Abstract
The main objective of this study is to examine the feasibility of several novel machine learning models and compare their network performance with the hybrid evolutionary based algorithm. In this regard, the best fit from the above machine learning-based solutions (i.e., known as M5Rules) were combined with the genetic algorithm (GA). These techniques were used to estimate the amount of heating load (HL) mitigation from an EEB (energy efficiency buildings) system. Then, the mentioned methods are utilized to identify a relationship between the input and output parameters of the EEB system. The amount of HL was taken as the essential output of the EEB system, while the input parameters were relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution. The predicted results for datasets from each of the above-mentioned models were evaluated according to several known statistical indices such as correlation coefficient (R2), mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE) as well as novel ranking systems of colour intensity rating and total ranking method. The M5Rules has been proposed as the best predictive network in this study and combined with the GA optimization algorithm. The results of the M5Rules–GA network indicated the R2, MAE, RMSE, RAE, and RRSE for the training and testing datasets were (0.9992, 0.0406, 0.0617, 6.2156, and 6.0189) and (0.9984, 0.0401, 0.0548, 6.4058, and 6.1785), respectively. Comparing to another non-hybrid proposed model with high accuracy (i.e., MLP Regressor with the R2 equal to 0.9876 and 0.9903 for the training and testing datasets, respectively), the results revealed that the M5Rules-GA network model could accomplish better performance.
Similar content being viewed by others
References
Moayedi H, Hayati S (2018) Artificial intelligence design charts for predicting friction capacity of driven pile in clay. Neural Comput Appl 31:1–17
Alnaqi AA, Moayedi H, Shahsavar A, Nguyen TK (2019) Prediction of energetic performance of a building integrated photovoltaic/thermal system thorough artificial neural network and hybrid particle swarm optimization models. Energy Convers Manag 183:137–148
Moayedi H, Mosallanezhad M, Nazir R (2017) Evaluation of maintained load test (MLT) and pile driving analyzer (PDA) in measuring bearing capacity of driven reinforced concrete piles. Soil Mech Found Eng 54:150–154
Moayedi H, Armaghani DJ (2017) Optimizing an ANN model with ICA for estimating bearing capacity of driven pile in cohesionless soil. Eng Comput 34:347–356
Nguyen H, Bui X-N, Tran Q-H, Le T-Q, Do N-H (2019) Evaluating and predicting blast-induced ground vibration in open-cast mine using ANN: a case study in Vietnam. SN Appl Sci 1:125. https://doi.org/10.1007/s42452-018-0136-2
Nguyen H, Bui X-N (2018) Predicting blast-induced air overpressure: a robust artificial intelligence system based on artificial neural networks and random forest. Nat Resour Res 29:1–15
Nguyen H, Bui X-N, Bui H-B, Mai N, Luan (2018) A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine, Vietnam. Neural Comput Appl 1–17
Hoang N, Xuan-Nam B, Quang-Hieu T, Ngoc-Luan M (2019) A new soft computing model for estimating and controlling blast-produced ground vibration based on hierarchical K-means clustering and cubist algorithms. Appl Soft Comput 77:376–386
Xuan-Nam B, Hoang N, Hai-An L, Hoang-Bac B, Ngoc-Hoan D (2019) Prediction of blast-induced air over-pressure in open-pit mine: assessment of different artificial intelligence techniques. Nat Resour Res 1–21
Gao W, Dimitrov D, Abdo H (2018) Tight independent set neighborhood union condition for fractional critical deleted graphs and ID deleted graphs. Discret Contin Dyn Syst-S 12(4&5):711–721
Gao W, Guirao JLG, Abdel-Aty M, Xi W (2019) An independent set degree condition for fractional critical deleted graphs. Discret Contin Dyn Syst-S 12:877–886
Gao W, Guirao JLG, Basavanagoud B, Wu J (2018) Partial multi-dividing ontology learning algorithm. Inf Sci 467:35–58
Gao W, Wang W, Dimitrov D, Wang Y (2018) Nano properties analysis via fourth multiplicative ABC indicator calculating. Arab J Chem 11:793–801
Gao W, Wu H, Siddiqui MK, Baig AQ (2018) Study of biological networks using graph theory. Saudi J Biol Sci 25:1212–1219
Muthusamy S, Manickam LP, Murugesan V, Muthukumaran C, Pugazhendhi A (2019) Pectin extraction from Helianthus annuus (sunflower) heads using RSM and ANN modelling by a genetic algorithm approach. Int J Biol Macromol 124:750–758
Safaei MR, Karimipour A, Abdollahi A, Truong Khang N (2018) The investigation of thermal radiation and free convection heat transfer mechanisms of nanofluid inside a shallow cavity by lattice Boltzmann method. Phys A Stat Mech Appl 509:515–535
Karimipour A, D’Orazio A, Goodarzi M (2018) Develop the lattice Boltzmann method to simulate the slip velocity and temperature domain of buoyancy forces of FMWCNT nanoparticles in water through a micro flow imposed to the specified heat flux. Phys A Stat Mech Appl 509:729–745
Goodarzi M, D’Orazio A, Keshavarzi A, Mousavi S, Karimipour A (2018) Develop the nano scale method of lattice Boltzmann to predict the fluid flow and heat transfer of air in the inclined lid driven cavity with a large heat source inside, two case studies: pure natural convection & mixed convection. Phys A Stat Mech Appl 509:210–233
Alrashed AAAA, Karimipour A, Bagherzadeh SA, Safaei MR, Afrand M (2018) Electro- and thermophysical properties of water-based nanofluids containing copper ferrite nanoparticles coated with silica: experimental data, modeling through enhanced ANN and curve fitting. Int J Heat Mass Transf 127:925–935
Gao W, Moayedi H, Shahsavar A (2019) The feasibility of genetic programming and ANFIS in prediction energetic performance of a building integrated photovoltaic thermal (BIPVT) system. Solar Energy 183:293–305
Almonacid F, Rus C, Perez-Higueras P, Hontoria L (2011) Calculation of the energy provided by a PV generator. Comparative study: conventional methods vs. artificial neural networks. Energy 36:375–384
Van-Duong D, Choi H-S (2018) Balance between the charge transfer resistance and diffusion impedance in a CNT/Pt counter electrode for highly efficient liquid-junction photovoltaic devices. Org Electron 58:159–166
Van-Duong D, Van-Tien B, Choi H-S (2018) Pt-coated cylindrical micropatterned honeycomb Petri dishes as an efficient TCO-free counter electrode in liquid junction photovoltaic devices. J Power Sources 376:41–45
Bao LQ, Thogiti S, Koyyada G, Kim JH (2019) Synthesis and photovoltaic performance of novel ullazine-based organic dyes for dye-sensitized solar cells. Jpn J Appl Phys 58:1–7
Shahsavar A, Khanmohammadi S, Karimipour A, Goodarzi M (2019) A novel comprehensive experimental study concerned synthesizes and prepare liquid paraffin-Fe3O4 mixture to develop models for both thermal conductivity & viscosity: a new approach of GMDH type of neural network. Int J Heat Mass Transf 131:432–441
Khan D, Khan A, Khan I, Ali F, ul Karim F, Tlili I (2019) Effects of relative magnetic field, chemical reaction, heat generation and newtonian heating on convection flow of Casson fluid over a moving vertical plate embedded in a porous medium. Sci Rep 9:400. https://doi.org/10.1038/s41598-018-36243-0
Yang W, Wen F, Wang K, Huang Y, Salam MA (2018) Modeling of a district heating system and optimal heat-power flow. Energies 11:929
Phong PT, Phuc NX, Nam PH, Chien NV, Dung DD, Linh PH (2018) Size-controlled heating ability of CoFe2O4 nanoparticles for hyperthermia applications. Phys B Condens Matter 531:30–34
Nam PH, Phuc NX, Linh PH, Lu LT, Manh DH, Phong PT, Lee I-J (2018) Effect of zinc on structure, optical and magnetic properties and magnetic heating efficiency of Mn1-xZnxFe2O4 nanoparticles. Phys B Condens Matter 550:428–435
Linh PH, Chien NV, Dung DD, Nam PH, Hoa DT, Anh NTN, Hong LV, Phuc NX, Phong PT (2018) Biocompatible nanoclusters of O-carboxymethyl chitosan-coated Fe3O4 nanoparticles: synthesis, characterization and magnetic heating efficiency. J Mater Sci 53:8887–8900
Garcia NP, Zubi G, Pasaoglu G, Dufo-Lopez R (2017) Photovoltaic thermal hybrid solar collector and district heating configurations for a Central European multi-family house. Energy Convers Manag 148:915–924
Rasmussen C (2006) CKI Williams Gaussian processes for machine. Learning MIT Press, Cambridge, UK
Rasmussen CE, Williams CK (2006) Gaussian processes for machine learning, 38. 2006. The MIT Press, Cambridge, pp 715–719
Bazi Y, Alajlan N, Melgani F, AlHichri H, Yager RR (2014) Robust estimation of water chlorophyll concentrations with gaussian process regression and IOWA aggregation operators. IEEE J Sel Top Appl Earth Obs Remote Sens 7:3019–3028
Buhmann MD (2003) Radial basis functions: theory and implementations. Cambridge University Press, Cambridge, UK
El-Bendary N, Elhariri E, Hazman M, Saleh SM, Hassanien AE (2016) Cultivation-time recommender system based on climatic conditions for newly reclaimed lands in Egypt. Procedia Comput Sci 96:110–119
Bayzid SM, Mohamed Y, Al-Hussein M (2016) Prediction of maintenance cost for road construction equipment: a case study. Can J Civ Eng 43:480–492
Sharma R, Kumar S, Maheshwari R (2015) Comparative analysis of classification techniques in data mining using different datasets. Int J Comput Sci Mob Comput 4:125–134
Holmes G, Hall M, Prank E (1999) Generating rule sets from model trees, Springer, Berlin, Heidelberg
Holland JH (1992) Genetic algorithms. Sci Am 267:66–73
Whitley D (1994) A genetic algorithm tutorial. Stat Comput 4:65–85
Houck CR, Joines J, Kay MG (1995) A genetic algorithm for function optimization: a Matlab implementation. Ncsu-ie tr 95:1–10
Ngoc Le C, Thanh-Phong D, Van Thanh Tien N (2018) An efficient hybrid approach of finite element method, artificial neural network-based multiobjective genetic algorithm for computational optimization of a linear compliant mechanism of nanoindentation tester. Math Probl Eng 2018:7070868
Qin S, Zhou Y-L, Cao H, Wahab MA (2018) Model updating in complex bridge structures using Kriging model ensemble with genetic algorithm. Ksce J Civ Eng 22:3567–3578
Tiachacht S, Bouazzouni A, Khatir S, Behtani A, Zhou YLM, Wahab MA (2018) Structural health monitoring of 3D frame structures using finite element modal analysis and genetic algorithm. J VibroEng 20:1272
Zaher Mundher Y, Haitham Abdulmohsin A, Minh-Tung T (2018) Beam-column joint shear prediction using hybridized deep learning neural network with genetic algorithm. In: Bui QB, Cajka R, Tran MT, Trinh TA, Yasar AUH, Wets G, Woloszyn M (eds), 2nd international conference on sustainable development in civil, urban and transportation engineering
Tsanas A, Xifara A (2012) Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy Build 49:560–567
Moayedi H, Mosallanezhad M, Mehrabi M, Safuan ARA, Biswajeet P (2018) Modification of landslide susceptibility mapping using optimized PSO-ANN technique. Eng Comput 36:1–18
Moayedi H (2018) Optimization of ANFIS with GA and PSO estimating α in driven shafts. Eng Comput 35:1–12
Moayedi H, Hayati S (2018) Modelling and optimization of ultimate bearing capacity of strip footing near a slope by soft computing methods. Appl Soft Comput 66:208–219
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Bui, XN., Moayedi, H. & Rashid, A.S.A. Developing a predictive method based on optimized M5Rules–GA predicting heating load of an energy-efficient building system. Engineering with Computers 36, 931–940 (2020). https://doi.org/10.1007/s00366-019-00739-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00366-019-00739-8