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Home All issues Volume 81 (2026) Sci. Tech. Energ. Transition, 81 (2026) 13 References
Open Access
Issue
Sci. Tech. Energ. Transition
Volume 81, 2026
Article Number 13
Number of page(s) 12
DOI https://doi.org/10.2516/stet/2026013
Published online 21 April 2026
  • IEA. 2023. Electricity market report 2023. Paris: International Energy Agency. https://www.iea.org/reports/electricity-market-report-2023. [Google Scholar]
  • Wang J.Q., Du Y., Wang J. (2020). LSTM based long-term energy consumption prediction with periodicity. Energy 197, 117197–117209. [CrossRef] [Google Scholar]
  • Khan A.N., Iqbal N., Rizwan A., Ahmad R., Kim D.H. (2021) An ensemble energy consumption forecasting model based on spatial-temporal clustering analysis in residential buildings, Energies 14, 11, 3020–3045. [CrossRef] [Google Scholar]
  • Walker S., Khan W., Katic K., Maassen W., Zeiler W. (2020) Accuracy of different machine learning algorithms and added-value of predicting aggregated-level energy performance of commercial buildings. Energy Build. 209, 109705. [Google Scholar]
  • Luo X.J., Oyedele L.O., Ajayi A.O., Akinade O.O., Owolabi H.A., Ahmed A. (2020) Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings, Renew. Sustain. Energy Rev. 131, 109980. [Google Scholar]
  • Kaur S., Bala A., Parashar A. (2024) GA-BiLSTM: an intelligent energy prediction and optimization approach for individual home appliances, Evolving Syst. 15, 2, 413–427. [Google Scholar]
  • Olu-Ajayi R., Alaka H., Sulaimon I., Sunmola F., Ajayi S. (2022) Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques, J. Build. Eng. 45, 103406. [Google Scholar]
  • Sajjad M., Khan Z.A., Ullah A., Hussain T., Ullah W., Lee M.Y., Baik S.W. (2020) A novel cnn-gru-based hybrid approach for short-term residential load forecasting, IEEE Access 8, 143759–143768. [Google Scholar]
  • Hossen T., Nair A.S., Chinnathambi R.A., Ranganathan P. (2018) Residential load forecasting using deep neural networks (DNN), in 2018 North American Power Symposium (NAPS), IEEE, pp. 1–5. [Google Scholar]
  • Zhou G., Moayedi H., Bahiraei M., Lyu Z. (2020) Employing artificial bee colony and particle swarm techniques for optimizing a neural network in prediction of heating and cooling loads of residential buildings, J. Clean. Prod. 254, 120082–120096. [Google Scholar]
  • Kaur S., Bala A., Parashar A. (2024) GA-BiLSTM: an intelligent energy prediction and optimization approach for individual home appliances, Evolving Syst. 15, 2, 413–427. [Google Scholar]
  • Kaur J., Bala A. (2019) A hybrid energy management approach for home appliances using climatic forecasting, Build. Simulation 12, 1033–1045 (Springer). [Google Scholar]
  • Gajowniczek K., Zabkowski T. (2017) Electricity forecasting on the individual household level enhanced based on activity patterns, PloS One 12, 4, 1–26. [Google Scholar]
  • Bourhnane S., Abid M.R., Lghoul R., Zine-Dine K., Elkamoun N., Benhaddou D. (2020) Machine learning for energy consumption prediction and scheduling in smart buildings, SN Appl. Sci. 2, 2, 297–307. [Google Scholar]
  • Torabi M., Hashemi S., Saybani M.R., Shamshirband S., Mosavi A. (2019) A hybrid clustering and classification technique for forecasting short-term energy consumption, Environ. Prog. Sustain. Energy 38, 1, 66–76. [Google Scholar]
  • Nazir A., Wajahat A., Akhtar F., Ullah F., Qureshi S., Malik S.A., Shakeel A. (2020) Evaluating energy efficiency of buildings using artificial neural networks and k-means clustering techniques, in 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), IEEE, pp. 1–7. [Google Scholar]
  • Satre-Meloy A., Diakonova M., Grünewald P. (2020) Cluster analysis and prediction of residential peak demand profiles using occupant activity data, Appl. Energy 260, 114246. [Google Scholar]
  • Abera F.Z., Khedkar V. (2020) Machine learning approach electric appliance consumption and peak demand forecasting of residential customers using smart meter data, Wireless Person. Commun. 111, 1, 65–82. [Google Scholar]
  • Petsis S., Karamanou A., Kalampokis E., Tarabanis K. (2022) Forecasting and explaining emergency department visits in a public hospital, J. Intell. Inform. Syst. 59, 2, 479–500. [Google Scholar]
  • Wang Z., Wang Y., Zeng R., Srinivasan R.S., Ahrentzen S. (2018) Random forest based hourly building energy prediction, Energy Build. 171, 11–25. [CrossRef] [Google Scholar]
  • Piscitelli M.S., Brandi S., Capozzoli A., Xiao F. (2021) A data analytics-based tool for the detection and diagnosis of anomalous daily energy patterns in buildings, in Building simulation (Vol. 14, No. 1, pp. 131–147). Beijing: Tsinghua University Press. [Google Scholar]
  • Ngo N.T., Pham A.D., Truong T.T.H., Truong N.S., Huynh N.T., Pham T.M. (2022) An ensemble machine learning model for enhancing the prediction accuracy of energy consumption in buildings, Arabian J. Sci. Eng. 47, 4, 4105–4117. [Google Scholar]
  • Kim T.-Y., Cho S.-B. (2019) Predicting residential energy consumption using CNN-LSTM neural networks, Energy 182, 72–81. [CrossRef] [Google Scholar]
  • Bimenyimana T. (2020) Using machine learning and deep learning for load disaggregation and recognition of activities in household, PhD thesis, Carleton University. [Google Scholar]
  • Ilbeigi M., Ghomeishi M., Dehghanbanadaki A. (2020) Prediction and optimization of energy consumption in an office building using artificial neural network and a genetic algorithm, Sustainable Cities and Society 61, 102325. [Google Scholar]
  • Li L., Fu Y., Fung J.-C., Qu H., Lau A.-K. (2021) Development of a back-propagation neural network and adaptive grey wolf optimizer algorithm for thermal comfort and energy consumption prediction and optimization, Energy Build. 253, 111439. [Google Scholar]
  • Wang S., Deng X., Chen H., Shi Q., Xu D. (2021) A bottom-up short-term residential load forecasting approach based on appliance characteristic analysis and multi-task learning, Electric Power Syst. Res. 196, 107233. [Google Scholar]
  • Sauer J., Mariani V.C., dos Santos Coelho L., Ribeiro M.H.D.M., Rampazzo M. (2022) Extreme gradient boosting model based on improved Jaya optimizer applied to forecasting energy consumption in residential buildings, Evolving Syst. 13, 4, 577–588. [Google Scholar]
  • Ullah F.U.M., Ullah A., Haq I.U., Rho S., Baik S.W. (2019) Short-term prediction of residential power energy consumption via CNN and multi-layer bi-directional lstm networks, IEEE Access 8, 123369–123380. [Google Scholar]
  • Goudarzi S., Anisi M.H., Kama N., Doctor F., Soleymani S.A., Sangaiah A.K. (2019) Predictive modelling of building energy consumption based on a hybrid nature-inspired optimization algorithm, Energy Build. 196, 83–93. [CrossRef] [Google Scholar]
  • Gaur M., Majumdar A. (2018) Disaggregating transform learning for non-intrusive load monitoring, IEEE Access 6, 46256–46265. [Google Scholar]
  • Pham A.-D., Ngo N.-T., Truong T.T.H., Huynh N.-T., Truong N.-S. (2020) Predicting energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability, J. Clean. Prod. 260, 121082. [Google Scholar]
  • Huang J., Koroteev D.D., Rynkovskaya M. (2022) Building energy management and forecasting using artificial intelligence: Advance technique, Comput. Electr. Eng. 99, 107790. [Google Scholar]
  • Fan C., Chen M., Tang R., Wang J. (2022) A novel deep generative modeling-based data augmentation strategy for improving short-term building energy predictions, in Building simulation (Vol. 15, No. 2, pp. 197–211), Beijing: Tsinghua University Press. [Google Scholar]
  • Özdemir D., Dörterler S., Aydın D. (2022) A new modified artificial bee colony algorithm for energy demand forecasting problem, Neural Comput. Appl. 34, 20, 17455–17471. [Google Scholar]
  • Cai Z., Dai S., Ding Q., Zhang J., Xu D., Li Y. (2023) Gray wolf optimization-based wind power load mid-long term forecasting algorithm, Comput. Electr. Eng. 109, 108769. [Google Scholar]
  • Akter R., Shirkoohi M.G., Wang J., Mérida W. (2025) An efficient hybrid deep neural network model for multi-horizon forecasting of power loads in academic buildings, Energy Build. 329, 115217. [Google Scholar]
  • Iram S., Shahzad A.R., Farid H.M.A., Shakeel H.M. (2025) Analyzing the impact of weather conditions on energy efficiency in residential buildings using machine learning techniques with explainable artificial intelligence, Adv. Build. Energy Res. 19, 5, 625–659. [Google Scholar]
  • Karijadi I., Chou S.-Y. (2022) A hybrid RF-LSTM based on CEEMDAN for improving the accuracy of building energy consumption prediction, Energy Build. 259, 111908. [CrossRef] [Google Scholar]
  • Rostamnezhad Z., Mary N., Dessaint L.A., Monfet D. (2022) Electricity consumption optimization using thermal and battery energy storage systems in buildings, IEEE Trans. Smart Grid 14, 1, 251–265. [Google Scholar]
  • Sebi N.P. (2023) Intelligent solar irradiance forecasting using hybrid deep learning model: a meta-heuristic-based prediction, Neural Proc. Lett. 55, 2, 1247–1280. [Google Scholar]
  • Łuczak M. (2016) Hierarchical clustering of time series data with parametric derivative dynamic time warping, Expert Syst. Appl. 62, 116–130. [Google Scholar]
  • Kaur S., Bala A., Parashar A. (2024) A multi-step electricity prediction model for residential buildings based on ensemble Empirical Mode Decomposition technique, Sci. Technol. Energy Trans. 79, 7. [Google Scholar]
  • Kumar J., Goomer R., Singh A.K. (2018) Long short term memory recurrent neural network (LSTM-RNN) based workload forecasting model for cloud datacenters, Proc. Comput. Sci. 125, 676–682. [Google Scholar]
  • Bedi J., Toshniwal D. (2019) Deep learning framework to forecast electricity demand, Appl. Energy 238, 1312–1326. [Google Scholar]
  • Shi Y. (2004) Particle swarm optimization, IEEE Connect. 2, 1, 8–13. [Google Scholar]
  • Makonin S. (2021) Ampds2: The almanac of minutely power dataset (version 2). [Google Scholar]
  • Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., Vanderplas J. (2011) Scikit-learn: Machine learning in Python, J. Mach. Learn. Res. 12, 2825–2830. [Google Scholar]
  • Karim F., Majumdar S., Darabi H. (2019) Insights into LSTM fully convolutional networks for time series classification, IEEE Access 7, 67718–67725. [Google Scholar]
  • Makonin S., Ellert B., Bajić I.V., Popowich F. (2016) Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014, Scientific Data 3, 1, 160037. [Google Scholar]

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