Air temperature prediction using different machine learning models

Adnan, Rana Muhammad; Liang, Zhou; Kuriqi, Alban; Kişi, Özgür; Malik, Anurag; Li, Binquan; Mortazavizadeh, Fatemehsadat

doi:10.11591/ijeecs.v22.i1.pp534-541

Cited by 9 publications

(8 citation statements)

References 31 publications

(39 reference statements)

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“…700 Because the prediction of TEG generated energy directly depends on temperature differentials, methods for predicting air temperature are suitable for application in this area. Air temperature also represents an essential climatic parameter in water resource management and other agro-hydrological/ meteorological activities [95]. To forecast air temperatures, neural networks (NN) and support vector machines (SVM) are widely implemented [96].…”

Section: Environmentmentioning

confidence: 99%

“…Neural network approaches are suitable for air temperature prediction because they have fast computing speeds and are capable of solving complex problems [101]. Many types of approach are based on NNs, for example, Cascade Forward NN, Feed Forward NN [97], Group method of data handling NN [95], Deep Belief Network [99], and Generalised Regression NN [100], and can be applied to air temperature forecasting. Neural networks methods are combined with optimisation approaches such as Artificial NN and genetic algorithms [98].…”

Section: Environmentmentioning

confidence: 99%

See 1 more Smart Citation

Thermoelectric energy harvesting for internet of things devices using machine learning: A review

Kucova,

Prauzek,

Konecny

et al. 2023

CAAI Trans on Intel Tech

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Initiatives to minimise battery use, address sustainability, and reduce regular maintenance have driven the challenge to use alternative power sources to supply energy to devices deployed in Internet of Things (IoT) networks. As a key pillar of fifth generation (5G) and beyond 5G networks,IoT is estimated to reach 42 billion devices by the year 2025. Thermoelectric generators (TEGs) are solid state energy harvesters which reliably and renewably convert thermal energy into electrical energy. These devices are able to recover lost thermal energy, produce energy in extreme environments, generate electric power in remote areas, and power micro‐sensors. Applying the state of the art, the authorspresent a comprehensive review of machine learning (ML) approaches applied in combination with TEG‐powered IoT devices to manage and predict available energy. The application areas of TEG‐driven IoT devices that exploit as a heat source the temperature differences found in the environment, biological structures, machines, and other technologies are summarised. Based on detailed research of the state of the art in TEG‐powered devices, the authors investigated the research challenges, applied algorithms and application areas of this technology. The aims of the research were to devise new energy prediction and energy management systems based on ML methods, create supervised algorithms which better estimate incoming energy, and develop unsupervised and semi‐supervised approaches which provide adaptive and dynamic operation. The review results indicate that TEGs are a suitable energy harvesting technology for low‐power applications through their scalability, usability in ubiquitous temperature difference scenarios, and long operating lifetime. However, TEGs also have low energy efficiency (around 10%) and require a relatively constant heat source.

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Section: Environmentmentioning

confidence: 99%

Section: Environmentmentioning

confidence: 99%

Thermoelectric energy harvesting for internet of things devices using machine learning: A review

Kucova,

Prauzek,

Konecny

et al. 2023

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

show abstract

“…Machine learning algorithms, especially classification algorithms are utilized for similar to human activity recognition. Some of the significant problems addressed using the machine learning algorithms are: detecting malicious links from world wide web (www) [21], handwritten digit recognition [22], depression detection from image and video analysis [23], air temperature prediction [24], etc. Therefore, we have applied seven different classification algorithms namely, logistic regression, random forest, K-nearest neighbor (k-NN), Support vector machine (SVM), Gradient Boosting, Convolutional Neural Network (CNN), Bi-directional LSTM.…”

Section: Related Workmentioning

confidence: 99%

Leveraging Sensor Fusion and Sensor-Body Position for Activity Recognition for Wearable Mobile Technologies

Алам

Das

Tasjid

et al. 2021

Int. J. Interact. Mob. Technol.

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<p class="0abstract">Smart devices like smartphones and smartwatches have made this world smarter. These wearable devices are created through complex research methodologies to make them more usable and interactive with its user. Various interactive mobile applications such as augmented reality (AR), virtual reality (VR) or mixed reality (MR) applications solely depend on the in-built sensors of the smart devices. A lot of facilities can be taken from these devices with sensors such as accelerometer and gyroscope. Different physical activities such as walking, jogging, sitting, etc., can be important for analysis like health state prediction and duration of exercise by using those sensors based on artificial intelligence. In this paper, we have implemented machine learning and deep learning algorithms to detect and recognize eight activities namely, walking, jogging, standing, walking upstairs, walking downstairs, sitting, sitting-in-a-car and cycling; with a maximum of 99.3% accuracy. A few activities are almost similar in action, such as sitting and sitting-in-a-car, but difficult to distinguish; which makes it more challenging to predict tasks. In this paper, we have hypothesized that with more sensors (sensor fusion) and data collection points (sensor-body positions) a wide range of activities can be recognized and the recognition accuracies can be increased. Finally, we showed that the combination of all the sensors data of both pocket/waist and wrist can be used to recognize a wide range of activities accurately. The possibility of using the proposed methodologies for futuristic mobile technologies is quite significant. The adaptation of most recent deep learning algorithms such as convolutional neural network (CNN) and bi-directional Long Short Time Memory (Bi-LSTM) demonstrated high credibility of the methods presented as experimentation.<strong></strong></p>

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“…Experimental results demonstrate that the K-closest neighbor method performs better than other models. Adnan et al [5] examined three prediction models including least square support vector machine (LSSVM), classification and regression trees (CART) and group method and data handling neural network (GMDHNN) to forecast air temperature based on monthly temperature data from Pakistan. The finding was indicated that the LSSVM model is more accurate in temperature forecasting than the other two models.…”

Section: Introductionmentioning

confidence: 99%

An intelligent demand forecasting model using a hybrid of metaheuristic optimization and deep learning algorithm for predicting concrete block production

Al-Khazraji

Nasser

Khlil

2022

IJ-AI

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Demand forecasting aims to optimize the production planning of industrial companies by ensuring that the production planning meets the future demand. Demand forecasting utilizes historical data as an input to predict future trends of the demand. In this paper, a new approach for developing an intelligent demand forecasting model using a hybrid of metaheuristic optimization and deep learning algorithm is presented. Fireﬂy algorithmbased gated recurrent units (FA-GRU) is used to tackle the production forecasting problem. The proposed model has been evaluated and compared with the standard gated recurrent unit (GRU) and standard long short-term memory model (LSTM) using historical data of 36 months of concrete block manufacturing at dler company in Iraq. The prediction accuracy of the three models is evaluated using the root mean square error (RMSE), the mean absolute percentage error (MAPE) and the statistical coefficient of determination (R2 ) indicators. The outcomes of the study show that the proposed FA-GRU gives better forecasting results compared to the standard GRU and standard LSTM.

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Air temperature prediction using different machine learning models

Cited by 9 publications

References 31 publications

Thermoelectric energy harvesting for internet of things devices using machine learning: A review

Thermoelectric energy harvesting for internet of things devices using machine learning: A review

Leveraging Sensor Fusion and Sensor-Body Position for Activity Recognition for Wearable Mobile Technologies

An intelligent demand forecasting model using a hybrid of metaheuristic optimization and deep learning algorithm for predicting concrete block production

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