Related references
Note: Only part of the references are listed.Data-driven modeling of general damping systems by k-means clustering and two-stage regression
Jia Guo et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2022)
Multi-objective optimization for improved project management: Current status and future directions
Kai Guo et al.
AUTOMATION IN CONSTRUCTION (2022)
Energy consumption prediction of appliances using machine learning and multi-objective binary grey wolf optimization for feature selection
Dorin Moldovan et al.
APPLIED SOFT COMPUTING (2021)
Multi-objective optimization for limiting tunnel-induced damages considering uncertainties
Limao Zhang et al.
RELIABILITY ENGINEERING & SYSTEM SAFETY (2021)
A deep learning framework for building energy consumption forecast
Nivethitha Somu et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2021)
An effective multi-model fusion method for EEG-based sleep stage classification
Panfeng An et al.
KNOWLEDGE-BASED SYSTEMS (2021)
Machine learning for occupant-behavior-sensitive cooling energy consumption prediction in office buildings
Kadir Amasyali et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2021)
Human Trajectory Prediction Using Similarity-Based Multi-Model Fusion
Golnaz Habibi et al.
IEEE ROBOTICS AND AUTOMATION LETTERS (2021)
Multi-step short-term wind speed prediction based on integrated multi-model fusion
Zhongda Tian et al.
APPLIED ENERGY (2021)
Privileged information-driven random network based non-iterative integration model for building energy consumption prediction
Hongchang Sun et al.
APPLIED SOFT COMPUTING (2021)
A combined forecasting model for time series: Application to short-term wind speed forecasting
Zhenkun Liu et al.
APPLIED ENERGY (2020)
Energy performance optimisation of building envelope retrofit through integrated orthogonal arrays with data envelopment analysis
Hong Xian Li et al.
RENEWABLE ENERGY (2020)
BIM log mining: Learning and predicting design commands
Yue Pan et al.
AUTOMATION IN CONSTRUCTION (2020)
Prediction of household electricity consumption and effectiveness of concerted intervention strategies based on occupant behaviour and personality traits
Meng Shen et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2020)
ATMFN: Adaptive-Threshold-Based Multi-Model Fusion Network for Compressed Face Hallucination
Kui Jiang et al.
IEEE TRANSACTIONS ON MULTIMEDIA (2020)
Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings
X. J. Luo et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2020)
Prediction of CO2 Solubility in Ionic Liquids Based on Multi-Model Fusion Method
Luyue Xia et al.
PROCESSES (2019)
Benchmarking air-conditioning energy performance of residential rooms based on regression and clustering techniques
Yuren Zhou et al.
APPLIED ENERGY (2019)
Gradient boosting machine for modeling the energy consumption of commercial buildings
Samir Touzani et al.
ENERGY AND BUILDINGS (2018)
Valuation of energy efficient certificates in buildings
Limao Zhang et al.
ENERGY AND BUILDINGS (2018)
Random Forest based hourly building energy prediction
Zeyu Wang et al.
ENERGY AND BUILDINGS (2018)
Random forest based thermal comfort prediction from gender-specific physiological parameters using wearable sensing technology
Tanaya Chaudhuri et al.
ENERGY AND BUILDINGS (2018)
A novel ensemble learning approach to support building energy use prediction
Zeyu Wang et al.
ENERGY AND BUILDINGS (2018)
A review of data-driven building energy consumption prediction studies
Kadir Amasyali et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2018)
A Similarity Regression Fusion Model for Integrating Multi-Omics Data to Identify Cancer Subtypes
Yang Guo et al.
GENES (2018)
Applying support vector machines to predict building energy consumption in China
Zhitong Ma et al.
CLEANER ENERGY FOR CLEANER CITIES (2018)
Modelling and experimental performance analysis of solar-assisted ground source heat pump system
Hikmet Esen et al.
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE (2017)
Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption
Muhammad Waseem Ahmad et al.
ENERGY AND BUILDINGS (2017)
Analysis of typical public building energy consumption in northern China
Hongting Ma et al.
ENERGY AND BUILDINGS (2017)
Energy consumption and efficiency technology measures in European non-residential buildings
Delia D'Agostino et al.
ENERGY AND BUILDINGS (2017)
A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models
Zeyu Wang et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2017)
Machine learning approaches for estimating commercial building energy consumption
Caleb Robinson et al.
APPLIED ENERGY (2017)
Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings
Yongbao Chen et al.
APPLIED ENERGY (2017)
Identifying the influential features on the regional energy use intensity of residential buildings based on Random Forests
Jun Ma et al.
APPLIED ENERGY (2016)
Building energy modeling (BEM) using clustering algorithms and semi-supervised machine learning approaches
Hariharan Naganathan et al.
AUTOMATION IN CONSTRUCTION (2016)
Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade
Xiaodong Cao et al.
ENERGY AND BUILDINGS (2016)
Building energy metering and environmental monitoring - A state-of-the-art review and directions for future research
Muhammad Waseem Ahmad et al.
ENERGY AND BUILDINGS (2016)
Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data
David Hsu
APPLIED ENERGY (2015)
Energy efficiency index as an indicator for measuring building energy performance: A review
Nur Najihah Abu Bakar et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2015)
Clustering time-series energy data from smart meters
Alexander Lavin et al.
ENERGY EFFICIENCY (2015)
Short-term load forecasting using a kernel-based support vector regression combination model
JinXing Che et al.
APPLIED ENERGY (2014)
Multi-model prediction and simulation of residential building energy in urban areas of Chongqing, South West China
Shazia Farzana et al.
ENERGY AND BUILDINGS (2014)
A prediction model based on neural networks for the energy consumption of a bioclimatic building
R. Mena et al.
ENERGY AND BUILDINGS (2014)
A new methodology for building energy performance benchmarking: An approach based on intelligent clustering algorithm
Xuefeng Gao et al.
ENERGY AND BUILDINGS (2014)
Root mean square error (RMSE) or mean absolute error (MAE)? - Arguments against avoiding RMSE in the literature
T. Chai et al.
GEOSCIENTIFIC MODEL DEVELOPMENT (2014)
Towards nearly zero-energy buildings: The state-of-art of national regulations in Europe
Eleonora Annunziata et al.
ENERGY (2013)
Solutions to reduce energy consumption in the management of large buildings
Antonio Colmenar-Santos et al.
ENERGY AND BUILDINGS (2013)
Multiple regression model for fast prediction of the heating energy demand
Tiberiu Catalina et al.
ENERGY AND BUILDINGS (2013)
Validation of building energy modeling tools under idealized and realistic conditions
Emily M. Ryan et al.
ENERGY AND BUILDINGS (2012)
A review on the prediction of building energy consumption
Hai-xiang Zhao et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2012)
Short-Term Load Forecasting for Electric Power Systems Using the PSO-SVR and FCM Clustering Techniques
Pan Duan et al.
ENERGIES (2011)
Using climate classification to evaluate building energy performance
Wen-Shing Lee et al.
ENERGY (2011)
A systematic procedure to study the influence of occupant behavior on building energy consumption
Zhun Yu et al.
ENERGY AND BUILDINGS (2011)
A decision tree method for building energy demand modeling
Zhun Yu et al.
ENERGY AND BUILDINGS (2010)
Data clustering: 50 years beyond K-means
Anil K. Jain
PATTERN RECOGNITION LETTERS (2010)
Black-box models for fault detection and performance monitoring of buildings
Dirk Jacob et al.
JOURNAL OF BUILDING PERFORMANCE SIMULATION (2010)
Prediction of building energy consumption by using artificial neural networks
Betul Bektas Ekici et al.
ADVANCES IN ENGINEERING SOFTWARE (2009)
Applying support vector machine to predict hourly cooling load in the building
Qiong Li et al.
APPLIED ENERGY (2009)
Electricity consumption forecasting in Italy using linear regression models
Vincenzo Bianco et al.
ENERGY (2009)
Development and validation of regression models to predict monthly heating demand for residential buildings
Tiberiu Catalina et al.
ENERGY AND BUILDINGS (2008)
Using intelligent clustering techniques to classify the energy performance of school buildings
M. Santamouris et al.
ENERGY AND BUILDINGS (2007)
Comparisons among clustering techniques for electricity customer classification
G Chicco et al.
IEEE TRANSACTIONS ON POWER SYSTEMS (2006)
Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance
CJ Willmott et al.
CLIMATE RESEARCH (2005)
Applying support vector machines to predict building energy consumption in tropical region
B Dong et al.
ENERGY AND BUILDINGS (2005)