Journal ArticleDOI
Modelling the energy performance of residential buildings using advanced computational frameworks based on RVM, GMDH, ANFIS-BBO and ANFIS-IPSO
TLDR
Four advanced computational frameworks including relevance vector machine (RVM), group method of data handling (GMDH), hybridization of adaptive neuro-fuzzy interface system (ANFIS) and biogeography-based optimisation (BBO) are proposed as novel approaches to predict the heating load (HL) and cooling load (CL) of residential buildings.Abstract:
Modelling the heating load (HL) and cooling load (CL) is the cornerstone of the designing of energy-efficient buildings, since it determines the heating and cooling equipment requirements needed to retain comfortable indoor air conditions. Advanced and specialised modelling tools for energy-efficient buildings may provide a reliable estimation of the effect of alternative building designs. However, implementing these tools can be a labour-intensive task, very time-consuming and dependent on user experiences. Hence, in this study, four advanced computational frameworks including relevance vector machine (RVM), group method of data handling (GMDH), hybridization of adaptive neuro-fuzzy interface system (ANFIS) and biogeography-based optimisation (BBO), i.e. ANFIS-BBO, and hybridization of ANFIS and improved particle swarm optimisation (IPSO), i.e. ANFIS-IPSO, are proposed as novel approaches to predict the heating load (HL) and cooling load (CL) of residential buildings. Obtained results from the proposed models are compared using several performance parameters. In addition, several visualisation methods including Taylor diagram, regression characteristic curve, a novel method called accuracy matrix and rank analysis are used to demonstrate the model with the best performance. Furthermore, Anderson–Darling’ Normality (A-D) test and Mann–Whitney U’ (M − W) tests are studied as non-parametric statistical test for further investigations of the models. Obtained results indicate the excellent ability of the applied models to map the non-linear relationships between the input and output variables. Result also identified RVM as the best predictive model among four proposed models. Finally, two equations are derived from the RVM model to address the HL and CL of residential buildings.read more
Citations
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Journal ArticleDOI
Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models
TL;DR: The newly constructed HENSM model is very potential to be a new alternative in handling the overfitting issues of CML models and hence, can be used to predict the concrete CS, including the design of less polluting and more sustainable concrete constructions.
Journal ArticleDOI
A novel technique based on the improved firefly algorithm coupled with extreme learning machine (ELM-IFF) for predicting the thermal conductivity of soil
Navid Kardani,Abidhan Bardhan,Pijush Samui,Majidreza Nazem,Annan Zhou,Danial Jahed Armaghani +5 more
TL;DR: All the proposed hybrid models have a great ability to be considered as alternatives for empirical relevant models and can be employed in the initial stages of any engineering projects for fast determination of thermal conductivity.
Journal ArticleDOI
ELM-based adaptive neuro swarm intelligence techniques for predicting the California bearing ratio of soils in soaked conditions
TL;DR: It can be concluded that the newly constructed ELM-based ANSI models can solve the difficulties in tuning the acceleration coefficients of SPSO by the trial-and-error method for predicting the CBR of soils and be further applied to other real-time problems of geotechnical engineering.
Journal ArticleDOI
Predicting permeability of tight carbonates using a hybrid machine learning approach of modified equilibrium optimizer and extreme learning machine
Navid Kardani,Abidhan Bardhan,Shubham Gupta,Pijush Samui,Majidreza Nazem,Yanmei Zhang,Annan Zhou +6 more
TL;DR: Novel hybrid models based on combination of the modified version of the equilibrium optimizer (EO) and two conventional machine learning algorithms, namely extreme learning machine (ELM) and artificial neural network (ANN) are constructed to predict the permeability of tight carbonates.
Journal ArticleDOI
Efficient computational techniques for predicting the California bearing ratio of soil in soaked conditions
TL;DR: The proposed MARS-L model is very potential to be an alternate solution to estimate the CBR value in different phases of civil engineering projects, and has the most accurate prediction in predicting the soaked CBR at all stages.
References
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Journal ArticleDOI
Particle swarm optimization
TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
Journal ArticleDOI
ANFIS: adaptive-network-based fuzzy inference system
TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.
Journal ArticleDOI
On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other
Henry B. Mann,D. R. Whitney +1 more
TL;DR: In this paper, the authors show that the limit distribution is normal if n, n$ go to infinity in any arbitrary manner, where n = m = 8 and n = n = 8.
Journal ArticleDOI
A review on buildings energy consumption information
TL;DR: In this article, the authors analyzed available information concerning energy consumption in buildings, and particularly related to HVAC systems, and compared different types of building types and end uses in different countries.
Journal ArticleDOI
Sparse bayesian learning and the relevance vector machine
TL;DR: It is demonstrated that by exploiting a probabilistic Bayesian learning framework, the 'relevance vector machine' (RVM) can derive accurate prediction models which typically utilise dramatically fewer basis functions than a comparable SVM while offering a number of additional advantages.