Systematic review of deep learning and machine learning for building energy

Abstract

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.

Investigating the predictive contribution of attitude towards life and belief system on self-resilience and psychological toughness of cancer patients about the mediating role of emotion regulation

“…Therefore, emotion regulation can be effective on psychological toughness. By Increasing the data points the machine learning methods can be used, e.g., [33][34][35][36][37][38][39][40][41][42][43][44]. Therefore, the study on the perception to life and belief system on self-resilience and psychological toughness of cancer patients about the mediating role of emotion regulation can be better investigated.…”

Application of a New Hybrid Machine Learning (Fuzzy-PSO) for Detection of Breast's Tumor

“…Data pre-processing includes noise removal and data normalization [21][22][23][24]. First, the data is normalized, then the beginner clustering method is used to identify the outlier data, and removal of the out-of-range data is performed [25][26][27][28][29][30].…”

Incorporating causality in energy consumption forecasting using deep neural networks

“…Another example of using deep networks in the energy sector is to perform the assets management for the electrical grid companies (Kala et al, 2020), where the authors showed that their proposed algorithm involves the faster regional convolutional neural networks outperformed the human-coding efforts for asset management. For a comprehensive review of deep learning for energy systems and building energy, please see, Forootan et al (2022) and Ardabili et al (2022), respectively.…”