Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings
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Lukumon O. Oyedele | Anuoluwapo O. Ajayi | Lukumon O. Oyedele | Olugbenga O. Akinade | Ashraf Ahmed | Olúgbénga O. Akinadé | X. J. Luo | Hakeem A. Owolabi | H. Owolabi | A. Ajayi | X. J. Luo | Ashraf A. Ahmed
[1] Frédéric Magoulès,et al. A review on the prediction of building energy consumption , 2012 .
[2] Xu Chen,et al. A hybrid teaching-learning artificial neural network for building electrical energy consumption prediction , 2018, Energy and Buildings.
[3] Melvin Robinson,et al. Prediction of residential building energy consumption: A neural network approach , 2016 .
[4] Sung-Bae Cho,et al. Predicting residential energy consumption using CNN-LSTM neural networks , 2019, Energy.
[5] Manuel P. Cuéllar,et al. Energy consumption forecasting based on Elman neural networks with evolutive optimization , 2018, Expert Syst. Appl..
[6] Simon Haykin,et al. Neural Networks: A Comprehensive Foundation , 1998 .
[7] Chirag Deb,et al. Forecasting diurnal cooling energy load for institutional buildings using Artificial Neural Networks , 2016 .
[8] Yong Shi,et al. A review of data-driven approaches for prediction and classification of building energy consumption , 2018 .
[9] Eric Wai Ming Lee,et al. Novel dynamic forecasting model for building cooling loads combining an artificial neural network and an ensemble approach , 2018, Applied Energy.
[10] Wenjie Gang,et al. Assessment of deep recurrent neural network-based strategies for short-term building energy predictions , 2019, Applied Energy.
[11] Kevin N. Gurney,et al. An introduction to neural networks , 2018 .
[12] Seung Hyun Cha,et al. A novel operation approach for the energy efficiency improvement of the HVAC system in office spaces through real-time big data analytics , 2020 .
[13] Maher AbuBaker. Data Mining Applications in Understanding Electricity Consumers’ Behavior: A Case Study of Tulkarm District, Palestine , 2019 .
[14] Philipp Geyer,et al. Deep-learning neural-network architectures and methods: Using component-based models in building-design energy prediction , 2018, Adv. Eng. Informatics.
[15] Nora El-Gohary,et al. A review of data-driven building energy consumption prediction studies , 2018 .
[16] Yongjun Sun,et al. Development of clustering-based sensor fault detection and diagnosis strategy for chilled water system , 2019, Energy and Buildings.
[17] Krithi Ramamritham,et al. A hybrid model for building energy consumption forecasting using long short term memory networks , 2020 .
[18] K. F. Fong,et al. Development of multi-supply-multi-demand control strategy for combined cooling, heating and power system primed with solid oxide fuel cell-gas turbine , 2017 .
[19] Xiaochen Zhao,et al. A hybrid model based on modified multi-objective cuckoo search algorithm for short-term load forecasting , 2019, Applied Energy.
[20] Jin Yang,et al. On-line building energy prediction using adaptive artificial neural networks , 2005 .
[21] Joao P. S. Catalao,et al. Analysis of the energy usage in university buildings: The case of aristotle university campus , 2015, 2015 Australasian Universities Power Engineering Conference (AUPEC).
[22] Anthony Denzer,et al. Energy efficient operation and modeling for greenhouses: A literature review , 2020 .
[23] Hooman Yarmand,et al. Assessment of thermal conductivity enhancement of nano-antifreeze containing single-walled carbon nanotubes: Optimal artificial neural network and curve-fitting , 2019, Physica A: Statistical Mechanics and its Applications.
[24] Andrew Kusiak,et al. A data-driven approach for steam load prediction in buildings , 2010 .
[25] Tanveer Ahmad,et al. Short and medium-term forecasting of cooling and heating load demand in building environment with data-mining based approaches , 2018 .
[26] Maurizio Cellura,et al. Assessment of building energy modelling studies to meet the requirements of the new Energy Performance of Buildings Directive , 2020 .
[27] Yi Liang,et al. Short term load forecasting based on feature extraction and improved general regression neural network model , 2019, Energy.
[28] Ming Zhong,et al. Energy consumption predicting model of VRV (Variable refrigerant volume) system in office buildings based on data mining , 2016 .
[29] Lukumon O. Oyedele,et al. Development of an IoT-based big data platform for day-ahead prediction of building heating and cooling demands , 2019, Adv. Eng. Informatics.
[30] X. J. Luo,et al. Development of integrated demand and supply side management strategy of multi-energy system for residential building application , 2019, Applied Energy.
[31] Radiša Jovanović,et al. Ensemble of various neural networks for prediction of heating energy consumption , 2015 .
[32] Federico Silvestro,et al. Electrical consumption forecasting in hospital facilities: An application case , 2015 .
[33] Fan Yang,et al. Effect of input variables on cooling load prediction accuracy of an office building , 2018 .
[34] Francesco D’Ettorre,et al. On the assessment and control optimisation of demand response programs in residential buildings , 2020, Renewable and Sustainable Energy Reviews.
[35] Manuel R. Arahal,et al. A prediction model based on neural networks for the energy consumption of a bioclimatic building , 2014 .
[36] Tanveer Ahmad,et al. Deployment of data-mining short and medium-term horizon cooling load forecasting models for building energy optimization and management , 2019, International Journal of Refrigeration.