Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system

Heating and cooling energy trends and drivers in buildings

Ürge-Vorsatz, D., Cabeza, L. F., Serrano, S., Barreneche, C., & Petrichenko, K. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2014.08.039 · Cited by 1,101 · see citing works

Day-ahead wind speed forecasting using f-ARIMA models

Kavasseri, R., & Seetharaman, K. (2008) · Renewable Energy · doi:10.1016/j.renene.2008.09.006 · Cited by 960 · see citing works

A review of data-driven approaches for prediction and classification of building energy consumption

Wei, Y., Zhang, X., Shi, Y., Xia, L., Pan, S., Wu, J., Han, M., & Zhao, X. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.09.108 · Cited by 811 · see citing works

Deep learning based ensemble approach for probabilistic wind power forecasting

Wang, H., Li, G., Wang, G., Peng, J., Jiang, H. Q., & Liu, Y. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.11.111 · Cited by 754 · see citing works

Analysis and evaluation of five short-term load forecasting techniques

Moghram, I. S., & Rahman, S. (1989) · IEEE Transactions on Power Systems · doi:10.1109/59.41700 · Cited by 709 · see citing works

A short-term building cooling load prediction method using deep learning algorithms

Fan, C., Xiao, F., & Zhao, Y. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.03.064 · Cited by 695 · see citing works

A review and analysis of regression and machine learning models on commercial building electricity load forecasting

Yildiz, B., Bilbao, J. I., & Sproul, A. B. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.02.023 · Cited by 563 · see citing works

Deep belief network based deterministic and probabilistic wind speed forecasting approach

Wang, H., Wang, G. B., Li, G. Q., Peng, J.-C., & Liu, Y. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.08.108 · Cited by 495 · see citing works

Applying support vector machine to predict hourly cooling load in the building

Li, Q., Meng, Q., Cai, J., Yoshino, H., & Mochida, A. (2009) · Applied Energy · doi:10.1016/j.apenergy.2008.11.035 · Cited by 485 · see citing works

Deep Neural Network Based Demand Side Short Term Load Forecasting

Ryu, S., Noh, J.-K., & Kim, H. (2016) · Energies · doi:10.3390/en10010003 · Cited by 414 · see citing works

Deterministic and probabilistic forecasting of photovoltaic power based on deep convolutional neural network

Wang, H., Yi, H., Peng, J., Wang, G., Liu, Y., Jiang, H. Q., & Liu, W. (2017) · Energy Conversion and Management · doi:10.1016/j.enconman.2017.10.008 · Cited by 407 · see citing works

Optimal Prediction Intervals of Wind Power Generation

Wan, C., Xu, Z., Pinson, P., Dong, Z. Y., & Wong, K. P. (2014) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2013.2288100 · Cited by 326 · see citing works

A data-driven multi-model methodology with deep feature selection for short-term wind forecasting

Feng, C., Cui, M., Hodge, B. S., & Zhang, J. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.01.043 · Cited by 320 · see citing works

Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression

Zhu, B., Han, D., Wang, P., Wu, Z., Zhang, T., & Wei, Y.-M. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.01.076 · Cited by 307 · see citing works

Deep belief network based electricity load forecasting: An analysis of Macedonian case

Dedinec, A., Filiposka, S., Dedinec, A., & Kocarev, L. (2016) · Energy · doi:10.1016/j.energy.2016.07.090 · Cited by 302 · see citing works

Forecasting diurnal cooling energy load for institutional buildings using Artificial Neural Networks

Deb, C., Eang, L. S., Yang, J., & Santamouris, M. (2015) · Energy and Buildings · doi:10.1016/j.enbuild.2015.12.050 · Cited by 283 · see citing works

Predictive Deep Boltzmann Machine for Multiperiod Wind Speed Forecasting

Zhang, C.-Y., Chen, C. L. P., Gan, M., & Chen, L. (2015) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2015.2434387 · Cited by 215 · see citing works

A Hybrid Intelligent Model for Deterministic and Quantile Regression Approach for Probabilistic Wind Power Forecasting

Haque, A. U., Nehrir, M. H., & Mandal, P. (2014) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2014.2299801 · Cited by 205 · see citing works

Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks

Zameer, A., Arshad, J., Khan, A., & Raja, M. A. Z. (2016) · Energy Conversion and Management · doi:10.1016/j.enconman.2016.12.032 · Cited by 205 · see citing works

Building hourly thermal load prediction using an indexed ARX model

Yun, K., Luck, R., Mago, P. J., & Cho, H. (2012) · Energy and Buildings · doi:10.1016/j.enbuild.2012.08.007 · Cited by 202 · see citing works

History and development of validation with the ESP-r simulation program

Strachan, P. A., Kokogiannakis, G., & Macdonald, I. (2006) · Building and Environment · doi:10.1016/j.buildenv.2006.06.025 · Cited by 177 · see citing works

Studying the potential of energy saving through vertical greenery systems: Using EnergyPlus simulation program

Dahanayake, K. C., & Chow, C. L. (2016) · Energy and Buildings · doi:10.1016/j.enbuild.2016.12.002 · Cited by 169 · see citing works

Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia

Pinaya, W. H. L., Gadelha, A., Doyle, O., Noto, C. D. S., Zugman, A., Cordeiro, Q., Jackowski, A. P., Bressan, R. A., & Sato, J. R. (2016) · Scientific Reports · doi:10.1038/srep38897 · Cited by 163 · see citing works

Crude oil price analysis and forecasting using wavelet decomposed ensemble model

He, K., Yu, L., & Lai, K. K. (2012) · Energy · doi:10.1016/j.energy.2012.07.055 · Cited by 146 · see citing works

Cooling-load prediction by the combination of rough set theory and an artificial neural-network based on data-fusion technique

Hou, Z., Lian, Z., Yao, Y., & Yuan, X. (2005) · Applied Energy · doi:10.1016/j.apenergy.2005.08.006 · Cited by 141 · see citing works

Wind Power Forecasting Using Neural Network Ensembles With Feature Selection

Song, L., Wang, P., & Goel, L. (2015) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2015.2441747 · Cited by 127 · see citing works

Effect of input variables on cooling load prediction accuracy of an office building

Ding, Y. Y., Zhang, Q., Yuan, T., & Yang, F. (2017) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2017.09.007 · Cited by 121 · see citing works

Research on short-term and ultra-short-term cooling load prediction models for office buildings

Ding, Y., Zhang, Q., & Yuan, T. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.08.077 · Cited by 121 · see citing works

Improving short term load forecast accuracy via combining sister forecasts

Nowotarski, J., Liu, B., Weron, R., & Hong, T. (2016) · Energy · doi:10.1016/j.energy.2015.12.142 · Cited by 117 · see citing works

Moments of the censored and truncated bivariate normal distribution

Muthén, B. (1990) · British Journal of Mathematical and Statistical Psychology · doi:10.1111/j.2044-8317.1990.tb00930.x · Cited by 114 · see citing works

An improved office building cooling load prediction model based on multivariable linear regression

Qiang, G., Zhe, T., Yan, D., & Neng, Z. (2015) · Energy and Buildings · doi:10.1016/j.enbuild.2015.08.041 · Cited by 108 · see citing works

Application of a hybrid quantized Elman neural network in short-term load forecasting

Li, P., Li, Y., Xiong, Q., Chai, Y., & Zhang, Y. (2013) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2013.10.020 · Cited by 101 · see citing works

Hourly cooling load forecasting using time-indexed ARX models with two-stage weighted least squares regression

Guo, Y., Nazarian, E., Ko, J., & Rajurkar, K. P. (2014) · Energy Conversion and Management · doi:10.1016/j.enconman.2013.12.060 · Cited by 87 · see citing works

Estimating hourly cooling load in commercial buildings using a thermal network model and electricity submetering data

Ji, Y., Xu, P., Duan, P., & Lu, X. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.02.036 · Cited by 85 · see citing works

Hourly cooling load prediction by a combined forecasting model based on Analytic Hierarchy Process

Yao, Y., Lian, Z., Liu, S., & Hou, Z. (2004) · International Journal of Thermal Sciences · doi:10.1016/j.ijthermalsci.2004.02.009 · Cited by 79 · see citing works

Development of Matlab-TRNSYS co-simulator for applying predictive strategy planning models on residential house HVAC system

Alibabaei, N., Fung, A. S., & Raahemifar, K. (2016) · Energy and Buildings · doi:10.1016/j.enbuild.2016.05.084 · Cited by 69 · see citing works

Residential air-conditioner usage in China and efficiency standardization

Wu, J., Liu, C., Li, H., Dong, O., Jianhong, C., Wang, Y., & You, S. (2016) · Energy · doi:10.1016/j.energy.2016.11.038 · Cited by 65 · see citing works

Robust Planning of Electric Vehicle Charging Facilities With an Advanced Evaluation Method

Wang, G., Zhang, X., Wang, H., Peng, J., Jiang, H. Q., Liu, Y., Wu, C., Xu, Z., & Liu, W. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2748342 · Cited by 58 · see citing works

Deep Learning Framework with Confused Sub-Set Resolution Architecture for Automatic Arabic Diacritization

Rashwan, M., Sallab, A. A. A., Raafat, H. M., & Rafea, A. (2015) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2015.2395255 · Cited by 46 · see citing works

Electric load forecasting for large office building based on radial basis function neural network

Mai, W., Chung, C. Y., Wu, T., & Wong, W. C. (2014) · IEEE Power Engineering Society General Meeting · doi:10.1109/pesgm.2014.6939378 · Cited by 45 · see citing works

GPNBI-inspired MOSFA for Pareto operation optimization of integrated energy system

Wang, H., Zhang, R., Peng, J., Wang, G., Liu, Y., Jiang, H. Q., & Liu, W. (2017) · Energy Conversion and Management · doi:10.1016/j.enconman.2017.09.005 · Cited by 42 · see citing works

An evolutionary artificial neural network ensemble model for estimating hourly direct normal irradiances from meteosat imagery

Linares-Rodríguez, Á., Quesada-Ruiz, S., Pozo‐Vázquez, D., & Tovar‐Pescador, J. (2015) · Energy · doi:10.1016/j.energy.2015.08.043 · Cited by 25 · see citing works

Experimental and numerical investigations of the energy efficiency of conventional air conditioning systems in cooling mode and comfort assurance in office buildings

Sârbu, I., & Adam, M. (2014) · Energy and Buildings · doi:10.1016/j.enbuild.2014.09.022 · Cited by 24 · see citing works

Prediction model of Cooling Load considering time-lag for preemptive action in buildings

Lim, H. S., & Kim, G. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.06.019 · Cited by 19 · see citing works

Cooling load forecasting for chiller plants using similar day based wavelet neural networks

Thota, P. K., Dani, A. P., Luh, P. B., & Gupta, S. (2015) · International Conference on Complex Systems Engineering (ICCSE) · doi:10.1109/complexsys.2015.7385978 · Cited by 5 · see citing works

Development of a General Package for Resolution of Uncertainty-Related Issues in Reservoir Engineering

Xue, L., Dai, C., & Wang, L. (2017) · Energies · doi:10.3390/en10020197 · Cited by 3 · see citing works

A review of deep learning for renewable energy forecasting

Wang, H., Lei, Z., Zhang, X., Zhou, B., & Peng, J. (2019) · Energy Conversion and Management · doi:10.1016/j.enconman.2019.111799 · Cited by 1,211 · see citing works

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

Mosavi, A. H., Salimi, M., Ardabili, S., Rabczuk, T., Shamshirband, S., & Várkonyi-Kóczy, A. R. (2019) · Energies · doi:10.3390/en12071301 · Cited by 582 · see citing works

A hybrid deep learning model for short-term PV power forecasting

Li, P., Zhou, K., Lu, X., & Yang, S. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.114216 · Cited by 541 · see citing works

Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects

Benti, N. E., Chaka, M. D., & Semie, A. G. (2023) · Sustainability · doi:10.3390/su15097087 · Cited by 445 · see citing works

Building energy performance forecasting: A multiple linear regression approach

Ciulla, G., & D’Amico, A. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.113500 · Cited by 363 · see citing works

Machine Learning and Deep Learning in Energy Systems: A Review

Forootan, M. M., Larki, I., Zahedi, R., & Ahmadi, A. (2022) · Sustainability · doi:10.3390/su14084832 · Cited by 373 · see citing works

A comprehensive review on deep learning approaches for short-term load forecasting

Eren, Y., & Küçükdemiral, İ. B. (2023) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2023.114031 · Cited by 333 · see citing works

Study on deep reinforcement learning techniques for building energy consumption forecasting

Liu, T., Tan, Z., Xu, C., Chen, H., & Li, Z. (2019) · Energy and Buildings · doi:10.1016/j.enbuild.2019.109675 · Cited by 278 · see citing works

Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review

Runge, J., & Zmeureanu, R. (2019) · Energies · doi:10.3390/en12173254 · Cited by 269 · see citing works

Building energy prediction using artificial neural networks: A literature survey

Lu, C., Li, S., & Lu, Z. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.111718 · Cited by 239 · see citing works

Intelligent techniques for forecasting electricity consumption of buildings

Amber, K. P., Ahmad, R., Aslam, M. W., Kousar, A., Usman, M., & Khan, M. S. (2018) · Energy · doi:10.1016/j.energy.2018.05.155 · Cited by 219 · see citing works

Tackling climate change with machine learning

Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works

A new hybrid ensemble deep reinforcement learning model for wind speed short term forecasting

Liu, H., Yu, C., Wu, H., Duan, Z., & Yan, G. (2020) · Energy · doi:10.1016/j.energy.2020.117794 · Cited by 186 · see citing works

A hybrid deep transfer learning strategy for short term cross-building energy prediction

Fang, X., Gong, G., Li, G., Chun, L., Li, W., & Peng, P. (2020) · Energy · doi:10.1016/j.energy.2020.119208 · Cited by 181 · see citing works

Data driven parallel prediction of building energy consumption using generative adversarial nets

Tian, C., Li, C., Zhang, G., & Lv, Y. (2019) · Energy and Buildings · doi:10.1016/j.enbuild.2019.01.034 · Cited by 168 · see citing works

Energy Forecasting: A Comprehensive Review of Techniques and Technologies

Mystakidis, A., Koukaras, P., Tsalikidis, N., Ioannidis, D., & Tjortjis, C. (2024) · Energies · doi:10.3390/en17071662 · Cited by 167 · see citing works

Multi-objective optimization of passive energy efficiency measures for net-zero energy building in Morocco

Abdou, N., Mghouchi, Y. E., Hamdaoui, S., Asri, N. E., & Mouqallid, M. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.108141 · Cited by 164 · see citing works

Dual stream network with attention mechanism for photovoltaic power forecasting

Khan, Z. A., Hussain, T., & Baik, S. W. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.120916 · Cited by 156 · see citing works

A multi-energy load prediction model based on deep multi-task learning and ensemble approach for regional integrated energy systems

Xuan, W., Wang, S., Qianyu, Z., Shaomin, W., & Liwei, F. (2020) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2020.106583 · Cited by 141 · see citing works

Cooling load prediction and optimal operation of HVAC systems using a multiple nonlinear regression model

Fan, C., & Ding, Y. (2019) · Energy and Buildings · doi:10.1016/j.enbuild.2019.05.043 · Cited by 129 · see citing works

A Review on Machine Learning Strategies for Real-World Engineering Applications

Jhaveri, R. H., Revathi, A. R., Ramana, K., Raut, R., & Dhanaraj, R. K. (2022) · Mobile Information Systems · doi:10.1155/2022/1833507 · Cited by 124 · see citing works

Improved Deep Belief Network for Short-Term Load Forecasting Considering Demand-Side Management

Kong, X., Li, C., Zheng, F., & Wang, C. (2019) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2019.2943972 · Cited by 118 · see citing works

Advances of machine learning in multi-energy district communities‒ mechanisms, applications and perspectives

Zhou, Y. (2022) · Energy and AI · doi:10.1016/j.egyai.2022.100187 · Cited by 119 · see citing works

Intelligent deep learning techniques for energy consumption forecasting in smart buildings: a review

Mathumitha, R., Rathika, P., & Manimala, K. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10660-8 · Cited by 115 · see citing works

A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings

Runge, J., & Zmeureanu, R. (2021) · Energies · doi:10.3390/en14030608 · Cited by 113 · see citing works

Solar irradiance forecasting based on direct explainable neural network

Wang, H., Ren, C., Zhou, B., Aziz, S., Qin, B., Voropai, N., Gan, L., & Алексеевич, Б. Е. (2020) · Energy Conversion and Management · doi:10.1016/j.enconman.2020.113487 · Cited by 103 · see citing works

Multi-energy net load forecasting for integrated local energy systems with heterogeneous prosumers

Zhou, B., Meng, Y., Huang, W., Wang, H., Deng, L., Huang, S., & Wei, J. (2020) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2020.106542 · Cited by 99 · see citing works

Energy Consumption Prediction Using Machine Learning; A ReviewPreprint

Mosavi, A. H., & Bahmani, A. (2019) · Preprints.org · doi:10.20944/preprints201903.0131.v1 · Cited by 99 · see citing works

Machine Learning for Harnessing Thermal Energy: From Materials Discovery to System Optimization

Li, M., Dai, L., & Hu, Y. (2022) · ACS Energy Letters · doi:10.1021/acsenergylett.2c01836 · Cited by 94 · see citing works

Improving cooling load prediction reliability for HVAC system using Monte-Carlo simulation to deal with uncertainties in input variables

Fan, C., Liao, Y., Zhou, G. Y., Zhou, X., & Ding, Y. (2020) · Energy and Buildings · doi:10.1016/j.enbuild.2020.110372 · Cited by 86 · see citing works

Data-Driven Tools for Building Energy Consumption Prediction: A Review

Olu-Ajayi, R., Alaka, H., Owolabi, H. A., Akanbi, L., & Ganiyu, S. A. (2023) · Energies · doi:10.3390/en16062574 · Cited by 83 · see citing works

Echo state network based ensemble approach for wind power forecasting

Wang, H., Lei, Z., Liu, Y., Peng, J., & Liu, J. (2019) · Energy Conversion and Management · doi:10.1016/j.enconman.2019.112188 · Cited by 82 · see citing works

Strategic potential of multi-energy system towards carbon neutrality: A forward-looking overview

Alabi, T. M., Agbajor, F. D., Yang, Z., Lu, L., & Ogungbile, A. J. (2022) · Energy and Built Environment · doi:10.1016/j.enbenv.2022.06.007 · Cited by 82 · see citing works

A generic prediction interval estimation method for quantifying the uncertainties in ultra-short-term building cooling load prediction

Zhang, C., Zhao, Y., Fan, C., Li, T., Zhang, X., & Li, J. (2020) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2020.115261 · Cited by 79 · see citing works

A Hybrid Forecasting Method for Solar Output Power Based on Variational Mode Decomposition, Deep Belief Networks and Auto-Regressive Moving Average

Xie, T., Zhang, G., Liu, H., Liu, F., & Du, P. (2018) · Applied Sciences · doi:10.3390/app8101901 · Cited by 79 · see citing works

Machine Learning Based Hybrid System for Imputation and Efficient Energy Demand Forecasting

Khan, P. W., Byun, Y.-C., Lee, S.-J., & Park, N. (2020) · Energies · doi:10.3390/en13112681 · Cited by 78 · see citing works

Wind Speed Forecasting Based on Extreme Gradient Boosting

Ren, C., Xie, S., Wang, B., Yang, R., Xu, D., & He, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3025967 · Cited by 75 · see citing works

Advances in Deep Learning Techniques for Short-term Energy Load Forecasting Applications: A Review

Chandrasekaran, R., & Paramasivan, S. K. (2024) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-024-10155-x · Cited by 73 · see citing works

Accurate forecasting of building energy consumption via a novel ensembled deep learning method considering the cyclic feature

Zhang, G., Tian, C., Li, C., Zhang, J. J., & Zuo, W. (2020) · Energy · doi:10.1016/j.energy.2020.117531 · Cited by 70 · see citing works

Forecasting performance comparison of two hybrid machine learning models for cooling load of a large-scale commercial building

Zhou, X., Xuehui, Z., Lie-quan, L., Zubing, F., Junwei, Y., & Dongmei, P. (2018) · Journal of Building Engineering · doi:10.1016/j.jobe.2018.10.006 · Cited by 69 · see citing works

PSO-Stacking improved ensemble model for campus building energy consumption forecasting based on priority feature selection

Cao, Y., Liu, G., Sun, J., Bavirisetti, D. P., & Xiao, G. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2023.106589 · Cited by 67 · see citing works

A new energy consumption prediction method for chillers based on GraphSAGE by combining empirical knowledge and operating data

Chen, Z., Deng, Q., Ren, H., Zhao, Z., Peng, T., Yang, C., & Gui, W. (2022) · Applied Energy · doi:10.1016/j.apenergy.2021.118410 · Cited by 65 · see citing works

ESAENARX and DE-RELM: Novel schemes for big data predictive analytics of electricity load and price

Mujeeb, S., & Javaid, N. (2019) · Sustainable Cities and Society · doi:10.1016/j.scs.2019.101642 · Cited by 61 · see citing works

Analysis of hourly cooling load prediction accuracy with data-mining approaches on different training time scales

Fan, C., Ding, Y., & Liao, Y. (2019) · Sustainable Cities and Society · doi:10.1016/j.scs.2019.101717 · Cited by 61 · see citing works

Developing a multi-level intrusion detection system using hybrid-DBN

Süzen, A. A. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02271-w · Cited by 58 · see citing works

A CNN‐BiLSTM based deep learning model for mid‐term solar radiation prediction

Rai, A., Shrivastava, A., & Jana, K. C. (2020) · International Transactions on Electrical Energy Systems · doi:10.1002/2050-7038.12664 · Cited by 58 · see citing works

A Review of Energy Consumption Forecasting in Smart Buildings: Methods, Input Variables, Forecasting Horizon and Metrics

Mariano-Hernández, D., Hernández-Callejo, L., García, F. S., Duque-Pérez, Ó., & Zorita-Lamadrid, Á. (2020) · Applied Sciences · doi:10.3390/app10238323 · Cited by 56 · see citing works

Research on a novel photovoltaic power forecasting model based on parallel long and short-term time series network

Li, G., Ding, C., Zhao, N., Wei, J., Guo, Y., Meng, C., Huang, K., & Zhu, R. (2024) · Energy · doi:10.1016/j.energy.2024.130621 · Cited by 52 · see citing works

A novel ensemble method for hourly residential electricity consumption forecasting by imaging time series

Zhang, G., & Guo, J. (2020) · Energy · doi:10.1016/j.energy.2020.117858 · Cited by 51 · see citing works

Elastic weight consolidation-based adaptive neural networks for dynamic building energy load prediction modeling

Zhou, Y., Tian, X., Zhang, C., Zhao, Y., & Li, T. (2022) · Energy and Buildings · doi:10.1016/j.enbuild.2022.112098 · Cited by 51 · see citing works

Modal decomposition based ensemble learning for ground source heat pump systems load forecasting

Xu, C., Chen, H., Xun, W., Zhou, Z., Liu, T., Zeng, Y., & Ahmad, T. (2019) · Energy and Buildings · doi:10.1016/j.enbuild.2019.04.018 · Cited by 49 · see citing works

District Heating Load Prediction Algorithm Based on Feature Fusion LSTM Model

Xue, G., Pan, Y., Lin, T., Song, J., Qi, C., & Wang, Z. (2019) · Energies · doi:10.3390/en12112122 · Cited by 49 · see citing works

Performance evaluation of short-term cross-building energy predictions using deep transfer learning strategies

Li, G., Wu, Y., Liu, J., Fang, X., & Wang, Z. (2022) · Energy and Buildings · doi:10.1016/j.enbuild.2022.112461 · Cited by 46 · see citing works

Probabilistic load forecasting for integrated energy systems using attentive quantile regression temporal convolutional network

Guo, H., Huang, B., & Wang, J. (2024) · Advances in Applied Energy · doi:10.1016/j.adapen.2024.100165 · Cited by 44 · see citing works

Comparison of time-frequency-analysis techniques applied in building energy data noise cancellation for building load forecasting: A real-building case study

Zhang, L., Alahmad, M. A., & Wen, J. (2020) · Energy and Buildings · doi:10.1016/j.enbuild.2020.110592 · Cited by 44 · see citing works

Multi-task learning based multi-energy load prediction in integrated energy system

Wang, L., Tan, M., Chen, J., & Liao, C. (2022) · Applied Intelligence · doi:10.1007/s10489-022-04054-6 · Cited by 43 · see citing works

An Improved Hybrid Model for Short Term Power Load Prediction

Zhang, J., Siya, W., Tan, Z., & Anli, S. (2022) · Energy · doi:10.1016/j.energy.2022.126561 · Cited by 42 · see citing works

Short-term PV power forecasting based on time series expansion and high-order fuzzy cognitive maps

Xia, Y., Wang, J., Zhang, Z., Wei, D., & Li, Y. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110037 · Cited by 42 · see citing works

An improved transfer learning strategy for short-term cross-building energy prediction using data incremental

Li, G., Wu, Y., Yan, C., Fang, X., Li, T., Gao, J., Xu, C., & Wang, Z. (2023) · Building Simulation · doi:10.1007/s12273-023-1053-x · Cited by 41 · see citing works

Meta-learning strategy based on user preferences and a machine recommendation system for real-time cooling load and COP forecasting

Li, W., Gong, G., Fan, H., Peng, P., & Chun, L. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115144 · Cited by 40 · see citing works

Energy consumption prediction in cement calcination process: A method of deep belief network with sliding window

Hao, X., Guo, T., Huang, G., Shi, X. C., Zhao, Y., & Yang, Y. (2020) · Energy · doi:10.1016/j.energy.2020.118256 · Cited by 40 · see citing works

Research on the combined forecasting model of cooling load based on IVMD-WOA-LSSVM

Zhou, M., Yu, J., Wang, M., Quan, W., & Bian, C. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114339 · Cited by 39 · see citing works

Short-term forecast model of cooling load using load component disaggregation

Lin, X., Tian, Z., Lu, Y., Zhang, H., & Niu, J. (2019) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2019.04.040 · Cited by 39 · see citing works

Modeling of H2S solubility in ionic liquids using deep learning: A chemical structure-based approach

Mousavi, S. P., Atashrouz, S., Nakhaei-Kohani, R., Hadavimoghaddam, F., Shawabkeh, A., Hemmati‐Sarapardeh, A., & Mohaddespour, A. (2021) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2021.118418 · Cited by 39 · see citing works

A Review on Deep Learning with Focus on Deep Recurrent Neural Network for Electricity Forecasting in Residential Building

Abdulrahman, M. L., Ibrahim, K. M., Gital, A. Y., Zambuk, F. U., Ja’afaru, B., Yakubu, I. Z., & Ibrahim, A. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.10.014 · Cited by 38 · see citing works

Algorithmic conservation in a changing climate

Scoville, C., Chapman, M., Amironesei, R., & Boettiger, C. (2021) · Current Opinion in Environmental Sustainability · doi:10.1016/j.cosust.2021.01.009 · Cited by 37 · see citing works

Application of artificial neural networks in predicting the performance of ice thermal energy storage systems

Odufuwa, O. Y., Tartibu, L. K., Kusakana, K., Hohne, P. A., & Numbi, B. P. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.112547 · Cited by 36 · see citing works

Short term load forecasting with markovian switching distributed deep belief networks

Dong, Y., Dong, Z., Zhao, T., Li, Z., & Ding, Z. (2021) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2021.106942 · Cited by 35 · see citing works

Machine Learning Applications for Smart Building Energy Utilization: A Survey

Huotari, M., Malhi, A. K., & Främling, K. (2024) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-10054-7 · Cited by 33 · see citing works

A novel robust ensemble model integrated extreme learning machine with multi-activation functions for energy modeling and analysis: Application to petrochemical industry

Zhang, X., Zhu, Q., He, Y., & Xu, Y. (2018) · Energy · doi:10.1016/j.energy.2018.08.069 · Cited by 32 · see citing works

Review of load data analytics using deep learning in smart grids: Open load datasets, methodologies, and application challenges

Elahe, M. F., Jin, M., & Zeng, P. (2021) · International Journal of Energy Research · doi:10.1002/er.6745 · Cited by 33 · see citing works

Performance assessment of cross office building energy prediction in the same region using the domain adversarial transfer learning strategy

Li, G., Wang, Z., Gao, J., Xu, C., Guo, Y., Sun, D., & Fang, X. (2024) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2024.122357 · Cited by 29 · see citing works

Comprehensive transferability assessment of short-term cross-building-energy prediction using deep adversarial network transfer learning

Li, G., Wu, Y., Yoon, S., & Fang, X. (2024) · Energy · doi:10.1016/j.energy.2024.131395 · Cited by 29 · see citing works

A novel hybrid model based on evolving multi-quantile long and short-term memory neural network for ultra-short-term probabilistic forecasting of photovoltaic power

Zhu, J., & He, Y. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124601 · Cited by 29 · see citing works

Air-Conditioning Load Forecasting for Prosumer Based on Meta Ensemble Learning

Chen, Y., Fu, G., & Liu, X. (2020) · IEEE Access · doi:10.1109/access.2020.2994119 · Cited by 28 · see citing works

Statistical and machine learning approaches for energy efficient buildings

Paravantis, J. A., Malefaki, S., Nikolakopoulos, P. G., Romeos, A., Giannadakis, A., Giannakopoulos, E., Mihalakakou, G. K., & Souliotis, M. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.115309 · Cited by 27 · see citing works

Real-time automatic control of multi-energy system for smart district community: A coupling ensemble prediction model and safe deep reinforcement learning

Alabi, T. M., Lu, L., & Yang, Z. (2024) · Energy · doi:10.1016/j.energy.2024.132209 · Cited by 25 · see citing works

Light-stacking strengthened fusion based building energy consumption prediction framework via variable weight feature selection

Sun, J., Liu, G., Sun, B., & Xiao, G. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117694 · Cited by 25 · see citing works

Modeling the impact of heat rejection from split air conditioner on outdoor air temperature in high-density residential areas

Liu, L., Du, Z., Wang, S., Liu, J., Hang, J., & Yang, W. (2025) · Building and Environment · doi:10.1016/j.buildenv.2025.112685 · Cited by 24 · see citing works

A method to estimate leaf area index from VIIRS surface reflectance using deep transfer learning

Li, J., Xiao, Z., Sun, R., & Song, J. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.07.012 · Cited by 24 · see citing works

Adaptive Feature Selection for Probabilistic Multi-Energy Load Forecasting

Ge, Y., Zhang, W., Liu, G., Li, Z., & Li, H. (2023) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2023.3344540 · Cited by 23 · see citing works

A Review of Industrial Load Flexibility Enhancement for Demand-Response Interaction

Zhang, J., Zhou, B., Yang, Z., Guo, Y., Lv, C., Xu, X., & Liu, J. (2025) · Sustainability · doi:10.3390/su17114938 · Cited by 23 · see citing works

Hourly Urban Water Demand Forecasting Using the Continuous Deep Belief Echo State Network

Xu, Y., Zhang, J., Long, Z., Tang, H., & Zhang, X. (2019) · Water · doi:10.3390/w11020351 · Cited by 23 · see citing works

Prediction model of the large commercial building cooling loads based on rough set and deep extreme learning machine

Lei, L., & Shao, S. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2023.107958 · Cited by 23 · see citing works

Forecasting of solar radiation for a cleaner environment using robust machine learning techniques

Magesh, T., Parthiban, V. J., Kesavaraman, D., & Murugesan, T. (2022) · Environmental Science and Pollution Research · doi:10.1007/s11356-022-24321-w · Cited by 21 · see citing works

Multiple criteria assessment of methods for forecasting building thermal energy demand

D’Amico, A., Ciulla, G., Tūpėnaitė, L., & Kaklauskas, A. (2020) · Energy and Buildings · doi:10.1016/j.enbuild.2020.110220 · Cited by 20 · see citing works

Statistical and Artificial Intelligence-Based Tools for Building Energy Prediction: A Systematic Literature Review

Olu-Ajayi, R., Alaka, H., Sunmola, F. T., Ajayi, S. O., & Mporas, I. (2024) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2024.3422821 · Cited by 20 · see citing works

Short-term prediction of photovoltaic power based on quadratic decomposition and residual correction

Wang, S., Su, Y., Li, H., Zhang, T., Jiang, W., Yang, B., Li, M., Li, M., Zhang, N., & Wang, J. (2024) · Electric Power Systems Research · doi:10.1016/j.epsr.2024.110968 · Cited by 20 · see citing works

An intelligent way to predict the building thermal needs: ANNs and optimization

D’Amico, A., & Ciulla, G. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116293 · Cited by 19 · see citing works

Modeling and predicting energy consumption of chiller based on dynamic spatial-temporal graph neural network

Liu, Q., Cheng, X., Jianzhong, S., Ma, Y., & Peng, P. (2024) · Journal of Building Engineering · doi:10.1016/j.jobe.2024.109657 · Cited by 19 · see citing works

BEForeGAN: An image-based deep generative approach for day-ahead forecasting of building HVAC energy consumption

Ma, Y. X., & Yeung, K. L. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124196 · Cited by 19 · see citing works

A novel ensemble method for residential electricity demand forecasting based on a novel sample simulation strategy

Zhang, G., & Guo, J. (2020) · Energy · doi:10.1016/j.energy.2020.118265 · Cited by 18 · see citing works

Modeling of nitrogen solubility in unsaturated, cyclic, and aromatic hydrocarbons: Deep learning methods and SAFT equation of state

Mohammadi, M.-R., Hadavimoghaddam, F., Atashrouz, S., Abedi, A., Hemmati‐Sarapardeh, A., & Mohaddespour, A. (2021) · Journal of the Taiwan Institute of Chemical Engineers · doi:10.1016/j.jtice.2021.10.024 · Cited by 18 · see citing works

Privileged information-driven random network based non-iterative integration model for building energy consumption prediction

Sun, H., Zhai, W., Wang, Y., Yin, L., & Zhou, F. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107438 · Cited by 17 · see citing works

Empirical mode decomposition using deep learning model for financial market forecasting

Jin, Z., Jin, Y., & Chen, Z. (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.1076 · Cited by 17 · see citing works

Estimating cooling production and monitoring efficiency in chillers using a soft sensor

Alonso, S., Morán, A., Pérez, D., Prada, M. A., Díaz, I., & Domí­nguez, M. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05165-2 · Cited by 17 · see citing works

Eye State Identification Utilizing EEG Signals: A Combined Method Using Self-Organizing Map and Deep Belief Network

Ahmadi, N., Nilashi, M., Bidgoli, B. M., Farooque, M. M. J., Samad, S., Aljehane, N. O., Zogaan, W. A., & Ahmadi, H. (2022) · Scientific Programming · doi:10.1155/2022/4439189 · Cited by 18 · see citing works

A hybrid model based on multivariate fast iterative filtering and long short-term memory for ultra-short-term cooling load prediction

Myat, A., Namitha, K., Soh, Y. L., & An, H. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.113977 · Cited by 16 · see citing works

A Novel Interval Energy-Forecasting Method for Sustainable Building Management Based on Deep Learning

Duan, Y. (2022) · Sustainability · doi:10.3390/su14148584 · Cited by 16 · see citing works

Electricity demand uncertainty modeling with Temporal Convolution Neural Network models

Ghimire, S., Deo, R. C., Casillas-Pérez, D., Salcedo‐Sanz, S., Acharya, U. R., & Dinh, T. (2024) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2024.115097 · Cited by 15 · see citing works

A Short-Term Building Load Prediction Method Based on Modal Decomposition and Deep Learning

Lu, S., Yu, D., Ding, Y. Y., Chen, W., Liang, C., Yuan, J., Tian, Z., & Lu, Y. (2025) · Buildings · doi:10.3390/buildings15244455 · Cited by 2 · see citing works

SPEHDSTCN: A statistical prior error-corrected heterogeneous depthwise separable temporal convolutional network for building air-conditioning cooling load forecasting

Zhou, M., Jia, X., Hu, F., Li, J., Wang, K., Zhao, Z., LI, 李. Y., Zhang, Z., Li, H., & Tong, X. (2026) · Journal of Building Engineering · doi:10.1016/j.jobe.2026.116484

TFDiFormer: A Hybrid Deep Learning Model for Airport Terminal Cooling Load ForecastingPreprint

Lin, R., Yuan, Z., Liu, T., Yu, L., Li, K., Xu, Y., & Wang, K. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5668722

A novel deep-learning framework for short-term prediction of cooling load in public buildings

Song, C., Yang, H., Meng, X.-B., Yang, P., Cai, J., Bao, H., & Xu, K. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.139796 · Cited by 38 · see citing works

Optimal load demand forecasting in air conditioning using deep belief networks optimized by an improved version of snake optimization algorithm

Jiang, H., Li, M., & Fathi, G. (2023) · IET Renewable Power Generation · doi:10.1049/rpg2.12819 · Cited by 10 · see citing works