Attention-LSTM architecture combined with Bayesian hyperparameter optimization for indoor temperature prediction

Ben, Jiang; Gong, Haimei; Qin, Haosen; Zhu, Mengjie

doi:10.1016/j.buildenv.2022.109536

Cited by 34 publications

(8 citation statements)

References 36 publications

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“…To assess the efficacy of the proposed method for forecasting COVID-19 in time-series data, three crucial metrics (Root Mean Square Error, Mean Absolute Error, and Mean Absolute Percentage Error) were calculated. The equations for computing SMAPE, MAPE, and RMSE were obtained from Equations 8 – 10 , correspondingly explained by Jiang et al ( 2022 ) and Kistenev et al ( 2022 ). The model's effectiveness was assessed through out-of-sample predictions from the test set (TS).…”

Section: Prediction Methods Stepsmentioning

confidence: 99%

“…Zarzycki and Ławryńczuk ( 2022 ) explain that the architecture relies entirely on the network to use an LSTM layer. Jiang et al ( 2022 ) proposed the incorporation of hourly weather measurements as data references in the LSTM architecture. In their 2022 study, Kistenev et al used unbalanced microcomputed tomography (Micro-CT) imaging, supervised deep learning, and adaptive self-degradation.…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence in healthcare: combining deep learning and Bayesian optimization to forecast COVID-19 confirmed cases

Alhhazmi,

Alferidi,

Almutawif

et al. 2024

Front. Artif. Intell.

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Healthcare is a topic of significant concern within the academic and business sectors. The COVID-19 pandemic has had a considerable effect on the health of people worldwide. The rapid increase in cases adversely affects a nation's economy, public health, and residents' social and personal well-being. Improving the precision of COVID-19 infection forecasts can aid in making informed decisions regarding interventions, given the pandemic's harmful impact on numerous aspects of human life, such as health and the economy. This study aims to predict the number of confirmed COVID-19 cases in Saudi Arabia using Bayesian optimization (BOA) and deep learning (DL) methods. Two methods were assessed for their efficacy in predicting the occurrence of positive cases of COVID-19. The research employed data from confirmed COVID-19 cases in Saudi Arabia (SA), the United Kingdom (UK), and Tunisia (TU) from 2020 to 2021. The findings from the BOA model indicate that accurately predicting the number of COVID-19 positive cases is difficult due to the BOA projections needing to align with the assumptions. Thus, a DL approach was utilized to enhance the precision of COVID-19 positive case prediction in South Africa. The DQN model performed better than the BOA model when assessing RMSE and MAPE values. The model operates on a local server infrastructure, where the trained policy is transmitted solely to DQN. DQN formulated a reward function to amplify the efficiency of the DQN algorithm. By examining the rate of change and duration of sleep in the test data, this function can enhance the DQN model's training. Based on simulation findings, it can decrease the DQN work cycle by roughly 28% and diminish data overhead by more than 50% on average.

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Section: Prediction Methods Stepsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in healthcare: combining deep learning and Bayesian optimization to forecast COVID-19 confirmed cases

Alhhazmi,

Alferidi,

Almutawif

et al. 2024

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…This modeling technique has become increasingly popular in recent years. There are also researchers in related fields using recurrent neural networks such as long short-term memory (LSTM) to predict temporal changes in temperature and achieving good results [11,[28][29][30], and LSTM is a special type of recurrent neural network (RNN) that is capable of learning long-term dependencies, which is more evident in the prediction of temporal sequences.…”

Section: Ref Time Granularitymentioning

confidence: 99%

Research on Online Temperature Prediction Method for Office Building Interiors Based on Data Mining

et al. 2023

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Indoor environmental parameters are closely related to the energy consumption and indoor thermal comfort of office buildings. Predicting these parameters, especially indoor temperature, can contribute to the management of energy consumption and thermal comfort levels in office buildings. An accurate indoor temperature prediction model is the basis for implementing this process. To this end, this paper first discusses the input and output parameters of the model, and then it compares the prediction effects of mainstream prediction model algorithms based on data mining under the same data conditions. The superiority of the XGBoost integrated learning algorithm is verified, and a further XGBoost-based indoor temperature online prediction method is designed. The effectiveness of the method is validated using actual data from a commercial office building in Haidian District, Beijing. Finally, optimization methods for the prediction method are discussed with regard to the scheduler mechanism proposed in this paper. Overall, this work can assist building operators in optimizing HVAC equipment running strategies, thus improving the indoor thermal comfort and energy efficiency of the building.

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“…In Machine learning, data is processed using attention mechanism, which is used to determine contribution size between input and out data, making it applicable to a variety of disciplines. For example, Jiang proposed a combined LSTM, transformer and attention mechanism for indoor temperature prediction model, which achieves accurate and efficient prediction of room temperature trends [17]. Zhang integrated transformer model and multiple attention mechanism to develop an attention network framework based on Transformer Encoder, which effectively achieved accurate prediction of stock trends [18].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Model to Predict Dissolved Oxygen for Efficient Water Quality in Intensive Aquaculture

Liu

Hassan

et al. 2023

IEEE Access

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Dissolved oxygen content is a key indicator of water quality in aquaculture environment. Because of its nonlinearity, dynamics, and complexity, which makes traditional methods face challenges in the accuracy and speed of dissolved oxygen content prediction. As a solution to these issues, this study introduces a hybrid model consisting of the Light Gradient Boosting Machine (LightGBM) and the Bidirectional Simple Recurrent Unit (BiSRU). Firstly, Linear interpolation and smoothing were used to identify significant parameters. LightGBM algorithm then determines the significance of dissolved oxygen by eliminating irrelevant variables and predicting dissolved oxygen in intensive aquaculture. Finally, the attention method was implemented to map the weighting and learning parameter matrices, so enabling the BiSRU's hidden states to be assigned different weights. The findings shown that the presented prediction model can accurately anticipate the fluctuating trend of dissolved oxygen over a 10-day period in just 122 seconds, and the accuracy rate reached 96.28%. Comparing the model effects of LightGBM -BiSRU, LightGBM -GRU, LightGBM-LSTM, and BiSRU -Attention takes the least time. Its higher prediction accuracy can provide an essential reference for intensive aquaculture water quality regulation.

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Attention-LSTM architecture combined with Bayesian hyperparameter optimization for indoor temperature prediction

Cited by 34 publications

References 36 publications

Artificial intelligence in healthcare: combining deep learning and Bayesian optimization to forecast COVID-19 confirmed cases

Artificial intelligence in healthcare: combining deep learning and Bayesian optimization to forecast COVID-19 confirmed cases

Research on Online Temperature Prediction Method for Office Building Interiors Based on Data Mining

A Novel Hybrid Model to Predict Dissolved Oxygen for Efficient Water Quality in Intensive Aquaculture

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