Prediction of heating and cooling loads based on light gradient boosting machine algorithms
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Sining Yun | Jiaxin Guo | Yao Meng | Ning He | Dongfu Ye | Zeni Zhao | Lingyun Jia | Liu Yang
[1] Sining Yun,et al. Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features , 2023, Eng. Appl. Artif. Intell..
[2] A. M. Abed,et al. The effect of carbon dioxide emissions on the building energy efficiency , 2022, Fuel.
[3] Jack C. P. Cheng,et al. Prediction and optimization of thermal comfort, IAQ and energy consumption of typical air-conditioned rooms based on a hybrid prediction model , 2022, Building and Environment.
[4] Xiao Liu,et al. Analysis and Visualization of Accidents Severity Based on Lightgbm-Tpe , 2022, SSRN Electronic Journal.
[5] S. Ergan,et al. Estimating power demand shaving capacity of buildings on an urban scale using extracted demand response profiles through machine learning models , 2022, Applied Energy.
[6] Shuli Liu,et al. Energy-saving potential prediction models for large-scale building: A state-of-the-art review , 2022, Renewable and Sustainable Energy Reviews.
[7] Jonghun Kim,et al. Data-driven approach to predicting the energy performance of residential buildings using minimal input data , 2022, Building and Environment.
[8] Wenting Zha,et al. Ultra-short-term power forecast method for the wind farm based on feature selection and temporal convolution network. , 2022, ISA transactions.
[9] Xinlei Zhou,et al. A data-driven strategy using long short term memory models and reinforcement learning to predict building electricity consumption , 2022, Applied Energy.
[10] Jian Yao,et al. Uncertainty Prediction of Energy Consumption in Buildings Under Stochastic Shading Adjustment , 2022, SSRN Electronic Journal.
[11] Jiansheng Peng,et al. Passive Fetal Movement Recognition Approaches Using Hyperparameter Tuned LightGBM Model and Bayesian Optimization , 2021, Comput. Intell. Neurosci..
[12] Kadir Amasyali,et al. Hybrid approach for energy consumption prediction: coupling data-driven and physical approaches , 2021, Energy and Buildings.
[13] H. Chai,et al. Real-time rainfall-runoff prediction using light gradient boosting machine coupled with singular spectrum analysis , 2021, Journal of Hydrology.
[14] Gang Xiao,et al. Light-stacking strengthened fusion based building energy consumption prediction framework via variable weight feature selection , 2021, Applied Energy.
[15] Jing Liu,et al. Comparison and Explanation of Forecasting Algorithms for Energy Time Series , 2021, Mathematics.
[16] Yang Liu,et al. Enhancing building energy efficiency using a random forest model: A hybrid prediction approach , 2021 .
[17] Hsi-Hsien Wei,et al. Identifying critical building-oriented features in city-block-level building energy consumption: A data-driven machine learning approach , 2021 .
[18] Joyjit Dhar. An adaptive intelligent diagnostic system to predict early stage of parkinson's disease using two-stage dimension reduction with genetically optimized lightgbm algorithm , 2021, Neural Computing and Applications.
[19] Mashud Rana,et al. A Data-driven Approach Based on Quantile Regression Forest to Forecast Cooling Load for Commercial Buildings , 2021, Sustainable Cities and Society.
[20] Zhijian Qu,et al. A combined genetic optimization with AdaBoost ensemble model for anomaly detection in buildings electricity consumption , 2021 .
[21] Hongjin Liu,et al. Overall grouting compactness detection of bridge prestressed bellows based on RF feature selection and the GA-SVM model , 2021 .
[22] Marijana Zekic-Susac,et al. Predicting energy cost of public buildings by artificial neural networks, CART, and random forest , 2021, Neurocomputing.
[23] Qiuhua Duan,et al. Space cooling energy usage prediction based on utility data for residential buildings using machine learning methods , 2021 .
[24] Ryozo Ooka,et al. Performance of neural network for indoor airflow prediction: sensitivity towards weight initialization , 2021 .
[25] C. Balaras,et al. On the short term forecasting of heat power for heating of building , 2021, Journal of Cleaner Production.
[26] Alireza Souri,et al. Hyper-parameter tuned light gradient boosting machine using memetic firefly algorithm for hand gesture recognition , 2021, Appl. Soft Comput..
[27] F. Isaia,et al. Enhancing energy efficiency and comfort in buildings through Model Predictive Control for dynamic façades with electrochromic glazing , 2021 .
[28] N. Wong,et al. Impact of façade design on indoor air temperatures and cooling loads in residential buildings in the tropical climate , 2021, Energy and Buildings.
[29] Ralph Evins,et al. Identifying whole-building heat loss coefficient from heterogeneous sensor data: An empirical survey of gray and black box approaches , 2021 .
[30] Rishee K. Jain,et al. Data-driven optimization of building layouts for energy efficiency , 2020, Energy and Buildings.
[31] Henry V. Burton,et al. Machine learning applications for building structural design and performance assessment: State-of-the-art review , 2021 .
[32] Sheng Liu,et al. Fault diagnosis of shipboard medium-voltage DC power system based on machine learning , 2021 .
[33] Alfonso Capozzoli,et al. Deep reinforcement learning to optimise indoor temperature control and heating energy consumption in buildings , 2020 .
[34] Ivan Glesk,et al. Machine learning modelling for predicting non-domestic buildings energy performance: A model to support deep energy retrofit decision-making , 2020 .
[35] Dongxiao Niu,et al. Short-term photovoltaic power generation forecasting based on random forest feature selection and CEEMD: A case study , 2020, Appl. Soft Comput..
[36] Ivan Glesk,et al. Data driven model improved by multi-objective optimisation for prediction of building energy loads , 2020, Automation in Construction.
[37] Rung-Ching Chen,et al. Selecting critical features for data classification based on machine learning methods , 2020, Journal of Big Data.
[38] Elie Azar,et al. Occupant-centric miscellaneous electric loads prediction in buildings using state-of-the-art deep learning methods , 2020 .
[39] Yones Khaledian,et al. Selecting appropriate machine learning methods for digital soil mapping , 2020, Applied Mathematical Modelling.
[40] Yucheng Chen,et al. Machine learning approaches to predict peak demand days of cardiovascular admissions considering environmental exposure , 2019, BMC Medical Informatics and Decision Making.
[41] R. Yao,et al. Particle removal efficiency of a household portable air cleaner in real-world residences: A single-blind cross-over field study , 2019, Energy and Buildings.
[42] P. G. Asteris,et al. Application of artificial neural networks for the predictionof the compressive strength of cement-based mortars , 2019 .
[43] Panagiotis G. Asteris,et al. Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures , 2019, Neural Computing and Applications.
[44] Liborio Cavaleri,et al. Krill herd algorithm-based neural network in structural seismic reliability evaluation , 2019 .
[45] Jan Carmeliet,et al. Building energy optimization: An extensive benchmark of global search algorithms , 2019, Energy and Buildings.
[46] Ahmad Salah,et al. Optimizing deep neural networks hyperparameter positions and values , 2019, J. Intell. Fuzzy Syst..
[47] N. Zhu,et al. Data and analytics for heating energy consumption of residential buildings: The case of a severe cold climate region of China , 2018, Energy and Buildings.
[48] Yunfei Shi,et al. Sample data selection method for improving the prediction accuracy of the heating energy consumption , 2018 .
[49] Frédéric Kuznik,et al. Modeling the heating and cooling energy demand of urban buildings at city scale , 2018 .
[50] Athanasios Tsanas,et al. Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools , 2012 .
[51] Mahesh Pal,et al. Random forest classifier for remote sensing classification , 2005 .
[52] Murat Dicleli,et al. Predicting the shear strength of reinforced concrete beams using artificial neural networks , 2004 .