Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings

Abstract

A reliable prediction of sustainable energy consumption is key for designing environmentally friendly buildings. In this study, three novel hybrid intelligent methods, namely the grasshopper optimization algorithm (GOA), wind-driven optimization (WDO), and biogeography-based optimization (BBO), are employed to optimize the multitarget prediction of heating loads (HLs) and cooling loads (CLs) in the heating, ventilation and air conditioning (HVAC) systems. Concerning the optimization of the applied algorithms, a series of swarm-based iterations are performed, and the best structure is proposed for each model. The GOA, WDO, and BBO algorithms are mixed with a class of feedforward artificial neural networks (ANNs), which is called a multi-layer perceptron (MLP) to predict the HL and CL. According to the sensitivity analysis, the WDO with swarm size = 500 proposes the most-fitted ANN. The proposed WDO-ANN provided an accurate prediction in terms of heating load (training (R2 correlation = 0.977 and RMSE error = 0.183) and testing (R2 correlation = 0.973 and RMSE error = 0.190)) and yielded the best-fitted prediction in terms of cooling load (training (R2 correlation = 0.99 and RMSE error = 0.147) and testing (R2 correlation = 0.99 and RMSE error = 0.148)).

Investigating the predictive contribution of attitude towards life and belief system on self-resilience and psychological toughness of cancer patients about the mediating role of emotion regulation

“…Therefore, emotion regulation can be effective on psychological toughness. By Increasing the data points the machine learning methods can be used, e.g., [33][34][35][36][37][38][39][40][41][42][43][44]. Therefore, the study on the perception to life and belief system on self-resilience and psychological toughness of cancer patients about the mediating role of emotion regulation can be better investigated.…”

https://www.academia.edu/93557562/Predicting_Power_and_Hydrogen_Generation_of_a_Renewable_Energy_Converter_Utilizing_Data_Driven_methods_A_Sustainable_Smart_Grid_Case_Study

“…The accuracy of the simulation findings, which enable researchers and investors to easily attain the required results by knowing the installation location of these devices, are another benefit of this concept. The creation of hydrogen in addition to electrical energy production is one of the principal uses of wave generators in the oceans [85]. The Navier-Stokes equations were used to examine the topic of how wave generators interact with ocean waves.…”

Application of a New Hybrid Machine Learning (Fuzzy-PSO) for Detection of Breast's Tumor

“…According to the data frequency of each class, the weighted average of the is considered and applied in fitness functions. The characteristic curve of system performance has been drawn for all methods [35][36][37][38][39][40]. The data has been divided into two parts to validate the systems, training, and testing, using the 10-part cross-validation method.…”