLs) in heating ventilation and air conditioning (HVAC) systems. Concerning the optimization of applied hybrid algorithms, a series of swarm-based iteration is performed, and the best structure for the abovementioned methods are proposed. Besides, through sensitivity analyzing the relationship between the HLs and CLs and influential factors are highlighted. In other words, the GOA, WDO, and BBO algorithm are mixed with a class of feedforward artificial neural networks (ANN), which called MLP (multi-layer perceptron) to predict the HLs and CLs. According to the provided sensitivity analysis, the WDO with swarm size = 500 proposes the most proper-fitted terms after it has been combined with optimized MLP. The proposed WDO-MLP (training (R2 correlation=0.977 and RMSE error=0.183) and testing (R2 correlation=0.973 and RMSE error=0.190)) provided accurate prediction in the heating load and (training (R2 correlation=0.99 and RMSE error=0.147) and testing (R2 correlation=0.99 and RMSE error=0.148)) presents the most-fit prediction in the cooling load.

Keywords

Energy efficiency; Heating loads; heating ventilation and air conditioning; metaheuristic; optimization algorithms.

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