Systematic review of deep learning and machine learning for building energy

• URL: http://arxiv.org/abs/2202.12269v1
• Date: Wed, 23 Feb 2022 13:40:45 GMT
• Title: Systematic review of deep learning and machine learning for building energy
• Authors: Ardabili Sina, Leila Abdolalizadeh, Csaba Mako, Bernat Torok, Mosavi Amir
• Abstract summary: Building energy (BE) management has an essential role in urban sustainability and smart cities. The machine learning (ML) and deep learning (DL) methods and applications 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs [70.91804882618243]
  This paper proposes DSMoE, a novel approach that achieves sparsification by partitioning pre-trained FFN layers into computational blocks. We implement adaptive expert routing using sigmoid activation and straight-through estimators, enabling tokens to flexibly access different aspects of model knowledge. Experiments on LLaMA models demonstrate that under equivalent computational constraints, DSMoE achieves superior performance compared to existing pruning and MoE approaches.
  arXiv  Detail & Related papers  (2025-02-18T02:37:26Z)
• Renewable Energy Prediction: A Comparative Study of Deep Learning Models for Complex Dataset Analysis [6.857122511301731]
  Deep learning (DL) models capture complex, nonlinear relationships in renewable energy datasets. Seven machine learning methods, LSTM, Stacked LSTM, CNN, CNN-LSTM, Time-DistributedDistributed (TD-MLP) and Autoencoder (AE) are evaluated. Regularization techniques such as early stopping, neuron dropout, L1 and L2 regularization are applied to address overfitting.
  arXiv  Detail & Related papers  (2025-01-27T02:10:10Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation. deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency. This survey analyzes optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• Benchmarks as Microscopes: A Call for Model Metrology [76.64402390208576]
  Modern language models (LMs) pose a new challenge in capability assessment. To be confident in our metrics, we need a new discipline of model metrology.
  arXiv  Detail & Related papers  (2024-07-22T17:52:12Z)
• EcoMLS: A Self-Adaptation Approach for Architecting Green ML-Enabled Systems [1.0923877073891446]
  Self-adaptation techniques, recognized for their potential in energy savings within software systems, have yet to be extensively explored in Machine Learning-Enabled Systems. This research underscores the feasibility of enhancing MLS sustainability through intelligent runtime adaptations.
  arXiv  Detail & Related papers  (2024-04-17T14:12:47Z)
• QualEval: Qualitative Evaluation for Model Improvement [82.73561470966658]
  We propose QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement. QualEval uses a powerful LLM reasoner and our novel flexible linear programming solver to generate human-readable insights. We demonstrate that leveraging its insights, for example, improves the absolute performance of the Llama 2 model by up to 15% points relative.
  arXiv  Detail & Related papers  (2023-11-06T00:21:44Z)
• Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression [64.07696663255155]
  Large-scale pre-trained language models (LLMs) have demonstrated exceptional performance in various natural language processing (NLP) tasks. However, the massive size of these models poses huge challenges for their deployment in real-world applications. We introduce a novel compression paradigm called Retrieval-based Knowledge Transfer (RetriKT) which effectively transfers the knowledge of LLMs to extremely small-scale models.
  arXiv  Detail & Related papers  (2023-10-24T07:58:20Z)
• MATNet: Multi-Level Fusion Transformer-Based Model for Day-Ahead PV Generation Forecasting [0.47518865271427785]
  MATNet is a novel self-attention transformer-based architecture for PV power generation forecasting. It consists of a hybrid approach that combines the AI paradigm with the prior physical knowledge of PV power generation. Results show that our proposed architecture significantly outperforms the current state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-06-17T14:03:09Z)
• Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation [93.52573037053449]
  H-MARL (Hallucinated Multi-Agent Reinforcement Learning) learns successful equilibrium policies after a few interactions with the environment. We demonstrate our approach experimentally on an autonomous driving simulation benchmark.
  arXiv  Detail & Related papers  (2022-03-14T17:24:03Z)
• An Adaptive Deep Learning Framework for Day-ahead Forecasting of Photovoltaic Power Generation [0.8702432681310401]
  This paper proposes an adaptive LSTM (AD-LSTM) model, which is a DL framework that can not only acquire general knowledge from historical data, but also dynamically learn specific knowledge from newly-arrived data. The developed AD-LSTM model demonstrates greater forecasting capability than the offline LSTM model, particularly in the presence of concept drift.
  arXiv  Detail & Related papers  (2021-09-28T02:39:56Z)
• Energy Forecasting in Smart Grid Systems: A Review of the State-of-the-art Techniques [2.3436632098950456]
  This paper presents a review of state-of-the-art forecasting methods for smart grid (SG) systems. Traditional point forecasting methods including statistical, machine learning (ML), and deep learning (DL) are extensively investigated. A comparative case study using the Victorian electricity consumption and American electric power (AEP) is conducted.
  arXiv  Detail & Related papers  (2020-11-25T09:17:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.