Prediction model on disturbance of maintenance operation during real‐time pricing adaptive control for building air‐conditioners

Matsukawa, Shun; Takehara, Masanori; Otsu, Hideyuki; Morikawa, Junji; Inaba, Takashi; Kondo, Seiji; Ninagawa, Chuzo

doi:10.1002/tee.22921

Cited by 4 publications

(2 citation statements)

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“…LSTM could significantly increase the prediction performance compared with LR (about 40%) and ANN (about 23.7%). Matsukawa et al (2019) explored a study for the estimation of energy consumption in air conditioner systems using LSTM methods. Using LSTM could successfully improve the prediction accuracy by up to 21%.…”

Section: Long Short-term Memory-based Studiesmentioning

confidence: 99%

Systematic Review of Deep Learning and Machine Learning for Building Energy

et al. 2022

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The building energy (BE) management plays an essential role in urban sustainability and smart cities. Recently, the novel data science and data-driven technologies have shown significant progress in analyzing the energy consumption and energy demand datasets for a smarter energy management. The machine learning (ML) and deep learning (DL) methods and applications, in particular, have been promising for the advancement of accurate and high-performance energy models. The present study provides a comprehensive review of ML- and DL-based techniques applied for handling BE systems, and it further evaluates the performance of these techniques. Through a systematic review and a comprehensive taxonomy, the advances of ML and DL-based techniques are carefully investigated, and the promising models are introduced. According to the results obtained for energy demand forecasting, the hybrid and ensemble methods are located in the high-robustness range, SVM-based methods are located in good robustness limitation, ANN-based methods are located in medium-robustness limitation, and linear regression models are located in low-robustness limitations. On the other hand, for energy consumption forecasting, DL-based, hybrid, and ensemble-based models provided the highest robustness score. ANN, SVM, and single ML models provided good and medium robustness, and LR-based models provided a lower robustness score. In addition, for energy load forecasting, LR-based models provided the lower robustness score. The hybrid and ensemble-based models provided a higher robustness score. The DL-based and SVM-based techniques provided a good robustness score, and ANN-based techniques provided a medium robustness score.

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Section: Long Short-term Memory-based Studiesmentioning

confidence: 99%

Systematic Review of Deep Learning and Machine Learning for Building Energy

et al. 2022

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“…LSTM could significantly increase the prediction performance compared with LR (about 40%) and ANN (about 23.7%). Matsukawa et al (Matsukawa, Takehara et al 2019) explored a study for the estimation of energy consumption in air conditioner systems using LSTM methods. Using LSTM could successfully improve the prediction accuracy by up to 21%.…”

Section: Long Short Term Memory (Lstm) Basedmentioning

confidence: 99%

Systematic review of deep learning and machine learning for building energy

Ardabili¹,

Abdilalizadeh²,

Makó³

et al. 2022

Preprint

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The building energy (BE) management has an essential role in urban sustainability and smart cities. Recently, the novel data science and data-driven technologies have shown significant progress in analyzing the energy consumption and energy demand data sets for a smarter energy management. The machine learning (ML) and deep learning (DL) methods and applications, in particular, have been promising for the advancement of the accurate and high-performance energy models. The present study provides a comprehensive review of ML and DL-based techniques applied for handling BE systems, and it further evaluates the performance of these techniques. Through a systematic review and a comprehensive taxonomy, the advances of ML and DL-based techniques are carefully investigated, and the promising models are introduced. According to the results obtained for energy demand forecasting, the hybrid and ensemble methods are located in high robustness range, SVM-based methods are located in good robustness limitation, ANN-based methods are located in medium robustness limitation and linear regression models are located in low robustness limitations. On the other hand, for energy consumption forecasting, DL-based, hybrid, and ensemble-based models provided the highest robustness score. ANN, SVM, and single ML models provided good and medium robustness and LR-based models provided the lower robustness score. In addition, for energy load forecasting, LR-based models provided the lower robustness score. The hybrid and ensemble-based models provided a higher robustness score. The DL-based and SVM-based techniques provided a good robustness score and ANN-based techniques provided a medium robustness score.

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