H-Ahead Multivariate microclimate Forecasting System Based on Deep Learning

Elhariri, Esraa; Taie, Shereen A.

doi:10.1109/itce.2019.8646540

Cited by 15 publications

(18 citation statements)

References 14 publications

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“…An integrated solution to collect and analyse IAQ using sensor nodes and ARIMA prediction models. [33] Wireless sensor network installed in a domestic house (SML system house) researchers should focus on the application of fuzzy logic and other relevant machine learning methods for forecasting IAQ conditions [32]. LSTM is another crucial solution, and several researchers considered this technique (see Table 5) for comparing performance of their proposed methods and to validate the quality of results.…”

Section: Arimamentioning

confidence: 99%

“…Moreover, the authors of [132] only provide a comparison between their methods and do not quantitatively describe metrics for their performance. Out of all these studies, four papers [2,33,77,119] provided the detailed performance analysis of the predicted hours. Note that authors in [2] used the classification method to identify good and bad air quality.…”

Section: Answer To Rq4mentioning

confidence: 99%

“…However, prediction systems should not just focus on error parameters. Instead, a significant IAQ prediction model should also provide a forecast for the coming hours [33]. These systems can help occupants to take adequate decisions about ventilation and follow preventive measures to avoid serious health complications.…”

Section: Answer To Rq4mentioning

confidence: 99%

“…These systems can help occupants to take adequate decisions about ventilation and follow preventive measures to avoid serious health complications. By considering the advantage of [2,33,77,119] for prediction hour analysis to alert the occupants, re- [33,49,66,77,113,119,133] searchers also need to make efforts to develop a costeffective, reliable and accurate future alert-based IAQ prediction system.…”

Section: Answer To Rq4mentioning

confidence: 99%

“…RQ6 concerns the opportunities for integrating IAQ prediction systems with smart building systems. In total, seven (33.3%) out of 21 studies [2,24,33,49,106,119,134] were based on smart building solutions where the IAQ prediction system was integrated with other smart solutions in the premises for improved lifestyle and well-being. However, the remaining 14 studies (66.6%) were independent solutions where researchers worked solely on IAQ monitoring and prediction.…”

Section: Answer To Rq6mentioning

confidence: 99%

See 4 more Smart Citations

Indoor air quality prediction systems for smart environments: A systematic review

Saini

Dutta

Marques

2020

AIS

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Air quality is a critical matter of concern in terms of the impact on public health and well-being. Although the consequences of poor air quality are more severe in developing countries, they also have a critical impact in developed countries. Healthcare costs due to air pollution reach $150 billion in the USA, whereas particulate matter causes 412,000 premature deaths in Europe, every year. According to the Environmental Protection Agency (EPA), indoor air pollutant levels can be up to 100 times higher in comparison to outdoor air quality. Indoor air quality (IAQ) is in the top five environmental risks to global health and well-being. The research community explored the scope of artificial intelligence (AI) in the past years to deal with this problem. The IAQ prediction systems contribute to smart environments where advanced sensing technologies can create healthy living conditions for building occupants. This paper reviews the applications and potential of AI for the prediction of IAQ to enhance building environment and public health. The results show that most of the studies analyzed incorporate neural networks-based models and the preferred evaluation metrics are RMSE, R 2 score and error rate. Furthermore, 66.6% of the studies include CO2 sensors for IAQ assessment. Temperature and humidity parameters are also included in 90.47% and 85.71% of the proposed methods, respectively. This study also presents some limitations of the current research activities associated with the evaluation of the impact of different pollutants based on different geographical conditions and living environments. Moreover, the use of reliable and calibrated sensor networks for real-time data collection is also a significant challenge.

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Section: Arimamentioning

confidence: 99%

Section: Answer To Rq4mentioning

confidence: 99%

Section: Answer To Rq4mentioning

confidence: 99%

Section: Answer To Rq4mentioning

confidence: 99%

Section: Answer To Rq6mentioning

confidence: 99%

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Indoor air quality prediction systems for smart environments: A systematic review

Saini

Dutta

Marques

2020

AIS

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DeepOcean: A General Deep Learning Framework for Spatio-Temporal Ocean Sensing Data Prediction

Gou

Zhang

et al. 2020

IEEE Access

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The emerging Internet of Underwater Things (IoUT) and deep learning technologies are combined to provide a novel, intelligent, and efficient data processing and analyzing schema, which facilitates the sensing and computing abilities for the smart ocean. The underwater acoustic (UWA) communication network is an essential part of IoUT. The thermocline, in which temperature and density change drastically, affects the connectivity and communication performance between IoUT nodes, as well as the network topologies. In this paper, we propose DeepOcean, a deep learning framework for spatio-temporal ocean sensing data prediction, which consists of a generative module and a prediction module. We implement the generative module with a multi-layer perceptron (MLP) to capture the spatial dependencies and construct high-resolution data based on sparse observations. The prediction module is implemented with our proposed Multivariate Convolutional LSTM (MVC-LSTM) neural network, which captures both the spatio-temporal dependencies and the interactions of different oceanographic features for prediction. We evaluate the effectiveness of DeepOcean with Argo data, where the proposed framework outperforms fifteen state-of-art baselines in terms of accuracy. INDEX TERMS Internet of Underwater Things (IoUT), deep learning, spatio-temporal prediction, multivariate convolutional LSTM (MVC-LSTM) neural network, thermocline.

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Sequence to Sequence Deep Learning Architecture for Forecasting Temperature and Humidity inside Closed Space

Setiawan

Elwirehardja

Pardamean

2022

2022 10th International Conference on Cyber and IT Service Management (CITSM)

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H-Ahead Multivariate microclimate Forecasting System Based on Deep Learning

Cited by 15 publications

References 14 publications

Indoor air quality prediction systems for smart environments: A systematic review

Indoor air quality prediction systems for smart environments: A systematic review

DeepOcean: A General Deep Learning Framework for Spatio-Temporal Ocean Sensing Data Prediction

Sequence to Sequence Deep Learning Architecture for Forecasting Temperature and Humidity inside Closed Space

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