Open Access
Sci. Tech. Energ. Transition
Volume 79, 2024
Article Number 7
Number of page(s) 14
Published online 26 January 2024
  • IEA (2022) World energy outlook, International Energy Agency. Report [Google Scholar]
  • Tiwari S., Jain A., Ahmed N.M.O.S., Alkwai L.M., Dafhalla A.K.Y., Hamad S.A.S. (2022) Machine learning-based model for prediction of power consumption in smart grid-smart way towards smart city, Expert Syst. 39, 5, e12832. [CrossRef] [Google Scholar]
  • Chou J.-S., Tran D.-S. (2018) Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders, Energy 165, 709–726. [CrossRef] [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]
  • 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]
  • Ferrández-Pastor F.-J., Mora H., Jimeno-Morenilla A., Volckaert B. (2018) Deployment of IoT edge and fog computing technologies to develop smart building services, Sustainability 10, 11, 3832–3855. [CrossRef] [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. [CrossRef] [Google Scholar]
  • Amarasinghe K., Marino D.L., Manic M. (2017) Deep neural networks for energy load forecasting, in 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE), IEEE, pp. 1483–1488. [CrossRef] [Google Scholar]
  • Gardner M.W., Dorling S.R. (1998) Artificial neural networks (the multilayer perceptron) – a review of applications in the atmospheric sciences, Atmos. Environ. 32, 14–15, 2627–2636. [CrossRef] [Google Scholar]
  • Jain R.K., Smith K.M., Culligan P.J., Taylor J.E. (2014) Forecasting energy consumption of multi-family residential buildings using support vector regression: investigating the impact of temporal and spatial monitoring granularity on performance accuracy, Appl. Energy 123, 168–178. [CrossRef] [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. [CrossRef] [Google Scholar]
  • Gaur M., Makonin S., Bajić I.V., Majumdar A. (2019) Performance evaluation of techniques for identifying abnormal energy consumption in buildings, IEEE Access 7, 62721–62733. [CrossRef] [Google Scholar]
  • Bedi J., Toshniwal D. (2018) Empirical mode decomposition based deep learning for electricity demand forecasting, IEEE Access 6, 49144–49156. [CrossRef] [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]
  • Chai S., Zhang Z., Zhang Z. (2021) Carbon price prediction for China’s ETS pilots using variational mode decomposition and optimized extreme learning machine, Ann. Oper. Res. 1–22. [Google Scholar]
  • Kaur S., Bala A., Parashar A. (2022) Intelligent energy aware approaches for residential buildings: state-of-the-art review and future directions, Cluster Comput. 16, 1–18. [Google Scholar]
  • Kaur J., Bala A. (2019) A hybrid energy management approach for home appliances using climatic forecasting, in Building Simulation, Vol. 12, Springer, pp. 1033–1045. [CrossRef] [Google Scholar]
  • Chinthavali S., Tansakul V., Lee S., Tabassum A., Munk J., Jakowski J., Starke M., Kuruganti T., Buckberry H., Leverette J. (2019) Quantification of energy cost savings through optimization and control of appliances within smart neighborhood homes, in Proceedings of the 1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization, pp. 59–68. [Google Scholar]
  • Verma M., Bhambri S., Buduru A.B. (2019) Making smart homes smarter: optimizing energy consumption with human in the loop. arXiv preprint arXiv:1912.03298. [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, Renewable and Sustainable Energy Reviews 131, 109980. [CrossRef] [Google Scholar]
  • Bedi J., Toshniwal D. (2020) Energy load time-series forecast using decomposition and autoencoder integrated memory network, Appl. Soft Comput. 93, 106390. [CrossRef] [Google Scholar]
  • Wahid F., Ghazali R., Fayaz M., Shah A.S. (2017) A simple and easy approach for home appliances energy consumption prediction in residential buildings using machine learning techniques, J. Appl. Environ. Biol. Sci 7, 3, 108–119. [Google Scholar]
  • Huber P., Gerber M., Rumsch A., Paice A. (2018) Prediction of domestic appliances usage based on electrical consumption, Energy Inform. 1, 1, 265–271. [Google Scholar]
  • Mohammadi M., Talebpour F., Safaee E., Ghadimi N., Abedinia O. (2018) Small-scale building load forecast based on hybrid forecast engine, Neural Process. Lett. 48, 1, 329–351. [CrossRef] [Google Scholar]
  • Fan C., Sun Y., Zhao Y., Song M., Wang J. (2019) Deep learning-based feature engineering methods for improved building energy prediction, Appl. Energy 240, 35–45. [CrossRef] [Google Scholar]
  • Kumari S., Kumar N., Rana P.S. (2021) Big data analytics for energy consumption prediction in smart grid using genetic algorithm and long short term memory, Comput. Inform. 40, 1, 29–56. [CrossRef] [MathSciNet] [Google Scholar]
  • Kaur S., Bala A., Parashar A. (2023) GA-BiLSTM: an intelligent energy prediction and optimization approach for individual home appliances, Evol. Syst. 1–15. [Google Scholar]
  • Liu D., Yang Q., Yang F. (2020) Predicting building energy consumption by time series model based on machine learning and empirical mode decomposition, in 2020 5th IEEE International Conference on Big Data Analytics (ICBDA), IEEE, pp. 145–150. [CrossRef] [Google Scholar]
  • An N., Zhao W., Wang J., Shang D., Zhao E. (2013) Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting, Energy 49, 279–288. [CrossRef] [Google Scholar]
  • Zhaohua W., Huang N.E. (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method, Adv. Adapt. Data Anal. 1, 1, 1–41. [CrossRef] [Google Scholar]
  • Colominas M.A., Schlotthauer G., Torres M.E. (2014) Improved complete ensemble EMD: a suitable tool for biomedical signal processing, Biomed.l Signal Process. Control 14, 19–29. [CrossRef] [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. [CrossRef] [Google Scholar]
  • Hafeez G., Alimgeer K.S., Khan I. (2020) Electric load forecasting based on deep learning and optimized by Heuristic algorithm in smart grid, Appl. Energy 269, 114915–114933. [CrossRef] [Google Scholar]
  • Kaur S., Bala A., Parashar A. (2023) Electricity consumption dataset. [Google Scholar]
  • Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., Vanderplas J., Passos A., Cournapeau D., Brucher M., Perrot M., Duchesnay E. (2011) Scikit-learn: machine learning in Python, J. Mach. Learn. Res. 12, 2825–2830. [MathSciNet] [Google Scholar]
  • Yu T., Liu Y., Li Z. (2010) Online segmentation algorithm for time series based on BIRCH clustering features, in 2010 International Conference on Computational Intelligence and Security, IEEE, pp. 55–59. [Google Scholar]
  • Zhang T., Ramakrishnan R., Livny M. (1996) BIRCH: an efficient data clustering method for very large databases, ACM SIGMOD Rec. 25, 2, 103–114. [CrossRef] [Google Scholar]
  • Nhon VLQ, Anh DT (2012) A birch-based clustering method for large time series databases, in New Frontiers in Applied Data Mining: PAKDD 2011 International Workshops, Shenzhen, China, May 24–27, 2011, Revised Selected Papers 15, Springer, pp. 148–159. [Google Scholar]
  • Sheu M.-H., Jhang Y.-S., Chang Y.-C., Wang S.-T., Chang C.-Y., Lai S.-C. (2022) Lightweight denoising autoencoder design for noise removal in electrocardiography, IEEE Access 10, 98104–98116. [CrossRef] [Google Scholar]
  • Yang M., Wang J. (2022) Adaptability of financial time series prediction based on BiLSTM, Procedia Comput. Sci. 199, 18–25. [CrossRef] [Google Scholar]
  • Kingma D.P., Ba J. (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980. [Google Scholar]
  • Norwawi N.M. (2021) Sliding window time series forecasting with multilayer perceptron and multiregression of COVID-19 outbreak in Malaysia, in Data Science for COVID-19, Elsevier, pp. 547–564. [Google Scholar]
  • Laszuk D. (2017) Python implementation of empirical mode decomposition algorithm. [Google Scholar]
  • IEA (2021) India energy outlook, IEA, Paris, International Energy Agency. Report [Google Scholar]

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