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Review

Air Temperature Forecasting Using Machine Learning Techniques: A Review

1
Santander Big Data Institute, Universidad Carlos III de Madrid, 28903 Getafe, Spain
2
Institute for Research in Technology (IIT), ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, Spain
*
Author to whom correspondence should be addressed.
Energies 2020, 13(16), 4215; https://doi.org/10.3390/en13164215
Received: 30 June 2020 / Revised: 5 August 2020 / Accepted: 10 August 2020 / Published: 14 August 2020
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. View Full-Text
Keywords: air temperature forecasting; artificial intelligence; machine learning; neural networks; support vector machines air temperature forecasting; artificial intelligence; machine learning; neural networks; support vector machines
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MDPI and ACS Style

Cifuentes, J.; Marulanda, G.; Bello, A.; Reneses, J. Air Temperature Forecasting Using Machine Learning Techniques: A Review. Energies 2020, 13, 4215. https://doi.org/10.3390/en13164215

AMA Style

Cifuentes J, Marulanda G, Bello A, Reneses J. Air Temperature Forecasting Using Machine Learning Techniques: A Review. Energies. 2020; 13(16):4215. https://doi.org/10.3390/en13164215

Chicago/Turabian Style

Cifuentes, Jenny, Geovanny Marulanda, Antonio Bello, and Javier Reneses. 2020. "Air Temperature Forecasting Using Machine Learning Techniques: A Review" Energies 13, no. 16: 4215. https://doi.org/10.3390/en13164215

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