Estimating the Heating Load of Buildings for Smart City Planning Using a Novel Artificial Intelligence Technique PSO-XGBoost

[1]  Y. Hayakawa,et al.  Shallow and Deep-Seated Landslide Differentiation Using Support Vector Machines: A Case Study of the Chuetsu Area, Japan , 2015 .

[2]  Hwataik Han,et al.  Simplified dynamic neural network model to predict heating load of a building using Taguchi method , 2016 .

[3]  Xiuzhi Shi,et al.  Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines , 2012 .

[4]  N. Eskin,et al.  Analysis of annual heating and cooling energy requirements for office buildings in different climates in Turkey , 2008 .

[5]  Masoud Monjezi,et al.  Prediction of seismic slope stability through combination of particle swarm optimization and neural network , 2015, Engineering with Computers.

[6]  Hoang Nguyen,et al.  A comparison of advanced computational models and experimental techniques in predicting blast-induced ground vibration in open-pit coal mine , 2019, Acta Geophysica.

[7]  S. Beck,et al.  Using regression analysis to predict the future energy consumption of a supermarket in the UK , 2014 .

[8]  Kazem Zare,et al.  Optimal scheduling of heating and power hubs under economic and environment issues in the presence of peak load management , 2018 .

[9]  Dragan Mitić,et al.  Appraisal of soft computing methods for short term consumers' heat load prediction in district heating systems , 2015 .

[10]  Xuan-Nam Bui,et al.  Developing a predictive method based on optimized M5Rules–GA predicting heating load of an energy-efficient building system , 2019, Engineering with Computers.

[11]  Frédéric Magoulès,et al.  A review on the prediction of building energy consumption , 2012 .

[12]  David C. Carslaw,et al.  Random forest meteorological normalisation models for Swiss PM 10 trend analysis , 2018 .

[13]  Agnieszka Turek,et al.  Spatiotemporal Modeling of the Smart City Residents’ Activity with Multi-Agent Systems , 2019, Applied Sciences.

[14]  Tanveer Ahmad,et al.  Short and medium-term forecasting of cooling and heating load demand in building environment with data-mining based approaches , 2018 .

[15]  Hoang Nguyen,et al.  Prediction of Blast-induced Air Over-pressure in Open-Pit Mine: Assessment of Different Artificial Intelligence Techniques , 2019, Natural Resources Research.

[16]  Radiša Jovanović,et al.  Ensemble of various neural networks for prediction of heating energy consumption , 2015 .

[17]  Amaryllis Audenaert,et al.  Improving the energy performance of residential buildings: A literature review , 2015 .

[18]  Sébastien Da Veiga,et al.  Global sensitivity analysis with dependence measures , 2013, ArXiv.

[19]  Cardona Alzate,et al.  Predicción y selección de variables con bosques aleatorios en presencia de variables correlacionadas , 2020 .

[20]  Benjamin C. M. Fung,et al.  A decision tree method for building energy demand modeling , 2010 .

[21]  Myoung-Souk Yeo,et al.  Application of artificial neural network to predict the optimal start time for heating system in building , 2003 .

[22]  Xuan-Nam Bui,et al.  Prediction of ultimate bearing capacity through various novel evolutionary and neural network models , 2019, Engineering with Computers.

[23]  Masoud Monjezi,et al.  Classification and regression tree technique in estimating peak particle velocity caused by blasting , 2016, Engineering with Computers.

[24]  David R. Riley,et al.  Multi-linear Regression Models to Predict the Annual Energy Consumption of an Office Building with Different Shapes , 2015 .

[25]  Emanuele Borgonovo,et al.  A new uncertainty importance measure , 2007, Reliab. Eng. Syst. Saf..

[26]  Loke Kok Foong,et al.  Optimizing ANN models with PSO for predicting short building seismic response , 2019, Engineering with Computers.

[27]  Biswajeet Pradhan,et al.  Modification of landslide susceptibility mapping using optimized PSO-ANN technique , 2018, Engineering with Computers.

[28]  Shahaboddin Shamshirband,et al.  Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm , 2016 .

[29]  Yang Liu,et al.  An introduction to decision tree modeling , 2004 .

[30]  Aminaton Marto,et al.  Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization , 2014, Arabian Journal of Geosciences.

[31]  Zeyu Wang,et al.  A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models , 2017 .

[32]  Amin Shahsavar,et al.  Application of PSO–ANN modelling for predicting the exergetic performance of a building integrated photovoltaic/thermal system , 2019, Engineering with Computers.

[33]  Yu-Hsiu Lin,et al.  Design and Implementation of an IoT-Oriented Energy Management System Based on Non-Intrusive and Self-Organizing Neuro-Fuzzy Classification as an Electrical Energy Audit in Smart Homes , 2018, Applied Sciences.

[34]  Frédéric Kuznik,et al.  Fast and accurate district heating and cooling energy demand and load calculations using reduced-order modelling , 2019, Applied Energy.

[35]  Shahaboddin Shamshirband,et al.  Extreme learning machine for prediction of heat load in district heating systems , 2016 .

[36]  Hoang Nguyen,et al.  A new soft computing model for estimating and controlling blast-produced ground vibration based on Hierarchical K-means clustering and Cubist algorithms , 2019, Appl. Soft Comput..

[37]  Weiming Shen,et al.  Inverse blackbox modeling of the heating and cooling load in office buildings , 2017 .

[38]  Ling Xie.  The Heat load Prediction Model based on BP Neural Network-markov Model , 2017 .

[39]  Biswajeet Pradhan,et al.  A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS , 2013, Comput. Geosci..

[40]  X. Bui,et al.  Predicting Blast-Induced Air Overpressure: A Robust Artificial Intelligence System Based on Artificial Neural Networks and Random Forest , 2018, Natural Resources Research.

[41]  Carsten Drebenstedt,et al.  Prediction of Blast-Induced Ground Vibration in an Open-Pit Mine by a Novel Hybrid Model Based on Clustering and Artificial Neural Network , 2019, Natural Resources Research.

[42]  Carlos Rubio-Bellido,et al.  Comparison of linear regression and artificial neural networks models to predict heating and cooling energy demand, energy consumption and CO2 emissions , 2017 .

[43]  Kwang Ho Lee,et al.  Influence of raised floor on zone design cooling load in commercial buildings , 2010 .

[44]  X. Bui,et al.  Developing an XGBoost model to predict blast-induced peak particle velocity in an open-pit mine: a case study , 2019, Acta Geophysica.

[45]  Hossein Moayedi,et al.  Modelling and optimization of ultimate bearing capacity of strip footing near a slope by soft computing methods , 2018, Appl. Soft Comput..

[46]  Xiufeng Pang,et al.  Assessment of the Potential of High-Performance Buildings to Achieve Zero Energy: A Case Study , 2019 .

[47]  Pablo Aparicio-Ruiz,et al.  Applying Renewable Energy Technologies in an Integrated Optimization Method for Residential Building’s Design , 2019, Applied Sciences.

[48]  J. Friedman.  Stochastic gradient boosting , 2002 .

[49]  Masoud Monjezi,et al.  Forecasting blast-induced ground vibration developing a CART model , 2017, Engineering with Computers.

[50]  Hani S. Mitri,et al.  Feasibility of Random-Forest Approach for Prediction of Ground Settlements Induced by the Construction of a Shield-Driven Tunnel , 2017 .

[51]  Jian Zhou,et al.  Feasibility of Stochastic Gradient Boosting Approach for Evaluating Seismic Liquefaction Potential Based on SPT and CPT Case Histories , 2019, Journal of Performance of Constructed Facilities.

[52]  Athanasios Tsanas,et al.  Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools , 2012 .

[53]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[54]  Hoang Nguyen.  Support vector regression approach with different kernel functions for predicting blast-induced ground vibration: a case study in an open-pit coal mine of Vietnam , 2019, SN Applied Sciences.

[55]  Yang,et al.  Deterministic and Probabilistic Wind Power Forecasting Based on Bi-Level Convolutional Neural Network and Particle Swarm Optimization , 2019, Applied Sciences.

[56]  Javier Bajo,et al.  Intelligent system for lighting control in smart cities , 2016, Inf. Sci..