Air temperature forecasting using artificial neural networks with delayed exogenous input

Jallal, Mohammed Ali; Chabaa, Samira; Yassini, Abdessalam El; Zeroual, Abdelouhab; Ibnyaich, Saida

doi:10.1109/wits.2019.8723699

Cited by 18 publications

(7 citation statements)

References 20 publications

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“…The MAE calculation for the year-round forecasting system varied from 0.516 • C for 1-h horizon to 1.873 • C for 12-h horizon. Recently, Jallal et al [27] proposed an autoregressive MLPNN-based model with delayed exogenous input sequence to analyze the global solar radiation to predict the air temperature in a half hour scale. The analyzed dataset contains the measurements at the weather station Agdal that is installed in the Agdal garden, Marrakesh, Morocco for the year 2014, and the model reports an MSE value of 0.272.…”

Section: Hourly Temperature Forecastingmentioning

confidence: 99%

“…Specifically, in the air temperature time series analysis, Artificial Neural Networks (ANN) and Support Vector Machines (SVM) are the most widely implemented strategies. In particular, most of the ANN models, developed to predict temperature values, are represented by the MultiLayer Perceptron Neural Networks (MLPNN) and Radial Basis Function Neural Networks (RBFNN) [20][21][22][23][24][25][26][27][28][29][30][31][32], with the Levenberg-Marquardt and Gradient Descent being the most used optimization algorithms. With regard to SVM models, most of the works developed in the field involve Radial Function Base Kernels [33][34][35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

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Air Temperature Forecasting Using Machine Learning Techniques: A Review

et al. 2020

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Efforts to understand the influence of historical climate change, at global and regional levels, have been increasing over the past decade. In particular, the estimates of air temperatures have been considered as a key factor in climate impact studies on agricultural, ecological, environmental, and industrial sectors. Accurate temperature prediction helps to safeguard life and property, playing an important role in planning activities for the government, industry, and the public. The primary aim of this study is to review the different machine learning strategies for temperature forecasting, available in the literature, presenting their advantages and disadvantages and identifying research gaps. This survey shows that Machine Learning techniques can help to accurately predict temperatures based on a set of input features, which can include the previous values of temperature, relative humidity, solar radiation, rain and wind speed measurements, among others. The review reveals that Deep Learning strategies report smaller errors (Mean Square Error = 0.0017 °K) compared with traditional Artificial Neural Networks architectures, for 1 step-ahead at regional scale. At the global scale, Support Vector Machines are preferred based on their good compromise between simplicity and accuracy. In addition, the accuracy of the methods described in this work is found to be dependent on inputs combination, architecture, and learning algorithms. Finally, further research areas in temperature forecasting are outlined.

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Section: Hourly Temperature Forecastingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Air Temperature Forecasting Using Machine Learning Techniques: A Review

et al. 2020

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“…All of the input signals are affected with synaptic weights and then transmitted to the hidden layer that is characterized by a specific activation function. Then, the signals of the output layer's neurons are computed based on the weighted hidden layer's outputs [18].…”

Section: Theory Behind Elman Neural Networkmentioning

confidence: 99%

A Deep Learning Algorithm for Solar Radiation Time Series Forecasting: A Case Study of El Kelaa Des Sraghna City

Jallal¹,

Yassini²,

Chabaa³

et al. 2020

RIA

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Nowadays, the studies that address solar radiation (SR) forecasting tend to focus on the implementation of conventional techniques. This provides good results, but researchers should focus on the creation of new methodologies that help us in going further and boost the prediction accuracy of SR data. The prime aim of this research study is to propose an efficient deep learning (DL) algorithm that can handle nonlinearities and dynamic behaviors of the meteorological data, and generate accurate real-time forecasting of hourly global solar radiation (GSR) data of the city of El Kelaa des Sraghna (32°2’53”N 7°24’30”W), Morocco. The proposed DL algorithm integrates the dynamic model named Elman neural network with a new input configuration-based autoregressive process in order to learn from the seasonal patterns of the historical SR measurements, and the actual measurements of air temperature. The attained performance proves the reliability and the accuracy of the proposed model to forecast the hourly GSR time series in case of missing values detection or pyranometer damage. Hence, electrical power engineers can adopt this forecasting tool to improve the integration of solar power resources into the power grid system.

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“…A series of forecasting methods including conventional methods and machine learning methods were proposed to predict temperature. Wang et al 8 proposed an improved support vector machine (SVM) to predict the daily minimum temperature; Babu et al 9 used different autoregressive integral moving average (ARIMA) models to predict the average global temperature; Jallal et al 10 proposed an artificial neural network (ANN) with delayed exogenous input to forecast air temperature on a half-hour scale. With the appearance of recurrent neural network (RNN), more and more methods based on RNN are used to solve the problem of temperature prediction.…”

Section: Introductionmentioning

confidence: 99%

Urban land surface temperature prediction using parallel STL-Bi-LSTM neural network

Huo

Cui

et al. 2022

J. Appl. Rem. Sens.

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Accurate temperature prediction is of great significance to human life and social economy. A series of traditional methods and machine learning methods have been proposed to achieve temperature prediction, but it is still a challenging problem. We propose a temperature prediction model that combines seasonal and trend decomposition using loess (STL) and the bidirectional long short-term memory (Bi-LSTM) network to achieve high-accuracy prediction of the daily average temperature of China cities. The proposed model decomposes the temperature data using STL into trend component, seasonal component, and remainder component. Decomposition components and the original temperature data are input into the two-layer Bi-LSTM to learn the features of the temperature data, and the sum of prediction of three components and the original temperature data prediction result are added using learnable weights as the prediction result. The experimental results show that the average root mean square error and mean absolute error of the proposed model on the testing data are 0.11 and 0.09, respectively, which are lower than 0.35 and 0.27 of STL-LSTM, 2.73 and 2.07 of EMD-LSTM, 0.39 and 0.15 of STL-SVM, achieving a higher precision temperature prediction.

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Air temperature forecasting using artificial neural networks with delayed exogenous input

Cited by 18 publications

References 20 publications

Air Temperature Forecasting Using Machine Learning Techniques: A Review

Air Temperature Forecasting Using Machine Learning Techniques: A Review

A Deep Learning Algorithm for Solar Radiation Time Series Forecasting: A Case Study of El Kelaa Des Sraghna City

Urban land surface temperature prediction using parallel STL-Bi-LSTM neural network

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