Suggesting a Stochastic Fractal Search Paradigm in Combination with Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings

Abstract

Early prediction of thermal loads plays an essential role in analyzing energy-efficient buildings’ energy performance. On the other hand, stochastic algorithms have recently shown high proficiency in dealing with this issue. These are the reasons that this study is dedicated to evaluating an innovative hybrid method for predicting the cooling load (CL) in buildings with residential usage. The proposed model is a combination of artificial neural networks and stochastic fractal search (SFS–ANNs). Two benchmark algorithms, namely the grasshopper optimization algorithm (GOA) and firefly algorithm (FA) are also considered to be compared with the SFS. The non-linear effect of eight independent factors on the CL is analyzed using each model’s optimal structure. Evaluation of the results outlined that all three metaheuristic algorithms (with more than 90% correlation) can adequately optimize the ANN. In this regard, this tool’s prediction error declined by nearly 23%, 18%, and 36% by applying the GOA, FA, and SFS techniques. Moreover, all used accuracy criteria indicated the superiority of the SFS over the benchmark schemes. Therefore, it is inferred that utilizing the SFS along with ANN provides a reliable hybrid model for the early prediction of CL.

Forecasting of energy efficiency in buildings using multilayer perceptron regressor with waterwheel plant algorithm hyperparameter

“…Results indicate that the Gaussian process is a valid approach for the prediction of building heating and cooling demand. The performance of artificial neural networks for HL and CL prediction was enhanced by (Moayedi et al, 2021) using genetic algorithms and imperialist competition algorithms. Results indicated that the optimization approach significantly improved the performance of the model, with the imperialist competition algorithms showing better results than GA in this case.…”

Micro‐ to macro‐scaling analysis of PM2.5 in sensitive environment of Himalaya, India

“…Air quality data (CO, SO 2 , O 3 , NO 2 and PM 2.5 ) of Taiwan Environmental Protection Administration analysed and concluded that the proposed multi-step fuzzy time series (MSFT) technique showed better results compared with seven predicting methods. In terms of effectiveness for a predictive approach, based on the fuzzy concept with Fractals techniques outperformed in contrast of classic fuzzy time series, linear regression models, and auto-regressive models (Moayedi & Mosavi, 2021). Researchers have used common wavelet transform fractal method (WTFM) for establishing correlation between H and PI of environmental factors.…”

Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu

“…Mirjalili et al ( 2014 ) proposed the metaheuristic algorithm grey wolf optimization (GWO) in 2014, a variant of the PSO with a metaphor, as proven in the recent works (Villalón et al, 2020 ). Similar to other metaheuristic approaches (Ala et al, 2020 ; Seifi et al, 2020 ; Moayedi and Mosavi, 2021a , b ), the algorithm is inspired by the social hierarchy and hunting strategies of gray wildlife wolves and it has been applied to various problems due to its simple idea (Heidari and Pahlavani, 2017 ; Aljarah et al, 2019 ; Heidari et al, 2019 ; Tang et al, 2020 ). Regardless of its defect, we still can see some performance features in this method (Niu et al, 2019 ; Hu et al, 2021 ).…”